Databases / Data management Books

Book SynopsisThis book is about the process of using analytics and the capabilities of analytics in today's organizations. Cutting through the buzz surrounding the term analytics and the overloaded expectations about using analytics, the book demystifies analytics with an in-depth examination of concepts grounded in operations research and management science. Analytics as a set of tools and processes is only as effective as: The data with which it is working The human judgment applying the processes and understanding the output of these processes. For this reason, the book focuses on the analytics process. What is intrinsic to analytics' real organizational impact are the careful application of tools and the thoughtful application of their outcomes. This work emphasizes analytics as part of a process that supports decision-making within organizations. It wants to debunk overblown expectations that somehow analytics outputs or analytics as applied toTable of ContentsSECTION I. ANALYTICS PROCESS CONCEPTS. About the Analytics Process. Illustrating the Analytics Process through Risk Assessment. and Modeling. Analytics, Strategy, and Management Control Systems. SECTION II. ANALYTICS PROCESS APPLICATIONS. Data, Information, and Intelligence. The Rise of Big Data and Analytics in Higher Education. Google Analytics as a Prosumption Tool for Web Analytics. Knowledge-Based Cause–Effect Analysis Enriched by Generating Multilayered DSS Models. Online Community Projects in Lithuania: Cyber Security Perspective. Exploring Analytics in Health Information Delivery to Acute Health Care in Australia. Information Visualization and Knowledge Reconstruction of RFID Technology Translation in Australian Hospitals. Health Care Analytics and Big Data Management in Influenza Vaccination Programs: Use of Information–Entropy Approach. Sharing Knowledge or Just Sharing Data? Index.

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Book SynopsisThis is an edited book based on the selected submissions made to the conference titled International Conference in Smart Cities. The project provides an innovative and new approach to holistic management of cities physical, socio-economic, environmental, transportation and political assets across all domains, typically supported by ICT and open data.Table of ContentsAdoption and Acceptance of Mandatory Electronic Public Services by Citizens in the Developing World. Self-Sustainable Integrated Township. Smart People for Smart Cities. How Smart Cities influence Governance? Role of Manufacturing Sector to Develop Smart Economy. Concept of Smart Village in India. Smart City. Smart City Technologies. A Cloud-Based Mobile Application for Cashless Payments. Financial Viability of Energy Conservation using Natural Light. Information Risk for Digital Services. Mobile Commerce Research for Individual, Business and Society. The Shift Toward a Sustainable Urban Mobility through Decision Support Systems.

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Book SynopsisDESCRIPTIONEvent Streams in Action is a foundational book introducing the ULPparadigm and presenting techniques to use it effectively in data-richenvironments. The book begins with an architectural overview,illustrating how ULP addresses the thorny issues associated withprocessing data from multiple sources. It then guides the readerthrough examples using the unified log technologies Apache Kafkaand Amazon Kinesis and a variety of stream processing frameworksand analytics databases. Readers learn to aggregate events frommultiple sources, store them in a unified log, and build data processingapplications on the resulting event streams. As readers progressthrough the book, they learn how to validate, filter, enrich, and storeevent streams, master key stream processing approaches, and exploreimportant patterns like the lambda architecture, stream aggregation,and event re-processing. The book also dives into the methods andtools usable for event modelling and event analytics, along withscaling, resiliency, and advanced stream patterns. KEY FEATURES • Building data-driven applications that are easier to design,deploy, and maintain• Uses real-world examples and techniques• Full of figures and diagrams• Hands-on code samples and walkthroughs This book assumes that the reader has written some Java code. SomeScala or Python experience is helpful but not required. ABOUT THE TECHNOLOGYUnified Log Processing is a coherent data processing architecture thatcombines batch and near-real time stream data, event logging andaggregation, and data processing into a unified event stream. By efficientlycreating a single log of events from multiple data sources, Unified LogProcessing makes it possible to design large-scale data-driven applicationsthat are easier to design, deploy, and maintain. AUTHOR BIOAlexander Dean is co-founder and technical lead of Snowplow Analytics,an open source event processing and analytics platform.

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Book Synopsis Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of automating tedious business processes and back-office tasks Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Machine Learning for Business teaches you how to make your company more automated, productive, and competitive by mastering practical, implementable machine learning techniques and tools. Thanks to the authors’ down-to-earth style, you’ll easily grok why process automation is so important and why machine learning is key to its success. In this hands-on guide, you’ll work through seven end-to-end automation scenarios covering business processes in accounts payable, billing, payroll, customer support, and other common tasks. Using Amazon SageMaker (no installation required!), you’ll build and deploy machine learning applications as you practice takeaway skills you’ll use over and over. By the time you’re finished, you’ll confidently identify machine learning opportunities in your company and implement automated applications that can sharpen your competitive edge! Key Features Identifying processes suited to machine learning Using machine learning to automate back office processes Seven everyday business process projects Using open source and cloud-based tools Case studies for machine learning decision making For technically-inclined business professionals or business developers. No previous experience with automation tools or programming is necessary. Doug Hudgeon runs a business automation consultancy, putting his considerable experience helping companies set up automation and machine learning teams to good use. In 2000, Doug launched one of Australia’s first electronic invoicing automation companies. Richard Nichol has over 20 years of experience as a data scientist and software engineer. He currently specializes in maximizing the value of data through AI and machine learning techniques.

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Book SynopsisWithin a healthcare enterprise, patient vital signs and other automated measurements are communicated from connected medical devices to end-point systems, such as electronic health records, data warehouses and standalone clinical information systems. Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients. The book begins with a comprehensive overview of the types of medical devices in use today and the ways in which those devices interact, before examining factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations. Offering lessons learned from his own experiences managing MDI rollouts in both operating room and intensive care unit settings, the author provides practical guidance for healthcare stakeholders charged with leading an MDI rollout. Topics include working with MDI solution providers, assembling an implementation team and transitioning to go-live. Special features in the book include a glossary of acronyms used throughout the book and sample medical device planning and testing tools.Table of ContentsIntroduction: Overview of Medical Device Interoperability (MDI) and the Current State of the MDI Industry, What is MDI and Why is it Needed Chapter 1: Medical Device Types and Classes Used and How They Communicate Chapter 2: MDI Solution Acquisition and Implementation Chapter 3: Semantic Data Alignment and Time Synchronization of Medical Devices Chapter 4: Standards Surrounding Medical Device Integration To Health Systems Chapter 5: Notifications, Alerts and Clinical Uses of Medical Device Data Chapter 6: Patient Identification and Medical Device Association Chapter 7: Regulatory and Security Considerations of MDI

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Book SynopsisThis book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.

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Book SynopsisAdvanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level.This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules.Table of ContentsIntroduction.- Part 1: Advanced language features.- Python type hints.- Class slots.- Weak references.- Data classes.- Structural pattern matching.- Working with pprint.- Shallow v deep copy.- The __init__versus __new__ and __call__.- Python metaclasses and meta programming.- Part 2: Computer graphics and GUIs.- Introduction to computer graphics.- Python turtle graphics.- Computer generated art.- Introduction to Matplotlib.- Graphing with Matplotlib pyplot.- Graphical user interfaces.- Tkinter GUI library.- Events in Tkinter user interfaces.- PyDraw Tkinter example application.- Part 3: Computer graphics and GUIs.- Introduction to games programming.- Building games with pygame.- StarshipMeteors pygame.- Part 4: Testing.- Introduction to testing.- PyTest testing framework.- Mocking for testing.- Part 5: File Input / Output.- Introduction to files, paths and IO.- Reading and writing files.- Stream IO.- Working with CSV files.- Working with excel files.- Regular expressions in Python.- Part 6: Database access.- Introduction to databases.- Python DB-API.- PyMySQL module.- Part 7: Logging.- Introduction to logging.- Logging in Python.- Advanced logging.- Part 8: Concurrency and parallelism.- Introduction to concurrency and parallelism.- Threading.- MultiProcessing.- Inter thread / Process synchronisation.- Futures.- Concurrency with AsyncIO.- Performance monitoring and profiling.- Part 9: Reactive programming.- Reactive programming introduction.- RxPy observables, observers and subjects.- RxPy operators.- Part 10: Network programming.- Introduction to sockets and web services.- Sockets in Python.- Web services in Python.- Flask web services.- Flask bookshop web service.- Part 11: Data analytics and machine learning.- Introduction to data science.- Pandas and data analytics.- Alternatives to pandas.- Machine learning in Python.- Pip and Conda virtual environments.

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Book SynopsisDeep Convolutional Neural Networks (CNNs).- Deep CNNs for Image Classification, Object Detection, and Segmentation.- Attention and Transformer Networks.- Transformer-based Approaches for Medical Image Analysis.- Deep Learning Networks for 3D Medical Image Analysis.- Multimodal Deep Learning for Medical Image Analysis.- Semi-supervised Learning for Medical Image Analysis.- Domain Adaptation and Generalization for Medical Image Analysis.- Deep Learning Models for Medical Image Translation.- Foundation Models for Medical Image Analysis.

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Book Synopsis.- Industrial Keynote..- From Data Silos to Data Mesh: A Case Study in Financial Data Architecture..- Invited Talk..- Blending Contextual Data with Heterogeneous Time Dimensions for Improved Time Series Analysis..- A Hybrid Data Model to Support Transportation Analytics of Emergency Service Vehicles..- Large Language Models..- Automated Archival Descriptions with Federated Intelligence of LLMs..- Entropy-Guided Probing for Predicting LLM Hallucinations with Knowledge Graph Features..- Towards Automating RDF Extraction for Archaeological Knowledge Graphs with LLMs..- Ontology-Based Forest Fire Management using Complex Event Processing and Large Language Models..- Table Annotation Utilizing Large Language Model and Knowledge Graph..- Improving Software Security Through a LLM-Based Vulnerability Detection Model..- SysResolve: Study on In-Context LLM Generation of Resolution Scripts..- Data Quality..- A Novel Unsupervised Anomaly Detection Method Based on TCN-LSTM-CMA Autoencoder..- Behaviour modelling and Wayfinding Error Detection in Low Mountain Hiking..- Explainable Time Series Anomaly Detection by Dynamic Mode Decomposition..- Exploring Quantum Bootstrap Sampling for AQP Error Assessment: A Pilot Study..- AI-Driven Semantic Data Quality Assessment and Scoring for Relational Databases..- Network Anomaly Detection Using Gramian Angular Field Transformation and Vision Transformer..- Machine Learning /Artificial Intelligence Applications..- Identifying Multimodal Sarcasm Based on Incongruous Knowledge Capturing and Contrastive Learning..- Ensemble ToT and Its Application to Automatic Grading..- Improving Prompt-based Learning Framework for Mental Health Aspect Detection from Social Media..- DInos: A Deep Reinforcement Learning Approach to Generalizable Autoscaling in Stateless Cloud Applications..- Influential Slot and Tag Selection in Billboard Advertisement..- Speech-scenario Generation based on the Philosophy of Prominent Leader within the Small Community..- VarCGAN: Variational Cyclic Generative Adversarial Network for Music Genre Style Transfer..- Innovative Framework for Early Estimation of Mental Disorder Scores to Enable Timely Interventions..- A Hybrid Approach to estimating AI Carbon Emissions..- Optimal Information Retrieval System in E-Learning Using Optimization-Driven Bidirectional Long Short-Term Memory..- A Data Product Classification by Technical and Machine Learning Aspects..- Classification Techniques..- Discovering Voting Power for Ensemble Methods..- Classifying Public and Private Documents Using Context-Based Predictions.

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Book Synopsis.- Image Processing, Analytics, and Vision Systems..- Relationship Analysis of Image-Text Pair in SNS Posts..- Enhancing Segmentation of Irregular Microstructural Elements Using Extended Channel Information and Transfer Learning..- Deep-RVT: A Residual Vision Transformers for Human Action Recognition..- Recommender Techniques..- Food Recommendation with Balancing Comfort and Curiosity..- ONFOOD: A Substitute Recommendation System in Food Recipes..- Inspire Me with Your Questions: Repurposing Historical Questions for New Documents..- Data Integration..- MRF-JOIN: Differentially Private Vertical Data Synthesis via Federated Marginal Join on Shared Attributes..- Efficient Source Selection for Federated SPARQL Queries using Adjacent Predicate Information..- Empathetic Response Generation in Emotional Support Conversation via Multi- Stage Cascading Information Fusion..- Unified Schema-Driven Graph Polystore: Achieving Transparency in Multi-Model Integration and Migration..- Optimisation Methods..- Group Trip Planning Query Problem with Multimodal Journey..- A Model-based Approach for Simple Construction and Efficient Evaluation of Dataframes..- Energy and Performance Evaluation of Serverless and Serverful Models on Spark for Database Join Operations..- Graph Applications..- The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment..- Graph Patterns in Fine-grained Access Control for Graph-structured Data..- An Efficient Point-of-Interest Placement Method Based on Betweenness Centrality..- Analytics..- Analytics Modelling over Multiple Datasets using Vector Embeddings..- Towards IoT-based Smart Mobility Framework for Proactive Road Stress Detection in Individuals with ASD..- A Divisive Unsupervised Feature Selection Approach for Explainable Remaining Useful Life Prediction..- Data Storytelling to Unlock the Communicative Power of Digital Twins..- Queueing Theory for Verifying the Utilization Rate of an Image Processing System..- Effect of frequency features of ELA maps on the detection performance of image manipulation based on DCT and FFT basis features..- Alpha: A Multi-Attention Enhanced YOLO Framework for Robust Photovoltaic Defect Detection..- Security/Privacy..- Secure Approach for Blockchain-based Anonymous Attribute-based Searchable Encryption Scheme for Data Sharing..- Incremental k-anonymization for Continuously Growing Big Databases..- Post Quantum Cryptographic Schemes and Libraries Selection..- Benchmarks and Surveys..- Workload-Based Clustering of Large Number of Database-as-a-Service Instances..- Accelerating Python Code with Parallel I/O..- Benchmarking Embedding Techniques for Modeling User Navigation Behavior on Task-Oriented Software..- The Wrecking SQL Incremental Validation Methodology..- A Survey of Control Technologies for Autonomous Underwater Vehicles.

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Book SynopsisAlgorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems. This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java. The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.Trade Review"This is another mainstream textbook on algorithms and data structures, mainly intended for undergraduate students and professionals … . The two-layer index table is also detailed and helpful. I do enjoy reading the informative sections of historical notes and further findings at the end of each chapter. … This book is very well written, with the help of … clear figures and tables, as well as many interesting and inspiring examples." Zhizhang Shen, Zentralblatt MATH, Vol. 1146, 2008"... the book develops the basic fundamental principles underlying their design and analysis without sacrificing depth or rigor. The authors' insight, knowledge and active research on algorithms and data structures provide a very solid approach to the book. I particularly liked their "as informally as possible and as formally as necessary" writing style, and I enjoyed a lot their decision to not only discuss classical results, but to broaden the view to alternative implementations, memory hierarchies and libraries, which transmits novelty and increases interest...I think that this book will be a superb addition particularly useful for teachers of undergraduate courses, to graduate students in Computer Science, and to researchers that work, or intend to work, with algorithms." Jordi Petit, Computer Science Review 3, 2009 "Mehlhorn and Sanders write well, and the well-organized presentation reflects their experience and interest in the various topics... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. [...]This text is intended for undergraduate computer science (CS) majors, and focuses on algorithm analysis. … it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. Finally, the book contains interesting tidbits that are not readily available elsewhere." M. G. Murphy, ACM Computing Reviews, October 2008"A 'Toolbox' should be portable, practical, and useful. This book is all these, covering a nice swath of the classic CS algorithms but addressing them in a way that is accessible to the student and practitioner. Furthermore, it manages to incorporate interesting examples as well as subtle examples of wit compressed into its 300 pages. Although it is not tied to any one language or library, it provides practical references to efficient open-source implementations of many of the algorithms and data structures; these should be the first refuge of the commercial developer. I can easily recommend this book as an intermediate undergraduate text, a refresher for those of us who only dimly remember our intermediate undergraduate courses, and as a reference for the professional development craftsman." Hal C. Elrod, SIGACT News Book Review Column 42(4) 2011Table of ContentsAppetizer: Integer Arithmetics.- Representing Sequences by Arrays and Linked Lists.- Hash Tables and Associative Arrays.- Sorting and Selection.- Priority Queues.- Sorted Sequences.- Graph Representation.- Graph Traversal.- Shortest Paths.- Minimum Spanning Trees.- Generic Approaches to Optimization.

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Book SynopsisThe design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen­ dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith­ mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa­ tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven­ tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.Table of ContentsI. Foundations.- 1. Machine Models: RAM and RASP.- 2. Randomized Computations.- 3. A High Level Programming Language.- 4. Structured Data Types.- 4.1 Queues and Stacks.- 4.2 Lists.- 4.3 Trees.- 5. Recursion.- 6. Order of Growth.- 7. Secondary Storage.- 8. Exercises.- 9. Bibliographic Notes.- II. Sorting.- 1. General Sorting Methods.- 1.1 Sorting by Selection, a First Attempt.- 1.2 Sorting by Selection: Heapsort.- 1.3 Sorting by Partitioning: Quicksort.- 1.4 Sorting by Merging.- 1.5 Comparing Different Algorithms.- 1.6 Lower Bounds.- 2. Sorting by Distribution.- 2.1 Sorting Words.- 2.2 Sorting Reals by Distribution.- 3. The Lower Bound on Sorting, Revisited.- 4. The Linear Median Algorithm.- 5. Exercises.- 6. Bibliographic Notes.- III. Sets.- 1. Digital Search Trees.- 1.1 Tries.- 1.2 Static Tries or Compressing Sparse Tables.- 2. Hashing.- 2.1 Hashing with Chaining.- 2.2 Hashing with Open Addressing.- 2.3 Perfect Hashing.- 2.4 Universal Hashing.- 2.5 Extendible Hashing.- 3. Searching Ordered Sets.- 3.1 Binary Search and Search Trees.- 3.2 Interpolation Search.- 4. Weighted Trees.- 4.1 Optimum Weighted Trees, Dynamic Programming, and Pattern Matching.- 4.2 Nearly Optimal Binary Search Trees.- 5. Balanced Trees.- 5.1 Weight-Balanced Trees.- 5.2 Height-Balanced Trees.- 5.3 AdvancedTopicson(a,b)-Trees.- 5.3.1 Mergable Priority Queues.- 5.3.2 Amortized Rebalancing Cost and Sorting Presorted Files.- 5.3.3 Finger Trees.- 5.3.4 Fringe Analysis.- 6. Dynamic Weighted Trees.- 6.1 Self-Organizing Data Structures and Their Amortized and Average Case Analysis.- 6.1.1 Self-Organizing Linear Lists.- 6.1.2 Splay Trees.- 6.2 D-trees.- 6.3 An Application to Multidimensional Searching.- 7. A Comparison of Search Structures.- 8. Subsets of a Small Universe.- 8.1 The Boolean Array (Bitvector).- 8.2 The O(log log N) Priority Queue.- 8.3 The Union-Find Problem.- 9. Exercises.- 10. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Table of ContentsVII. Multidimensional Data Structures.- 1. A Black Box Approach to Data Structures.- 1.1 Dynamization.- 1.2 Weighting and Weighted Dynamization.- 1.3 Order Decomposable Problems.- 2. Multi-dimensional Searching Problems.- 2.1 D-dimensional Trees and Polygon Trees.- 2.2 Range Trees and Multidimensional Divide and Conquer.- 2.3 Lower Bounds.- 2.3.1 Partial MatchRetrieval in Minimum Space.- 2.3.2 The Spanning Bound.- 3. Exercises.- 4. Bibliographic Notes.- VIII. Computational Geometry.- 1. Convex Polygons.- 2. Convex Hulls.- 3. Voronoi Diagrams and Searching Planar Subdivisions.- 3.1 Voronoi Diagrams.- 3.2 Searching Planar Subdivisions.- 3.2.1 Removal of Large Independent Sets.- 3.2.2 Path Decompositions.- 3.2.3 Searching Dynamic Planar Subdivisions.- 3.3 Applications.- 4. The Sweep Paradigm.- 4.1 Intersection of Line Segments and Other Intersection Problems in the Plane.- 4.2 Triangulation and its Applications.- 4.3 Space Sweep.- 5. The Realm of Orthogonal Objects.- 5.1 Plane Sweep for Iso-Oriented Objects.- 5.1.1 The Interval Tree and its Applications.- 5.1.2 The Priority Search Tree and its Applications.- 5.1.3 Segment Trees.- 5.1.4 Path Decomposition and Plane Sweep for Non-Iso-Oriented Objects.- 5.2 Divide and Conquer on Iso-Oriented Objects.- 5.2.1 The Line Segment Intersection Problem.- 5.2.2 The Measure and Contour Problems.- 5.3 Intersection Problems in Higher-Dimensional Space.- 6. Geometric Transforms.- 6.1 Duality.- 6.2 Inversion.- 7. Exercises.- 8. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Book SynopsisThis book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2020, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization. Table of ContentsSegregating and Recognizing Human Actions from Video Footages using LRCN Technique.- Fall Alert: A Novel Approach to Detect Fall.- Evaluation of Automatic Text Visualization Systems: A Case Study.- Face Recognition Based Attendance System using Real Time Computer Vision Algorithms.- The Impact of Knowledge Management Adoption on the Government Sector’s Performance: The Case of Bahrain.- Video Surveillance for the Crime Detection using Features.- Real-time Neural-net Driven Optimized Inverse-kinematics for a Robotic Manipulator.- A Deep Learning Technique to Countermeasure Video Based Presentation Attacks.- Optimization of Loss Functions for Predictive Soil Mapping.- Natural Language Information Extraction through Non Factoid Question and Answering System.- An Enhanced Differential Evolution Algorithm with New Environmental-based Parameters for Solving Optimization Problems.- Reactive Power Optimization Approach based on Chaotic Particle Swarm Optimization.- Data Mining Model for Better Admissions in Higher Educational Institutions (HEIs) – A Case Study of Bahrain.- The Effectiveness of Renewable Energies Projects in Kuwait - PAAET Solar Energy Project.- Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using Mobile Net.- Real-Time Object Detection in Remote Sensing Images using Deep Learning.- Malaria Detection using Convolutional Neural Network.- Drone-Based Face Recognition using Deep Learning.- Traffic Sign Recognition for Self-Driving Cars with Deep Learning.- Identifying the Association Rule to Determine the Possibilities of Cardio Vascular Diseases(CVD).- Prediction of Service Level Agreement Violation in Cloud Computing using Bayesian Regularization.- A New Methodology for Language Identification in Social Media Code-mixed Text.- Detecting Influencers in Social Networks Through Machine Learning Techniques.- Application and Analysis of K-Means Algorithms on a Decision Support Framework for Municipal Solid Waste Management.- Android Rogue Application Detection using Image Resemblance and Reduced LDA.- An Indexed Non-Probability Skyline Query Processing Framework for Uncertain Data.- Analysis of Operational Control Mode of Intelligent Distribution Grids with Multi-microgrid.- Technical Present Situation based on Micro Grid Operation Control.- Skin Lesion Classification: A Transfer Learning Approach Using Efficient Nets.- Change Footprint Pattern Analysis of Crime Hotspot of Indian Districts.- Itemset Mining based Episode Profiling of Terrorist Attacks using Weighted Ontology.- Enabling Technologies in Banking Industry, Regulatory Technology RegTech and Money Laundering Prevention.- A Cognitive Knowledge Base for Learning Disabilities using Concept Analysis.- Native Monkey Detection using Deep Convolution Neural Network.- Evaluation and Summarization of Student Feedbacks Using Sentiment Analysis.- Predicting Competitive Weight Lifting Performance using Regression and Tree-based Algorithms.- Predicting the Primary Dominant Personality Trait of Perceived Leaders by Mapping Linguistic Cues from Social Media Data onto the Big-Five Model.- Analysis of Users behaviour on Micro Blogging Site using a Topic.- Machine Learning Techniques for Short Term Forecasting of Wind Power Generation.- Integrated Process Management System of Smart Substation Secondary Side based on Practical Scheme.- Research on Forms and Strategies of Alternative Energy Achieved.- Lightweight Access Control Algorithm for Internet of Things.- Named Entity Recognition for Legal Documents.- Visual Speech Processing and Recognition.- Predictive Analytics for Cardiovascular Disease Diagnosis using Machine Learning Techniques.- A Novel Approach for Smart Health Care Recommender System.- Heart Disorder Prognosis employing KNN, ANN, ID3 and SVM.- IoT based Home Security System with Wireless Communication.- Implementation of Internet of Things IoT in Small Business Industry: Case of Bahrain.- A Comparative Study of Model-Free Reinforcement Learning Approaches.- Location Aware Security System for Smart Cities using IoT.- An Assessment Study of Gait Biometric Recognition using Machine Learning.- A Study on Risk Management Practices in Online Banking in Bahrain.- Deep Learning Techniques: An Overview.- A Multilayer Deep Learning Framework For Auto-Content Tagging.- Case Based Reasoning (CBR) based Anemia Severity Detection System (ASDS) Using Machine Learning Algorithm.- ECG Signal Analysis, Diagnosis and Transmission.- The Effect of Real-Time Feedback on Consumer’s Behaviour in the Energy Management Sector: Empirical study.- Synchronization Control in Fractional Discrete-Time Systems with Chaotic Hidden Attractors.- Employment of Cryptographic Modus Operandi based on Trigonometric Algorithm and Resistor Color Code.- Experimental & Dimensional Analysis Approach for Human Energy Required In Wood Chipping Process.- Impact of High-k gate dielectric and Workfunctions Variation on Electrical Characteristics of VeSFET.- Correlating Personality Traits to Different Aspects of Facebook Usage.- Fractional Order control of a Fuel Cell-boost Converter System.- Battery Pack Construction Scheme based on UPS System Reliability.- Study on Land Compensation for High Voltage Transmission Lines in Power Grid based on Easement.- Study on the Terrain of Power Network Construction Expropriation and Legal Risk Prevention.

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Book SynopsisThis book includes selected papers presented at World Conference on Information Systems for Business Management (ISBM 2022), held in Bangkok, Thailand, during September 2–3, 2022. It covers up-to-date cutting-edge research on data science, information systems, infrastructure and computational systems, engineering systems, business information systems, and smart secure systems.Table of Contents Social Media as Communication-Transformation Tools.- Bi directional DC-DC converter-based Energy Storage Method for Electric Vehicles.- Design of Smart Irrigation System in Sone Command Area Bihar for Paddy Crop.- A Footstep to Image Deconvolution Technique for the both Known and Unknown Blur Parameter.- Secured Monitoring of Unauthorized UAV by Surveillance Drone Using NS2.

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Book SynopsisThis book includes selected papers presented at the World Conference on Information Systems for Business Management (ISBM 2023), held in Bangkok, Thailand, on September 78, 2023.

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Book SynopsisChapter 1: Introduction.- Chapter 2: Framework Overview.- Chapter 3: Deep Dive.- Chapter 4: Examples - Files.- Chapter 5: Examples -  Databases.- Chapter 6: Examples - Python.- Chapter 7: Examples - APIs.- Chapter 8: Platforms.- Chapter 9: Pipelines.- Chapter 10: Analog.- Appendix A.

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Book SynopsisTrade Review"You might think that museums are for collecting and laboratories for experimenting. Bruno J. Strasser tracks the creation of a hybrid culture--a 'way of knowing' that was comparative and experimental at the same time. Molecular biologists used the protein sequences of very various species to crack the genetic code. From bacteria to blood and protein to DNA, this engaging book restores collecting to the experimentalist tradition and gives 'big data' biology the history it needs."--Nick Hopwood, author of Haeckel's Embryos: Images, Evolution, and Fraud "Amidst all the hype surrounding Big Data and the life sciences, Bruno J. Strasser uncovers the deep continuities of collecting and comparing that link the latest data banks to venerable natural history museums. This bold book rethinks the relationship between field, laboratory, and archive, with important implications for the ethos of open publication in science."--Lorraine Daston, Max Planck Institute for the History of Science "The long-contested line between experimental life sciences and those that collect, compare, and classify is once more unsettled. It is now accepted that comparative sciences are open to experiment and always have been. And Bruno J. Strasser now argues that the celebrated achievements of experimental biology have similarly depended on practices of collecting and curating. And not just in our own new world of digital databases, but historically: from when experimenters first thought to make collecting forever obsolete. Strasser supports his bold revision with case studies of a broad range of sciences, from taxonomy to serology, experimental and then molecular biology, and bio-informatics. In its historical depth and breadth this is a benchmark book; and for all who want to know how life sciences really work, it's a must read."--Robert E. Kohler, University of Pennsylvania "A masterful, groundbreaking work: Strasser explores collecting activities in multiple branches of biology and medicine across several centuries, covering the territory from natural historical specimens to blood and proteins, and on to DNA sequences and contemporary big-data biology. His book assesses issues of lasting salience, including control of the collections, access to specimens and data, modes of publication, and assignment of authorship and credit. Strasser contends that big-data biology is not a sharp departure from the past but a hybrid, a joining of the experimentalist-reductionist inquiries into model organisms with the practices of collectors who classified and characterized their specimens and compared them with others. Strasser's research is wide and deep, his prose lucidly informative, and his analysis subtle, discerning, and persuasive." --Daniel J. Kevles, Yale University "Collecting Experiments is an exciting and welcome addition to the historiography of the long-standing debates about the changing roles of experimentation and description in the life sciences. Rejecting the older notion of an impassable dichotomy, Bruno J. Strasser suggests that the rise of experimental approaches to biology in the nineteenth century did not eclipse the more descriptive work of natural history, but rather became a part of an overall 'way of knowing' that included both approaches. 'Big data, ' whether obtained by experimental or observational methods had to be analyzed in the same manner. Strasser has done a great service to clarify the historical relationship between these two methodologies. It is a must for all scholars in the history of biology."--Garland Allen, Washington University in Saint Louis

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Book SynopsisData warehousing is one of the hottest business topics, and there's more to understanding data warehousing technologies than you might think. Find out the basics of data warehousing and how it facilitates data mining and business intelligence with Data Warehousing For Dummies, 2nd Edition.Table of ContentsIntroduction 1 Part I: The Data Warehouse: Home for Your Data Assets 7 Chapter 1: What’s in a Data Warehouse? 9 Chapter 2: What Should You Expect from Your Data Warehouse? 25 Chapter 3: Have It Your Way: The Structure of a Data Warehouse 37 Chapter 4: Data Marts: Your Retail Data Outlet 59 Part II: Data Warehousing Technology 71 Chapter 5: Relational Databases and Data Warehousing 73 Chapter 6: Specialty Databases and Data Warehousing 85 Chapter 7: Stuck in the Middle with You: Data Warehousing Middleware 95 Part III: Business Intelligence and Data Warehousing 113 Chapter 8: An Intelligent Look at Business Intelligence 115 Chapter 9: Simple Database Querying and Reporting 125 Chapter 10: Business Analysis (OLAP) 135 Chapter 11: Data Mining: Hi-Ho, Hi-Ho, It’s Off to Mine We Go 149 Chapter 12: Dashboards and Scorecards 155 Part IV: Data Warehousing Projects: How to Do Them Right 163 Chapter 13: Data Warehousing and Other IT Projects: The Same but Different 165 Chapter 14: Building a Winning Data Warehousing Project Team 179 Chapter 15: You Need What? When? — Capturing Requirements 193 Chapter 16: Analyzing Data Sources 203 Chapter 17: Delivering the Goods 213 Chapter 18: User Testing, Feedback, and Acceptance 225 Part V: Data Warehousing: The Big Picture 231 Chapter 19: The Information Value Chain: Connecting Internal and External Data 233 Chapter 20: Data Warehousing Driving Quality and Integration 247 Chapter 21: The View from the Executive Boardroom 263 Chapter 22: Existing Sort-of Data Warehouses: Upgrade or Replace? 271 Chapter 23: Surviving in the Computer Industry (and Handling Vendors) 281 Chapter 24: Working with Data Warehousing Consultants 291 Part VI: Data Warehousing in the Not-Too-Distant Future 297 Chapter 25: Expanding Your Data Warehouse with Unstructured Data 299 Chapter 26: Agreeing to Disagree about Semantics 305 Chapter 27: Collaborative Business Intelligence 311 Part VII: The Part of Tens 317 Chapter 28: Ten Questions to Consider When You’re Selecting User Tools 319 Chapter 29: Ten Secrets to Managing Your Project Successfully 325 Chapter 30: Ten Sources of Up-to-Date Information about Data Warehousing 331 Chapter 31: Ten Mandatory Skills for a Data Warehousing Consultant 335 Chapter 32: Ten Signs of a Data Warehousing Project in Trouble 339 Chapter 33: Ten Signs of a Successful Data Warehousing Project 343 Chapter 34: Ten Subject Areas to Cover with Product Vendors 347 Index 351

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Book SynopsisLike many other organizing paradigms, smart data strategy isrevolutionary and essential to enterprise performance. SmartData explores smart data strategy to enhance enterpriseperformance. Smart Data provides valuable tools in business,like skills for better enterprise decision-making, enterpriseperformance, and agility towards change.Table of ContentsForeword. Preface. Acknowledgments. Introduction: A Comprehensive Overview. Predictive Management. IDEF Lexicon for Executives. Organization of This Book. Smart Data in Three Dimensions. Business Rule. Case Study: IT Capital Budgeting Using a Knapsack Problem. Case Study: Better Decision Making: Field Testing, Evaluation and Validation of a Web-Based MedWatch Decision Support System (MWDSS). Engineering an Ubiquitous Strategy for Catalyzing Enterprise Performance Optimization. What Smart Data Provides. References. 1 Context: The Case and Place for Smart Data Strategy. 1.1 Value of Data to the Enterprise. 1.2 Enterprise Performance Versus Enterprise Integration. 1.3 Current Problems and Deficiencies from Poor Data Strategy. 1.4 New Technologies. 1.5 Breaking from Tradition with Improved Results. References. 2 Elements: Smart Data and Smart Data Strategy. 2.1 Performance Outcomes and Attributes. 2.2 Policy and Business Rules. 2.3 Expectations: Managerial and Technical. 2.4 Capacity for Change and Improvement. 2.5 Iteration Versus Big Bang. References. 3 Barriers: Overcoming Hurdles and Reaching a New Performance Trajectory. 3.1 Barriers. 3.2 Overcoming Barriers. 3.3 Top–Down Strategy. 3.4 Balance of Consequences and Reinforcement. 3.5 Collaboration. 3.6 Enterprise Performance Optimization Process. 3.7 Enterprise Performance Optimization Architecture. 3.8 Scoping, Scheduling, Budgeting, and Project and Program Management. References. 4 Visionary Ideas: Technical Enablement. 4.1 Today’s Possibilities. 4.2 Calibrating Executive Expectations. 4.3 Five Years from Now. 4.4 Ten Years From Now. References. 5. CEO’s Smart Data Handbook. 5.1 Strategy. 5.2 Policy. 5.3 Organization. 5.4 Actions. 5.5 Timing. 5.6 Funding and Costing Variables. 5.7 Outcomes and Measurements. References. Index. Wiley Series in Systems Engineering and Management.

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Book SynopsisThis book demonstrates the role of coding in communication and data storage systems design, illustrating the correct use of codes and the selection of the right code parameters. Relevant decoding techniques and their implementation are discussed in detail, while emphasizing the fundamental concepts of coding theory with minimal mathematical tools.Table of ContentsThe Principles of Coding in Digital Communications. Convolutional Codes. Linear Block Codes. Cyclic Codes. Finite Field Arithmetic. BCH Codes. Reed Solomon Codes. Performance Calculations for Block Codes. Multistage Coding. Iterative Decoding. Index.

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Book SynopsisData warehousing is split into two camps: Ralph Kimball leads those who champion a technique called dimensional modeling; Bill Inmon leads the rest who believe in using relational modeling techniques.Table of ContentsAcknowledgments. About the Authors. PART ONE: CONCEPTS. Chapter 1. Introduction. Chapter 2. Fundamental Relational Concepts. PART TWO: MODEL DEVELOPMENT. Chapter 3. Understanding the Business Model. Chapter 4. Developing the Model. Chapter 5. Creating and Maintaining Keys. Chapter 6. Modeling the Calendar. Chapter 7. Modeling Hierarchies. Chapter 8. Modeling Transactions. Chapter 9. Data Warehouse Optimization. PART THREE: OPERATION AND MANAGEMENT. Chapter 10. Accommodating Business Change. Chapter 11. Maintaining the Models. Chapter 12. Deploying the Relational Solution. Chapter 13. Comparison of Data Warehouse Methodologies. Glossary. Recommended Reading. Index.

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Book SynopsisThe many and powerful data structures for representing information physically (in contrast to a database management system that represents information with logical structures) are introduced by this book.Table of ContentsPreface xi Part One Primary File Organizations 25 Part Two Bit Level And Related Structures 127 Part Three Tree Structures 197 Part Four File Sorting 337 Answers to Selected Exercises 375 Index 393

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist

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Book SynopsisThis comprehensive guide offers a detailed treatment of the analysis, design, simulation and testing of the full range of today''s leading delta-sigma data converters. Written by professionals experienced in all practical aspects of delta-sigma modulator design, Delta-Sigma Data Converters provides comprehensive coverage of low and high-order single-bit, bandpass, continuous-time, multi-stage modulators as well as advanced topics, including idle-channel tones, stability, decimation and interpolation filter design, and simulation.Table of ContentsPreface. Introduction. An Overview of Basic Concepts (J. Candy). Quantization Noise in DeltaSigma A/D Converters (R. Gray). Quantization Errors and Dithering in DeltaSigma Modulators (S. Norsworthy). Stability Theory for DeltaSigma Modulators (R. Adams & R. Schreier). The Design of High-Order Single-Bit DeltaSigma ADCs (R. Adams). The Design of Cascaded DeltaSigma ADCs (M. Rebeschini). High-Speed Cascaded DeltaSigma ADCs (B. Brandt). Delta-Sigma ADCs with Multibit Internal Converters (R. Carley, et al.). The Design of Bandpass DeltaSigma ADCs (S. Jantzi, et al.). Architectures for DeltaSigma DACs (G. Temes, et al.). Analog Circuit Design for DeltaSigma ADCs (B. Brandt, et al.). Analog Circuit Design for DeltaSigma DACs (M. Rebeschini & P. Ferguson). Decimation and Interpolation for DeltaSigma Conversion (S. Norsworthy & R. Crochiere). CAD for the Analysis and Design of DeltaSigma Converters (C. Wolff, et al.). Index. About the Editors.

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Book SynopsisTable of ContentsPreface. Introduction to Data Conversion and Processing. Basic Sampling Circuits. Sample-and-Hold Architectures. Basic Principles of Digital-to-Analog Conversion. Digital-to-Analog Converter Architectures. Analog-to-Digital Converter Architectures. Building Blocks Data Conversion Systems. Precision Techniques. Testing and Characterization. Index.

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Book SynopsisThis book is aimed at students in communications and signal processing who want to extend their skills in the energy area. It describes power systems and why these backgrounds are so useful to smart grid, wireless communications being very different to traditional wireline communications.Table of ContentsPreface xv Acknowledgments xix Some Notation xxi 1 Introduction 1 1.1 Big Data: Basic Concepts 1 1.2 Data Mining with Big Data 9 1.3 A Mathematical Introduction to Big Data 13 1.4 A Mathematical Theory of Big Data 28 1.5 Smart Grid 34 1.6 Big Data and Smart Grid 36 1.7 Reading Guide 37 Bibliographical Remarks 39 Part I Fundamentals of Big Data 41 2 The Mathematical Foundations of Big Data Systems 43 2.1 Big Data Analytics 44 2.2 Big Data: Sense, Collect, Store, and Analyze 45 2.3 Intelligent Algorithms 48 2.4 Signal Processing for Smart Grid 48 2.5 Monitoring and Optimization for Power Grids 48 2.6 Distributed Sensing and Measurement for Power Grids 49 2.7 Real-time Analysis of Streaming Data 50 2.8 Salient Features of Big Data 51 2.9 Big Data for Quantum Systems 54 2.10 Big Data for Financial Systems 55 2.11 Big Data for Atmospheric Systems 73 2.12 Big Data for Sensing Networks 74 2.13 Big Data forWireless Networks 75 2.14 Big Data for Transportation 78 Bibliographical Remarks 78 3 Large Random Matrices: An Introduction 79 3.1 Modeling of Large Dimensional Data as Random Matrices 79 3.2 A Brief of Random MatrixTheory 81 3.3 Change Point of Views: From Vectors to Measures 85 3.4 The Stieltjes Transform of Measures 86 3.5 A Fundamental Result: The Marchenko–Pastur Equation 88 3.6 Linear Eigenvalue Statistics and Limit Laws 89 3.7 Central LimitTheorem for Linear Eigenvalue Statistics 99 3.8 Central LimitTheorem for Random Matrix S−1T 101 3.9 Independence for Random Matrices 103 3.10 Matrix-Valued Gaussian Distribution 110 3.11 Matrix-ValuedWishart Distribution 112 3.12 Moment Method 112 3.13 Stieltjes Transform Method 113 3.14 Concentration of the Spectral Measure for Large Random Matrices 114 3.15 Future Directions 117 Bibliographical Remarks 117 4 Linear Spectral Statistics of the Sample Covariance Matrix 121 4.1 Linear Spectral Statistics 121 4.2 Generalized Marchenko–Pastur Distributions 122 4.3 Estimation of Spectral Density Functions 127 4.4 Limiting Spectral Distribution of Time Series 146 Bibliographical Remarks 154 5 Large Hermitian Random Matrices and Free Random Variables 155 5.1 Large Economic/Financial Systems 156 5.2 Matrix-Valued Probability 157 5.3 Wishart-Levy Free Stable Random Matrices 166 5.4 Basic Concepts for Free Random Variables 168 5.5 The Analytical Spectrum of theWishart–Levy Random Matrix 172 5.6 Basic Properties of the Stieltjes Transform 176 5.7 Basic Theorems for the Stieltjes Transform 179 5.8 Free Probability for Hermitian Random Matrices 185 5.9 Random Vandermonde Matrix 196 5.10 Non-Asymptotic Analysis of State Estimation 200 Bibliographical Remarks 201 6 Large Non-Hermitian Random Matrices and Quatartenionic Free Probability Theory 203 6.1 Quatartenionic Free ProbabilityTheory 204 6.2 R-diagonalMatrices 209 6.3 The Sum of Non-Hermitian Random Matrices 216 6.4 The Product of Non-Hermitian Random Matrices 220 6.5 Singular Value Equivalent Models 226 6.6 The Power of the Non-Hermitian Random Matrix 234 6.7 Power Series of Large Non-Hermitian Random Matrices 239 6.8 Products of Random Ginibre Matrices 246 6.9 Products of Rectangular Gaussian Random Matrices 249 6.10 Product of ComplexWishart Matrices 252 6.11 Spectral Relations between Products and Powers 254 6.12 Products of Finite-Size I.I.D. Gaussian Random Matrices 258 6.13 Lyapunov Exponents for Products of Complex Gaussian Random Matrices 260 6.14 Euclidean Random Matrices 264 6.15 Random Matrices with Independent Entries and the Circular Law 273 6.16 The Circular Law and Outliers 275 6.17 Random SVD, Single Ring Law, and Outliers 285 6.18 The Elliptic Law and Outliers 295 Bibliographical Remarks 305 7 The Mathematical Foundations of Data Collection 307 7.1 Architectures and Applications for Big Data 307 7.2 Covariance Matrix Estimation 308 7.3 Spectral Estimators for Large Random Matrices 312 7.4 Asymptotic Framework for Matrix Reconstruction 319 7.5 Optimum Shrinkage 329 7.6 A Shrinkage Approach to Large-Scale Covariance Matrix Estimation 331 7.7 Eigenvectors of Large Sample Covariance Matrix Ensembles 338 7.8 A General Class of Random Matrices 351 Bibliographical Remarks 359 8 Matrix Hypothesis Testing using Large RandomMatrices 361 8.1 Motivating Examples 362 8.2 Hypothesis Test of Two Alternative Random Matrices 363 8.3 Eigenvalue Bounds for Expectation and Variance 364 8.4 Concentration of Empirical Distribution Functions 369 8.5 Random Quadratic Forms 381 8.6 Log-Determinant of Random Matrices 382 8.7 General MANOVA Matrices 383 8.8 Finite Rank Perturbations of Large Random Matrices 386 8.9 Hypothesis Tests for High-Dimensional Datasets 391 8.9.1 Motivation for Likelihood Ratio Test (LRT) and Covariance Matrix Tests 392 8.10 Roy’s Largest Root Test 428 8.11 Optimal Tests of Hypotheses for Large Random Matrices 431 8.12 Matrix Elliptically Contoured Distributions 444 8.13 Hypothesis Testing for Matrix Elliptically Contoured Distributions 446 Bibliographical Remarks 452 Part II Smart Grid 455 9 Applications and Requirements of Smart Grid 457 9.1 History 457 9.2 Concepts and Vision 458 9.3 Today’s Electric Grid 459 9.4 Future Smart Electrical Energy System 464 10 Technical Challenges for Smart Grid 471 Bibliographical Remarks 483 11 Big Data for Smart Grid 485 11.1 Power in Numbers: Big Data and Grid Infrastructure 485 11.2 Energy’s Internet:The Convergence of Big Data and the Cloud 486 11.3 Edge Analytics: Consumers, Electric Vehicles, and Distributed Generation 486 11.4 CrosscuttingThemes: Big Data 486 11.5 Cloud Computing for Smart Grid 488 11.6 Data Storage, Data Access and Data Analysis 488 11.7 The State-of-the-Art Processing Techniques of Big Data 488 11.8 Big Data Meets the Smart Electrical Grid 488 11.9 4Vs of Big Data: Volume, Variety, Value and Velocity 489 11.10 Cloud Computing for Big Data 490 11.11 Big Data for Smart Grid 490 11.12 Information Platforms for Smart Grid 491 Bibliographical Remarks 491 12 Grid Monitoring and State Estimation 493 12.1 Phase Measurement Unit 493 12.2 Optimal PMU Placement 495 12.3 State Estimation 495 12.4 Basics of State Estimation 495 12.5 Evolution of State Estimation 496 12.6 Static State Estimation 497 12.7 Forecasting-Aided State Estimation 500 12.8 Phasor Measurement Units 501 12.9 Distributed System State Estimation 502 12.10 Event-Triggered Approaches to State Estimation 502 12.11 Bad Data Detection 502 12.12 Improved Bad Data Detection 504 12.13 Cyber-Attacks 504 12.14 Line Outage Detection 504 Bibliographical Remarks 504 13 False Data Injection Attacks against State Estimation 505 13.1 State Estimation 505 13.2 False Data Injection Attacks 507 13.3 MMSE State Estimation and Generalized Likelihood Ratio Test 508 13.4 Sparse Recovery from Nonlinear Measurements 512 13.5 Real-Time Intrusion Detection 515 Bibliographical Remarks 515 14 Demand Response 517 14.1 Why Engage Demand? 517 14.2 Optimal Real-time Pricing Algorithms 520 14.3 Transportation Electrification and Vehicle-to-Grid Applications 522 14.4 Grid Storage 522 Bibliographical Remarks 523 Part III Communications and Sensing 525 15 Big Data for Communications 527 15.1 5G and Big Data 527 15.2 5GWireless Communication Networks 527 15.3 Massive Multiple Input, Multiple Output 528 15.4 Free Probability for the Capacity of the Massive MIMO Channel 537 15.5 Spectral Sensing for Cognitive Radio 539 Bibliographical Remarks 539 16 Big Data for Sensing 541 16.1 Distributed Detection and Estimation 541 16.2 Euclidean Random Matrix 547 16.3 Decentralized Computing 548 Appendix A: Some Basic Results on Free Probability 551 Appendix B: Matrix-Valued Random Variables 557 References 567 Index 601

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Book SynopsisExploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data.Table of ContentsPrologue 1 Berkeley, 1930s 1 Pattern Recognition 2 Nelson Peltz 3 Committing to One Percent 5 The Big Data Revolution 6 Introduction 7 Storytelling 7 Objective 7 Outline 8 Part I “The Revolution Starts Now: 9 Industries Transforming with Data” 8 Part II “Learning from Patterns in Big Data” 11 Part III “Leading the Revolution” 11 Storytelling (Continued) 13 Part I: the Revolution Starts Now: 9 Industries Transforming With Data 15 Chapter 1: Transforming Farms with Data 17 California, 2013 17 Brief History of Farming 18 The Data Era 19 Potato Farming 20 Precision Farming 21 Capturing Farm Data 22 Deere & Company Versus Monsanto 24 Integrated Farming Systems 25 Data Prevails 26 The Climate Corporation 26 Growsafe Systems 27 Farm of the Future 27 California, 2013 (Continued) 29 Chapter 2: Why Doctors Will Have Math Degrees 31 United States, 2014 31 The History of Medical Education 32 Scientific Method 32 Rise of Specialists 33 We Have a Problem 34 Ben Goldacre 35 Vinod Khosla 35 The Data Era 36 Collecting Data 36 Telemedicine 38 Innovating with Data 40 Implications of a Data-Driven Medical World 42 The Future of Medical School 42 A Typical Medical School 42 A Medical School for the Data Era 43 United States, 2030 44 Chapter 3: Revolutionizing Insurance: Why Actuaries Will Become Data Scientists 45 Middle of Somewhere, 2012 45 Short History of Property & Casualty Insurance and Underwriting 46 Actuarial Science In Insurance 47 Pensions, Insurance, Leases 49 Compound Interest 50 Probability 50 Mortality Data 50 Modern-Day Insurance 51 Eight Weeks to Eight Days 51 Online Policies 52 The Data Era 52 Dynamic Risk Management 52 Catastrophe Risk 54 Open Access Modeling 55 Opportunities 56 Middle of Somewhere, 2012 (Continued) 58 Chapter 4: Personalizing Retail and Fashion 59 Karolina 59 A Brief History of Retail 60 Retail Eras 60 Aristide Boucicaut 61 The Shift 62 The Data Era 63 Stitch Fix 63 Keaton Row 65 Zara 66 Karolina (Continued) 67 Chapter 5: Transforming Customer Relationships with Data 69 Buying a House 69 Brief History of Customer Service 70 Customer Service Over Time 70 Boeing 72 Financial Services 74 The Data Era 75 An Automobile Manufacturer 76 Zendesk 76 Buying a House (Continued) 77 Chapter 6: Intelligent Machines 79 Denmark 79 Intelligent Machines 80 Machine Data 81 The Data Era 82 General Electric 82 Drones 84 Tesla 86 Networks of Data 87 Denmark (Continued) 88 Chapter 7: Government and Society 89 Egypt, 2011 89 Social Media 90 Intelligence 90 Snowden Effect 91 Privacy Risk Versus Reward 91 Observation or Surveillance 93 Development Targets 93 Open Data 95 Hackathons 95 Open Access 95 Ensuring Personal Protection 96 Private Clouds 97 Sanitizing Data 97 Evidence-Based Policy 97 Public-Private Partnerships 98 Impact Bonds 101 Social Impact Bond 102 Development Impact Bonds 103 The Role of Big Data 104 Egypt, 2011 (Continued) 105 Chapter 8: Corporate Sustainability 107 City of London 107 Global Megaforces 109 Population 109 Carbon Footprint 110 Water Scarcity 110 Environmental Risk 111 BP and Exxon Mobile 111 Early Warning Systems 112 Social Media 113 Risk and Resilience 114 Measuring Sustainability 115 Long-Term Decision Making 116 Stranded Assets 117 City of London (Continued) 118 Chapter 9: Weather and Energy 119 India, 2012 119 The Weather 120 Forecasting the Weather 120 When are Weather Forecasts Wrong? 121 Chaos 122 Ensemble Forecasts 122 Communication 123 Renewable Energy 124 Solar, Hydro, and Wind Power 124 Volatile or Intermittent Supply 125 Energy Consumption 126 Smart Meters 127 Intelligent Demand-Side Management 128 India, 2012 (Continued) 129 Part II: Learning From Patterns in Big Data 131 Chapter 10: Pattern Recognition 133 Elements of Success Rhyme 133 Pattern Recognition: A Gift or Trap? 134 What Fish Teach Us About Pattern Recognition 135 Bayes’ Theorem 135 Tsukiji Market 135 Pattern Recognition 137 Rochester Institute of Technology 137 A Method for Recognizing Patterns 137 Elements of Success Rhyme (Continued) 140 Chapter 11: Why Patterns in Big Data Have Emerged 141 Meatpacking District 141 Business Models in the Data Era 142 Data as a Competitive Advantage 143 Data Improves Existing Products or Services 145 Data as the Product 145 Dun & Bradstreet 146 CoStar 148 Ihs 149 Meatpacking District (Continued) 151 Chapter 12: Patterns in Big Data 153 The Data Factor 154 Summary of Big Data Patterns 155 Redefining a Skilled Worker 155 Creating and Utilizing New Sources of Data 156 Building New Data Applications 157 Transforming and Creating New Business Processes 157 Data Collection for Competitive Advantage 158 Exposing Opinion-Based Biases 159 Real-Time Monitoring and Decision Making 159 Social Networks Leveraging and Creating Data 160 Deconstructing the Value Chain 161 New Product Offerings 161 Building for Customers Instead of Markets 162 Tradeoff Between Privacy and Insight 163 Changing the Definition of a Product 163 Inverting the Search Paradigm for Data Discovery 164 Data Security 165 New Partnerships Founded on Data 165 Shortening the Innovation Lifecycle 166 Defining New Channels to Market 166 New Economic Models 167 Forecasting and Predicting Future Events 168 Changing Incentives 168 New Partnerships (Public/Private) 169 Real-Time Monitoring and Decision Making (Early Warning Systems) 169 A Framework for Big Data Patterns 170 Part III: Leading the Revolution 171 Chapter 13: The Data Opportunity 173 What Oil Teaches Us About Data 173 Bain Study 175 Seizing the Opportunity 176 Chapter 14: Porsche 177 Rome 177 Ferdinand Porsche 178 The Birth of Porsche 178 The Porsche Sports Car 179 Porsche Today 180 Rome (Continued) 180 Chapter 15: Puma 181 Herzogenaurach 181 Advertising Wars 182 Jochen Zeitz 182 Environmental Profit and Loss 183 Herzogenaurach (Continued) 184 Chapter 16: A Methodology for Applying Big Data Patterns 185 Introduction 185 The Method 186 Step 1: Understand Data Assets 187 The Patterns 188 Step 2: Explore Data 191 Challenges 192 Questions 192 Hypotheses 193 Data 193 Models 193 Statistical Significance 194 Step 3: Design the Future 194 The Patterns 195 Step 4: Design a Data-Driven Business Model 197 The Patterns 197 Step 5: Transform Business Processes for the Data Era 199 The Patterns 199 Step 6: Design for Governance and Security 201 The Patterns 201 Step 7: Share Metrics and Incentives 202 Chapter 17: Big Data Architecture 205 Introduction 205 Architect for the Future 206 Lessons from Stuttgart 207 Big Data Reference Architectures 207 Leveraging Investments in Architecture 208 Big Data Reference Architectures 211 Business View 212 Logical View 213 Chapter 18: Business View Reference Architecture 215 Introduction 215 Men’s Trunk: A Retailer in the Data Era 216 The Business View Reference Architecture 217 Answer Fabric 218 Data Virtualization 219 Data Engines 220 Management 221 Data Governance 221 User Interface, Applications, and Business Processes 222 Summary 222 Chapter 19: Logical View Reference Architecture 223 Introduction 223 Men’s Trunk: A Retailer in the Data Era (Continued) 224 The Logical View Reference Architecture 226 Data Ingest 227 Analytics 227 Discovery 228 Landing 228 Operational Warehouse 229 Information Insight 230 Operational Data 231 Governance 231 Men’s Trunk: A Retailer in the Data Era (Continued) 232 Chapter 20: The Architecture of the Future 233 Men’s Trunk: A Retailer in the Data Era (Continued) 233 Men’s Trunk: Applying the Methodology 235 Step 1: Understand Data Assets 235 Step 2: Explore the Data 236 Step 3: Design the Future 237 Step 4: Design a Data-Driven Business Model 237 Step 5: Transform Business Processes for the Data Era 237 Step 6: Design for Governance and Security 237 Step 7: Share Metrics and Incentives 238 Men’s Trunk: The Business View Reference Architecture 239 Answer Fabric 240 Data Virtualization 241 Data Engines 241 Management 242 Data Governance 242 User Interface, Applications, and Business Processes 243 Men’s Trunk: The Logical View Reference Architecture 244 Approach 244 Men’s Trunk: A Retailer in the Data Era (Continued) 248 Epilogue 249 The Time is Now 249 Taking Action 250 Fear not Usual Competitors 251 The Future 252 Index 255

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Book SynopsisAn essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data.Table of ContentsForeword xi Acknowledgments xv Introduction 1 Who Should Read This Book? 3 What’s in This Book? 4 How to Contact Us 6 Chapter 1 Healthcare, History, and Heartbreak 7 Top Issues in Healthcare 9 Data Management 16 Biosimilars, Drug Pricing, and Pharmaceutical Compounding 18 Promising Areas of Innovation 19 Conclusion 25 Notes 25 Chapter 2 Genome Sequencing: Know Thyself, One Base Pair at a Time 27 Content contributed by Sheetal Shetty and Jacob Brill Challenges of Genomic Analysis 29 The Language of Life 30 A Brief History of DNA Sequencing 31 DNA Sequencing and the Human Genome Project 35 Select Tools for Genomic Analysis 38 Conclusion 47 Notes 48 Chapter 3 Data Management 53 Content contributed by Joe Arnold Bits about Data 54 Data Types 56 Data Security and Compliance 59 Data Storage 66 SwiftStack 70 OpenStack Swift Architecture 78 Conclusion 94 Notes 94 Chapter 4 Designing a Data-Ready Network Infrastructure 105 Research Networks: A Primer 108 ESnet at 30: Evolving toward Exascale and Raising Expectations 109 Internet2 Innovation Platform 111 Advances in Networking 113 InfiniBand and Microsecond Latency 114 The Future of High-Performance Fabrics 117 Network Function Virtualization 119 Software-Defined Networking 121 OpenDaylight 122 Conclusion 157 Notes 157 Chapter 5 Data-Intensive Compute Infrastructures 163 Content contributed by Dijiang Huang, Yuli Deng, Jay Etchings, Zhiyuan Ma, and Guangchun Luo Big Data Applications in Health Informatics 166 Sources of Big Data in Health Informatics 168 Infrastructure for Big Data Analytics 171 Fundamental System Properties 186 GPU-Accelerated Computing and Biomedical Informatics 187 Conclusion 190 Notes 191 Chapter 6 Cloud Computing and Emerging Architectures 211 Cloud Basics 213 Challenges Facing Cloud Computing Applications in Biomedicine 215 Hybrid Campus Clouds 216 Research as a Service 217 Federated Access Web Portals 219 Cluster Homogeneity 220 Emerging Architectures (Zeta Architecture) 221 Conclusion 229 Notes 229 Chapter 7 Data Science 235 NoSQL Approaches to Biomedical Data Science 237 Using Splunk for Data Analytics 244 Statistical Analysis of Genomic Data with Hadoop 250 Extracting and Transforming Genomic Data 253 Processing eQTL Data 256 Generating Master SNP Files for Cases and Controls 259 Generating Gene Expression Files for Cases and Controls 260 Cleaning Raw Data Using MapReduce 261 Transpose Data Using Python 263 Statistical Analysis Using Spark 264 Hive Tables with Partitions 268 Conclusion 270 Notes 270 Appendix: A Brief Statistics Primer 290 Content Contributed by Daniel Peñaherrera Chapter 8 Next-Generation Cyberinfrastructures 307 Next-Generation Cyber Capability 308 NGCC Design and Infrastructure 310 Conclusion 327 Note 330 Conclusion 335 Appendix A The Research Data Management Survey: From Concepts to Practice 337 Brandon Mikkelsen and Jay Etchings Appendix B Central IT and Research Support 353 Gregory D. Palmer Appendix C HPC Working Example: Using Parallelization Programs Such as GNU Parallel and OpenMP with Serial Tools 377 Appendix D HPC and Hadoop: Bridging HPC to Hadoop 385 Appendix E Bioinformatics + Docker: Simplifying Bioinformatics Tools Delivery with Docker Containers 391 Glossary 399 About the Author 419 About the Contributors 421 Index 427

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Book SynopsisGet to know the why' and how' of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it's a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. Gain a solid reason to use machine learning Frame your question using financial markets laws Know your data Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effectivTable of ContentsCHAPTER 1 Do Algorithms Dream About Artificial Alphas? 1By Michael Kollo CHAPTER 2 Taming Big Data 13By Rado Lipuš and Daryl Smith CHAPTER 3 State of Machine Learning Applications in Investment Management 33By Ekaterina Sirotyuk CHAPTER 4 Implementing Alternative Data in an Investment Process 51By Vinesh Jha CHAPTER 5 Using Alternative and Big Data to Trade Macro Assets 75By Saeed Amen and Iain Clark CHAPTER 6 Big Is Beautiful: How Email Receipt Data Can Help Predict Company Sales 95By Giuliano De Rossi, Jakub Kolodziej and Gurvinder Brar CHAPTER 7 Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework 129By Tony Guida and Guillaume Coqueret CHAPTER 8 A Social Media Analysis of Corporate Culture 149By Andy Moniz CHAPTER 9 Machine Learning and Event Detection for Trading Energy Futures 169By Peter Hafez and Francesco Lautizi CHAPTER 10 Natural Language Processing of Financial News 185By M. Berkan Sesen, Yazann Romahi and Victor Li CHAPTER 11 Support Vector Machine-Based Global Tactical Asset Allocation 211By Joel Guglietta CHAPTER 12 Reinforcement Learning in Finance 225By Gordon Ritter CHAPTER 13 Deep Learning in Finance: Prediction of Stock Returns with Long Short-Term Memory Networks 251By Miquel N. Alonso, Gilberto Batres-Estrada and Aymeric Moulin Biography 279

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Book SynopsisIntroduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuseTable of ContentsForeword xxv About the Author xxvii Acknowledgements xxix Preface xxxi About the Companion Site xxxv I Introduction 1 1 The Big Picture with Kondratiev and Kardashev 3 2 The Scientific Method and Data 7 3 Conventions 11 II Starting with R and Elements of Statistics 19 4 The Basics of R 21 4.1 Getting Started with R 23 4.2 Variables 26 4.3 Data Types 28 4.3.1 The Elementary Types 28 4.3.2 Vectors 29 4.3.3 Accessing Data from a Vector 29 4.3.4 Matrices 32 4.3.5 Arrays 38 4.3.6 Lists 41 4.3.7 Factors 45 4.3.8 Data Frames 49 4.3.9 Strings or the Character-type 54 4.4 Operators 57 4.4.1 Arithmetic Operators 57 4.4.2 Relational Operators 57 4.4.3 Logical Operators 58 4.4.4 Assignment Operators 59 4.4.5 Other Operators 61 4.5 Flow Control Statements 63 4.5.1 Choices 63 4.5.2 Loops 65 4.6 Functions 69 4.6.1 Built-in Functions 69 4.6.2 Help with Functions 69 4.6.3 User-defined Functions 70 4.6.4 Changing Functions 70 4.6.5 Creating Function with Default Arguments 71 4.7 Packages 72 4.7.1 Discovering Packages in R 72 4.7.2 Managing Packages in R 73 4.8 Selected Data Interfaces 75 4.8.1 CSV Files 75 4.8.2 Excel Files 79 4.8.3 Databases 79 5 Lexical Scoping and Environments 81 5.1 Environments in R 81 5.2 Lexical Scoping in R 83 6 The Implementation of OO 87 6.1 Base Types 89 6.2 S3 Objects 91 6.2.1 Creating S3 Objects 94 6.2.2 Creating Generic Methods 96 6.2.3 Method Dispatch 97 6.2.4 Group Generic Functions 98 6.3 S4 Objects 100 6.3.1 Creating S4 Objects 100 6.3.2 Using S4 Objects 101 6.3.3 Validation of Input 105 6.3.4 Constructor functions 107 6.3.5 The Data slot 108 6.3.6 Recognising Objects, Generic Functions, and Methods 108 6.3.7 CreatingS4Generics 110 6.3.8 Method Dispatch 111 6.4 The Reference Class, refclass, RC or R5 Model 113 6.4.1 Creating RC Objects 113 6.4.2 Important Methods and Attributes 117 6.5 Conclusions about the OO Implementation 119 7 Tidy R with the Tidyverse 121 7.1 The Philosophy of the Tidyverse 121 7.2 Packages in the Tidyverse 124 7.2.1 The Core Tidyverse 124 7.2.2 The Non-core Tidyverse 125 7.3 Working with the Tidyverse 127 7.3.1 Tibbles 127 7.3.2 Piping with R 132 7.3.3 Attention Points When Using the Pipe 133 7.3.4 Advanced Piping 134 7.3.5 Conclusion 137 8 Elements of Descriptive Statistics 139 8.1 Measures of Central Tendency 139 8.1.1 Mean 139 8.1.2 The Median 142 8.1.3 The Mode 143 8.2 Measures of Variation or Spread 145 8.3 Measures of Covariation 147 8.3.1 The Pearson Correlation 147 8.3.2 The Spearman Correlation 148 8.3.3 Chi-square Tests 149 8.4 Distributions 150 8.4.1 Normal Distribution 150 8.4.2 Binomial Distribution 153 8.5 Creating an Overview of Data Characteristics 155 9 Visualisation Methods 159 9.1 Scatterplots 161 9.2 Line Graphs 163 9.3 Pie Charts 165 9.4 Bar Charts 167 9.5 Boxplots 171 9.6 Violin Plots 173 9.7 Histograms 176 9.8 Plotting Functions 179 9.9 Maps and Contour Plots 180 9.10 Heat-maps 181 9.11 Text Mining 184 9.11.1 Word Clouds 184 9.11.2 Word Associations 188 9.12 Colours in R 191 10 Time Series Analysis 197 10.1 Time Series in R 197 10.1.1 The Basics of Time Series in R 197 10.2 Forecasting 200 10.2.1 Moving Average 200 10.2.2 Seasonal Decomposition 206 11 Further Reading 211 III Data Import 213 12 A Short History of Modern Database Systems 215 13 RDBMS 219 14 SQL 223 14.1 Designing the Database 223 14.2 Building the Database Structure 226 14.2.1 Installing a RDBMS 226 14.2.2 Creating the Database 228 14.2.3 Creating the Tables and Relations 229 14.3 Adding Data to the Database 235 14.4 Querying the Database 239 14.4.1 The Basic Select Query 239 14.4.2 More Complex Queries 240 14.5 Modifying the Database Structure 244 14.6 Selected Features of SQL 249 14.6.1 Changing Data 249 14.6.2 Functions in SQL 249 15 Connecting R to an SQL Database 253 IV Data Wrangling 257 16 Anonymous Data 261 17 Data Wrangling in the tidyverse 265 17.1 Importing the Data 266 17.1.1 Importing from an SQLRDBMS 266 17.1.2 Importing Flat Files in the Tidyverse 267 17.2 Tidy Data 275 17.3 Tidying Up Data with tidyr 277 17.3.1 Splitting Tables 278 17.3.2 Convert Headers to Data 281 17.3.3 Spreading One Column Over Many 284 17.3.4 Split One Columns into Many 285 17.3.5 Merge Multiple Columns Into One 286 17.3.6 Wrong Data 287 17.4 SQL-like Functionality via dplyr 288 17.4.1 Selecting Columns 288 17.4.2 Filtering Rows 289 17.4.3 Joining 290 17.4.4 Mutating Data 293 17.4.5 Set Operations 296 17.5 String Manipulation in the tidyverse 299 17.5.1 Basic String Manipulation 300 17.5.2 Pattern Matching with Regular Expressions 302 17.6 Dates with lubridate 314 17.6.1 ISO 8601 Format 315 17.6.2 Time-zones 317 17.6.3 Extract Date and Time Components 318 17.6.4 Calculating with Date-times 319 17.7 Factors with Forcats 325 18 Dealing with Missing Data 333 18.1 Reasons for Data to be Missing 334 18.2 Methods to Handle Missing Data 336 18.2.1 Alternative Solutions to Missing Data 336 18.2.2 Predictive Mean Matching(PMM) 338 18.3 R Packages to Deal with Missing Data 339 18.3.1 mice 339 18.3.2 missForest 340 18.3.3 Hmisc 341 19 Data Binning 343 19.1 What is Binning and Why Use It 343 19.2 Tuning the Binning Procedure 347 19.3 More Complex Cases: Matrix Binning 352 19.4 Weight of Evidence and Information Value 359 19.4.1 Weight of Evidence(WOE) 359 19.4.2 Information Value(IV) 359 19.4.3 WOE and IV in R 359 20 Factoring Analysis and Principle Components 363 20.1 Principle Components Analysis (PCA) 364 20.2 Factor Analysis 368 V Modelling 373 21 Regression Models 375 21.1 Linear Regression 375 21.2 Multiple Linear Regression 379 21.2.1 Poisson Regression 379 21.2.2 Non-linear Regression 381 21.3 Performance of Regression Models 384 21.3.1 Mean Square Error (MSE) 384 21.3.2 R-Squared 384 21.3.3 Mean Average Deviation(MAD) 386 22 Classification Models 387 22.1 Logistic Regression 388 22.2 Performance of Binary Classification Models 390 22.2.1 The Confusion Matrix and Related Measures 391 22.2.2 ROC 393 22.2.3 The AUC 396 22.2.4 The Gini Coefficient 397 22.2.5 Kolmogorov-Smirnov (KS) for Logistic Regression 398 22.2.6 Finding an Optimal Cut-off 399 23 Learning Machines 405 23.1 Decision Tree 407 23.1.1 Essential Background 407 23.1.2 Important Considerations 412 23.1.3 Growing Trees with the Package rpart 414 23.1.4 Evaluating the Performance of a Decision Tree 424 23.2 Random Forest 428 23.3 Artificial Neural Networks (ANNs) 434 23.3.1 The Basics of ANNs in R 434 23.3.2 Neural Networks in R 436 23.3.3 The Work-flow to for Fitting a NN 438 23.3.4 Cross Validate the NN 444 23.4 Support Vector Machine 447 23.4.1 Fitting a SVM in R 447 23.4.2 Optimizing the SVM 449 23.5 Unsupervised Learning and Clustering 450 23.5.1 k-Means Clustering 450 23.5.2 Visualizing Clusters in Three Dimensions 462 23.5.3 Fuzzy Clustering 464 23.5.4 Hierarchical Clustering 466 23.5.5 Other Clustering Methods 468 24 Towards a Tidy Modelling Cycle with modelr 469 24.1 Adding Predictions 470 24.2 Adding Residuals 471 24.3 Bootstrapping Data 472 24.4 Other Functions of modelr 474 25 Model Validation 475 25.1 Model Quality Measures 476 25.2 Predictions and Residuals 477 25.3 Bootstrapping 479 25.3.1 Bootstrapping in Base R 479 25.3.2 Bootstrapping in the tidyverse with modelr 481 25.4 Cross-Validation 483 25.4.1 Elementary Cross Validation 483 25.4.2 Monte Carlo Cross Validation 486 25.4.3 k-Fold Cross Validation 488 25.4.4 Comparing Cross Validation Methods 489 25.5 Validation in a Broader Perspective 492 26 Labs 495 26.1 Financial Analysis with quantmod 495 26.1.1 The Basics of quantmod 495 26.1.2 Types of Data Available in quantmod 496 26.1.3 Plotting with quantmod 497 26.1.4 The quantmod Data Structure 500 26.1.5 Support Functions Supplied by quantmod 502 26.1.6 Financial Modelling in quantmod 504 27 Multi Criteria Decision Analysis (MCDA) 511 27.1 What and Why 511 27.2 General Work-flow 513 27.3 Identify the Issue at Hand: Steps 1 and 2 516 27.4 Step3: the Decision Matrix 518 27.4.1 Construct a Decision Matrix 518 27.4.2 Normalize the Decision Matrix 520 27.5 Step 4: Delete Inefficient and Unacceptable Alternatives 521 27.5.1 Unacceptable Alternatives 521 27.5.2 Dominance – Inefficient Alternatives 521 27.6 Plotting Preference Relationships 524 27.7 Step5: MCDA Methods 526 27.7.1 Examples of Non-compensatory Methods 526 27.7.2 The Weighted Sum Method(WSM) 527 27.7.3 Weighted Product Method(WPM) 530 27.7.4 ELECTRE 530 27.7.5 PROMethEE 540 27.7.6 PCA(Gaia) 553 27.7.7 Outranking Methods 557 27.7.8 Goal Programming 558 27.8 Summary MCDA 561 VI Introduction to Companies 563 28 Financial Accounting (FA) 567 28.1 The Statements of Accounts 568 28.1.1 Income Statement 568 28.1.2 Net Income: The P&L statement 568 28.1.3 Balance Sheet 569 28.2 The Value Chain 571 28.3 Further, Terminology 573 28.4 Selected Financial Ratios 575 29 Management Accounting 583 29.1 Introduction 583 29.1.1 Definition of Management Accounting (MA) 583 29.1.2 Management Information Systems (MIS) 584 29.2 Selected Methods in MA 585 29.2.1 Cost Accounting 585 29.2.2 Selected Cost Types 587 29.3 Selected Use Cases of MA 590 29.3.1 Balanced Scorecard 590 29.3.2 Key Performance Indicators (KPIs) 591 30 Asset Valuation Basics 597 30.1 Time Value of Money 598 30.1.1 Interest Basics 598 30.1.2 Specific Interest Rate Concepts 598 30.1.3 Discounting 600 30.2 Cash 601 30.3 Bonds 602 30.3.1 Features of a Bond 602 30.3.2 Valuation of Bonds 604 30.3.3 Duration 606 30.4 The Capital Asset Pricing Model (CAPM) 610 30.4.1 The CAPM Framework 610 30.4.2 The CAPM and Risk 612 30.4.3 Limitations and Shortcomings of the CAPM 612 30.5 Equities 614 30.5.1 Definition 614 30.5.2 Short History 614 30.5.3 Valuation of Equities 615 30.5.4 Absolute Value Models 616 30.5.5 Relative Value Models 625 30.5.6 Selection of Valuation Methods 630 30.5.7 Pitfalls in Company Valuation 631 30.6 Forwards and Futures 638 30.7 Options 640 30.7.1 Definitions 640 30.7.2 Commercial Aspects 642 30.7.3 Short History 643 30.7.4 Valuation of Options at Maturity 644 30.7.5 The Black and Scholes Model 649 30.7.6 The Binomial Model 654 30.7.7 Dependencies of the Option Price 660 30.7.8 The Greeks 664 30.7.9 Delta Hedging 665 30.7.10 Linear Option Strategies 667 30.7.11 Integrated Option Strategies 674 30.7.12 Exotic Options 678 30.7.13 Capital Protected Structures 680 VII Reporting 683 31 A Grammar of Graphics with ggplot2 687 31.1 TheBasicsofggplot2 688 31.2 Over-plotting 692 31.3 CaseStudyforggplot2 696 32 R Markdown 699 33 knitr and LATEX 703 34 An Automated Development Cycle 707 35 Writing and Communication Skills 709 36 Interactive Apps 713 36.1 Shiny 715 36.2 Browser Born Data Visualization 719 36.2.1 HTML-widgets 719 36.2.2 Interactive Maps with leaflet 720 36.2.3 Interactive Data Visualisation with ggvis 721 36.2.4 googleVis 723 36.3 Dashboards 725 36.3.1 The Business Case: a Diversity Dashboard 726 36.3.2 A Dashboard with flexdashboard 731 36.3.3 A Dashboard with shinydashboard 737 VIII Bigger and Faster R 741 37 Parallel Computing 743 37.1 Combine foreach and doParallel 745 37.2 Distribute Calculations over LAN with Snow 748 37.3 Using the GPU 752 37.3.1 Getting Started with gpuR 754 37.3.2 On the Importance of Memory use 757 37.3.3 Conclusions for GPU Programming 759 38 R and Big Data 761 38.1 Use a Powerful Server 763 38.1.1 Use R on a Server 763 38.1.2 Let the Database Server do the Heavy Lifting 763 38.2 Using more Memory than we have RAM 765 39 Parallelism for Big Data 767 39.1 Apache Hadoop 769 39.2 Apache Spark 771 39.2.1 Installing Spark 771 39.2.2 Running Spark 773 39.2.3 SparkR 776 39.2.4 sparklyr 788 39.2.5 SparkR or sparklyr 791 40 The Need for Speed 793 40.1 Benchmarking 794 40.2 Optimize Code 797 40.2.1 Avoid Repeating the Same 797 40.2.2 Use Vectorisation where Appropriate 797 40.2.3 Pre-allocating Memory 799 40.2.4 Use the Fastest Function 800 40.2.5 Use the Fastest Package 801 40.2.6 Be Mindful about Details 802 40.2.7 Compile Functions 804 40.2.8 Use C or C++ Code in R 806 40.2.9 Using a C++ Source File in R 809 40.2.10CallCompiledC++Functions in R 811 40.3 Profiling Code 812 40.3.1 The Package profr 813 40.3.2 The Package proftools 813 40.4 Optimize Your Computer 817 IX Appendices 819 A Create your own R Package 821 A.1 Creating the Package in the R Console 823 A.2 Update the Package Description 825 A.3 Documenting the Functionsxs 826 A.4 Loading the Package 827 A.5 Further Steps 828 B Levels of Measurement 829 B.1 Nominal Scale 829 B.2 Ordinal Scale 830 B.3 Interval Scale 831 B.4 Ratio Scale 832 C Trademark Notices 833 C.1 General Trademark Notices 834 C.2 R-Related Notices 835 C.2.1 Crediting Developers of R Packages 835 C.2.2 The R-packages used in this Book 835 D Code Not Shown in the Body of the Book 839 E Answers to Selected Questions 845 Bibliography 859 Nomenclature 869 Index 881

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Book SynopsisCOGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention. As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications. This book tackles all these issues and provides insight into these diversifieTable of ContentsPreface xv 1 Era of Computational Cognitive Techniques in Healthcare Systems 1Deependra Rastogi, Varun Tiwari, Shobhit Kumar and Prabhat Chandra Gupta 1.1 Introduction 2 1.2 Cognitive Science 3 1.3 Gap Between Classical Theory of Cognition 4 1.4 Cognitive Computing’s Evolution 6 1.5 The Coming Era of Cognitive Computing 7 1.6 Cognitive Computing Architecture 9 1.6.1 The Internet-of-Things and Cognitive Computing 10 1.6.2 Big Data and Cognitive Computing 11 1.6.3 Cognitive Computing and Cloud Computing 13 1.7 Enabling Technologies in Cognitive Computing 13 1.7.1 Reinforcement Learning and Cognitive Computing 13 1.7.2 Cognitive Computing with Deep Learning 15 1.7.2.1 Relational Technique and Perceptual Technique 15 1.7.2.2 Cognitive Computing and Image Understanding 16 1.8 Intelligent Systems in Healthcare 17 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) 20 1.9 The Cognitive Challenge 32 1.9.1 Case Study: Patient Evacuation 32 1.9.2 Case Study: Anesthesiology 32 1.10 Conclusion 34 References 35 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics 41Ana Carolina Borges Monteiro, Reinaldo Padilha França, Rangel Arthur and Yuzo Iano 2.1 Introduction 42 2.2 Literature Concept 44 2.2.1 Cognitive Computing Concept 44 2.2.2 Neural Networks Concepts 47 2.2.3 Convolutional Neural Network 49 2.2.4 Deep Learning 52 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) 55 2.4 Case Study and Discussion 57 2.5 Conclusions with Future Research Scopes 60 References 61 3 Convergence of Big Data and Cognitive Computing in Healthcare 67R. Sathiyaraj, U. Rahamathunnisa, M.V. Jagannatha Reddy and T. Parameswaran 3.1 Introduction 68 3.2 Literature Review 70 3.2.1 Role of Cognitive Computing in Healthcare Applications 70 3.2.2 Research Problem Study by IBM 73 3.2.3 Purpose of Big Data in Healthcare 74 3.2.4 Convergence of Big Data with Cognitive Computing 74 3.2.4.1 Smart Healthcare 74 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare 75 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification 76 3.3.1 EEG Pathology Diagnoses 76 3.3.2 Cognitive–Big Data-Based Smart Healthcare 77 3.3.3 System Architecture 79 3.3.4 Detection and Classification of Pathology 80 3.3.4.1 EEG Preprocessing and Illustration 80 3.3.4.2 CNN Model 80 3.3.5 Case Study 81 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud 83 3.4.1 Cloud Computing with Big Data in Healthcare 86 3.4.2 Heart Diseases 87 3.4.3 Healthcare Big Data Techniques 88 3.4.3.1 Rule Set Classifiers 88 3.4.3.2 Neuro Fuzzy Classifiers 89 3.4.3.3 Experimental Results 91 3.5 Conclusion 92 References 93 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging 97R. Indrakumari, Nilanjana Pradhan, Shrddha Sagar and Kiran Singh 4.1 Introduction 98 4.2 The Role of Technology in an Aging Society 99 4.3 Literature Survey 100 4.4 Health Monitoring 101 4.5 Nutrition Monitoring 105 4.6 Stress-Log: An IoT-Based Smart Monitoring System 106 4.7 Active Aging 108 4.8 Localization 108 4.9 Navigation Care 111 4.10 Fall Monitoring 113 4.10.1 Fall Detection System Architecture 114 4.10.2 Wearable Device 114 4.10.3 Wireless Communication Network 114 4.10.4 Smart IoT Gateway 115 4.10.5 Interoperability 115 4.10.6 Transformation of Data 115 4.10.7 Analyzer for Big Data 115 4.11 Conclusion 115 References 116 5 Influence of Cognitive Computing in Healthcare Applications 121Lucia Agnes Beena T. and Vinolyn Vijaykumar 5.1 Introduction 122 5.2 Bond Between Big Data and Cognitive Computing 124 5.3 Need for Cognitive Computing in Healthcare 126 5.4 Conceptual Model Linking Big Data and Cognitive Computing 128 5.4.1 Significance of Big Data 128 5.4.2 The Need for Cognitive Computing 129 5.4.3 The Association Between the Big Data and Cognitive Computing 130 5.4.4 The Advent of Cognition in Healthcare 132 5.5 IBM’s Watson and Cognitive Computing 133 5.5.1 Industrial Revolution with Watson 134 5.5.2 The IBM’s Cognitive Computing Endeavour in Healthcare 135 5.6 Future Directions 137 5.6.1 Retail 138 5.6.2 Research 139 5.6.3 Travel 139 5.6.4 Security and Threat Detection 139 5.6.5 Cognitive Training Tools 140 5.7 Conclusion 141 References 141 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems 145Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur and Yuzo Iano 6.1 Introduction 146 6.2 Literature Concept 148 6.2.1 Cognitive Computing Concept 148 6.2.1.1 Application Potential 151 6.2.2 Cognitive Computing in Healthcare 153 6.2.3 Deep Learning in Healthcare 157 6.2.4 Natural Language Processing in Healthcare 160 6.3 Discussion 162 6.4 Trends 163 6.5 Conclusions 164 References 165 7 Protecting Patient Data with 2F- Authentication 169G. S. Pradeep Ghantasala, Anu Radha Reddy and R. Mohan Krishna Ayyappa 7.1 Introduction 170 7.2 Literature Survey 175 7.3 Two-Factor Authentication 177 7.3.1 Novel Features of Two-Factor Authentication 178 7.3.2 Two-Factor Authentication Sorgen 178 7.3.3 Two-Factor Security Libraries 179 7.3.4 Challenges for Fitness Concern 180 7.4 Proposed Methodology 181 7.5 Medical Treatment and the Preservation of Records 186 7.5.1 Remote Method of Control 187 7.5.2 Enabling Healthcare System Technology 187 7.6 Conclusion 189 References 190 8 Data Analytics for Healthcare Monitoring and Inferencing 197Gend Lal Prajapati, Rachana Raghuwanshi and Rambabu Raghuwanshi 8.1 An Overview of Healthcare Systems 198 8.2 Need of Healthcare Systems 198 8.3 Basic Principle of Healthcare Systems 199 8.4 Design and Recommended Structure of Healthcare Systems 199 8.4.1 Healthcare System Designs on the Basis of these Parameters 200 8.4.2 Details of Healthcare Organizational Structure 201 8.5 Various Challenges in Conventional Existing Healthcare System 202 8.6 Health Informatics 202 8.7 Information Technology Use in Healthcare Systems 203 8.8 Details of Various Information Technology Application Use in Healthcare Systems 203 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below 204 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems 205 8.11 Healthcare Data Analytics 206 8.12 Healthcare as a Concept 206 8.13 Healthcare’s Key Technologies 207 8.14 The Present State of Smart Healthcare Application 207 8.15 Data Analytics with Machine Learning Use in Healthcare Systems 208 8.16 Benefit of Data Analytics in Healthcare System 210 8.17 Data Analysis and Visualization: COVID-19 Case Study in India 210 8.18 Bioinformatics Data Analytics 222 8.18.1 Notion of Bioinformatics 222 8.18.2 Bioinformatics Data Challenges 222 8.18.3 Sequence Analysis 222 8.18.4 Applications 223 8.18.5 COVID-19: A Bioinformatics Approach 224 8.19 Conclusion 224 References 225 9 Features Optimistic Approach for the Detection of Parkinson’s Disease 229R. Shantha Selva Kumari, L. Vaishalee and P. Malavikha 9.1 Introduction 230 9.1.1 Parkinson’s Disease 230 9.1.2 Spect Scan 231 9.2 Literature Survey 232 9.3 Methods and Materials 233 9.3.1 Database Details 233 9.3.2 Procedure 234 9.3.3 Pre-Processing Done by PPMI 235 9.3.4 Image Analysis and Features Extraction 235 9.3.4.1 Image Slicing 235 9.3.4.2 Intensity Normalization 237 9.3.4.3 Image Segmentation 239 9.3.4.4 Shape Features Extraction 240 9.3.4.5 SBR Features 241 9.3.4.6 Feature Set Analysis 242 9.3.4.7 Surface Fitting 242 9.3.5 Classification Modeling 243 9.3.6 Feature Importance Estimation 246 9.3.6.1 Need for Analysis of Important Features 246 9.3.6.2 Random Forest 247 9.4 Results and Discussion 248 9.4.1 Segmentation 248 9.4.2 Shape Analysis 249 9.4.3 Classification 249 9.5 Conclusion 252 References 253 10 Big Data Analytics in Healthcare 257Akanksha Sharma, Rishabha Malviya and Ramji Gupta 10.1 Introduction 258 10.2 Need for Big Data Analytics 260 10.3 Characteristics of Big Data 264 10.3.1 Volume 264 10.3.2 Velocity 265 10.3.3 Variety 265 10.3.4 Veracity 265 10.3.5 Value 265 10.3.6 Validity 265 10.3.7 Variability 266 10.3.8 Viscosity 266 10.3.9 Virality 266 10.3.10 Visualization 266 10.4 Big Data Analysis in Disease Treatment and Management 267 10.4.1 For Diabetes 267 10.4.2 For Heart Disease 268 10.4.3 For Chronic Disease 270 10.4.4 For Neurological Disease 271 10.4.5 For Personalized Medicine 271 10.5 Big Data: Databases and Platforms in Healthcare 279 10.6 Importance of Big Data in Healthcare 285 10.6.1 Evidence-Based Care 285 10.6.2 Reduced Cost of Healthcare 285 10.6.3 Increases the Participation of Patients in the Care Process 285 10.6.4 The Implication in Health Surveillance 285 10.6.5 Reduces Mortality Rate 285 10.6.6 Increase of Communication Between Patients and Healthcare Providers 286 10.6.7 Early Detection of Fraud and Security Threats in Health Management 286 10.6.8 Improvement in the Care Quality 286 10.7 Application of Big Data Analytics 286 10.7.1 Image Processing 286 10.7.2 Signal Processing 287 10.7.3 Genomics 288 10.7.4 Bioinformatics Applications 289 10.7.5 Clinical Informatics Application 291 10.8 Conclusion 293 References 294 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery 303V. Sathananthavathi and G. Indumathi 11.1 Introduction 304 11.1.1 Glaucoma 304 11.2 Literature Survey 306 11.3 Methodology 309 11.3.1 Sclera Segmentation 310 11.3.1.1 Fully Convolutional Network 311 11.3.2 Pupil/Iris Ratio 313 11.3.2.1 Canny Edge Detection 314 11.3.2.2 Mean Redness Level (MRL) 315 11.3.2.3 Red Area Percentage (RAP) 316 11.4 Results and Discussion 317 11.4.1 Feature Extraction from Frontal Eye Images 318 11.4.1.1 Level of Mean Redness (MRL) 318 11.4.1.2 Percentage of Red Area (RAP) 318 11.4.2 Images of the Frontal Eye Pupil/Iris Ratio 318 11.4.2.1 Histogram Equalization 319 11.4.2.2 Morphological Reconstruction 319 11.4.2.3 Canny Edge Detection 319 11.4.2.4 Adaptive Thresholding 320 11.4.2.5 Circular Hough Transform 321 11.4.2.6 Classification 322 11.5 Conclusion and Future Work 324 References 325 12 State of Mental Health and Social Media: Analysis, Challenges, Advancements 327Atul Pankaj Patil, Kusum Lata Jain, Smaranika Mohapatra and Suyesha Singh 12.1 Introduction 328 12.2 Introduction to Big Data and Data Mining 328 12.3 Role of Sentimental Analysis in the Healthcare Sector 330 12.4 Case Study: Analyzing Mental Health 332 12.4.1 Problem Statement 332 12.4.2 Research Objectives 333 12.4.3 Methodology and Framework 333 12.4.3.1 Big 5 Personality Model 333 12.4.3.2 Openness to Explore 334 12.4.3.3 Methodology 335 12.4.3.4 Detailed Design Methodologies 340 12.4.3.5 Work Done Details as Required 341 12.5 Results and Discussion 343 12.6 Conclusion and Future 345 References 346 13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease 349Geetanjali, Rishabha Malviya, Rajendra Awasthi, Pramod Kumar Sharma, Nidhi Kala, Vinod Kumar and Sanjay Kumar Yadav 13.1 Introduction 350 13.2 Artificial Intelligence and Management of Chronic Diseases 351 13.3 Blockchain and Healthcare 354 13.3.1 Blockchain and Healthcare Management of Chronic Disease 355 13.4 Internet-of-Things and Healthcare Management of Chronic Disease 358 13.5 Conclusions 360 References 360 14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain 367BKSP Kumar Raju Alluri 14.1 Introduction 367 14.2 Cognitive Computing Framework in Healthcare 371 14.3 Benefits of Using Cognitive Computing for Healthcare 372 14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management 374 14.4.1 Using Cognitive Services for a Patient’s Healthcare Management 375 14.4.2 Using Cognitive Services for Healthcare Providers 376 14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management 377 14.6 Future Directions for Extending Heathcare Services Using CATs 380 14.7 Addressing CAT Challenges in Healthcare as a General Framework 384 14.8 Conclusion 384 References 385 Index 391

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Book Synopsis.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor TexTable of Contents.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Text.- Annotation.- Annotation-based Image Retrieval.- Anomaly Detection on Streams.- Anonymity.- ANSI/INCITS RBAC Standard.- Answering Queries Using Views.- Anti-monotone Constraints.- Applicability Period.- Application Benchmark.- Application Recovery.- Application Server.- Application-Level Tuning.- Applications of Emerging Patterns for Microarray Gene Expression Data Analysis.- Applications of Sensor Network Data Management.- Approximate Queries in Peer-to-Peer Systems.- Approximate Query Processing.- Approximate Reasoning.- Approximation of Frequent Itemsets.- Apriori Property and Breadth-First Search Algorithms.- Architecture-Conscious Database System.- Archiving Experimental Data.- Armstrong Axioms.- Array Databases.- Array Databases_old.- Association Rule Mining on Streams.- Association Rules.- Asymmetric Encryption.- Atelic Data.- Atomic Event.- Atomicity.- Audio.- Audio Classification.- Audio Content Analysis.- Audio Metadata.- Audio Representation.- Audio Segmentation.- Auditing and Forensic Analysis.- Authentication.- Automatic Image Annotation.- Autonomous Replication.- Average Precision.- Average Precision at n.- Average Precision Histogram.- Average R-Precision.- B+-Tree.- Backup and Restore.- Bag Semantics.- Bagging.- Bayesian Classification.- Benchmark Frameworks.- Benchmarks for Big Data Analytics.- Big Data Platforms for Data Analytics.- Big Stream Systems.- Biological Metadata Management.- Biological Networks.- Biological Resource Discovery.- Biological Sequences.- Biomedical Data/Content Acquisition, Curation.- Biomedical Image Data Types and Processing.- Biomedical Scientific Textual Data Types and Processing.- Biostatistics and Data Analysis.- Bi-Temporal Indexing.- Bitemporal Interval.- Bitemporal Relation.- Bitmap Index.- Bitmap-based Index Structures.- Blind Signatures.- Bloom Filters.- BM25.- Boolean Model.- Boosting.- Bootstrap.- Boyce-Codd Normal Form.- BP-Completeness.- Bpref.- Browsing.- Browsing in Digital Libraries.- B-Tree Locking.- Buffer Management.- Buffer Manager.- Buffer Pool.- Business Intelligence.- Business Process Execution Language.- Business Process Management.- Business Process Modeling Notation.- Business Process Reengineering.- Cache-Conscious Query Processing.- Calendar.- Calendric System.- CAP Theorem.- Cardinal Direction Relationships.- Cartesian Product.- Cataloging in Digital Libraries.- Causal Consistency.- Certain (and Possible) Answers.- Change Detection on Streams.- Channel-Based Publish/Subscribe.- Chart.- Chase.- Checksum and Cyclic Redundancy Check Mechanism.- Choreography.- Chronon.- Citation.- Classification.- Classification by Association Rule Analysis.- Classification in Streams.- Client-Server Architecture.- Clinical Data Acquisition, Storage and Management.- Clinical Data and Information Models.- Clinical Data Quality and Validation.- Clinical Decision Support.- Clinical Document Architecture.- Clinical Event.- Clinical Knowledge Repository.- Clinical Observation.- Clinical Ontologies.- Clinical Order.- Closed Itemset Mining and Non-redundant Association Rule Mining.- Closest-Pair Query.- Cloud Computing.- Cloud Intelligence.- Cluster and Distance Measure.- Clustering for Post Hoc Information Retrieval.- Clustering on Streams.- Clustering Overview and Applications.- Clustering Validity.- Clustering with Constraints.- Collaborative Filtering.- Column Segmentation.- Column Stores.- Common Warehouse Metamodel.- Comparative Visualization.- Compensating Transactions.- Complex Event.- Complex Event Processing.- Composed Services and WS-BPEL.- Composite Event.- Composition.- Comprehensions.- Compression of Mobile Location Data.- Computational Media Aesthetics.- Computationally Complete Relational Query Languages.- Computerized Physician Order Entry.- Conceptual Modeling Foundations.- Conceptual Schema Design.- Concurrency Control - Traditional Approaches.- Concurrency Control for Replicated Databases.- Concurrency Control Manager.- Conditional Tables.- Conjunctive Query.- Connection.- Consistency Models For Replicated Data.- Consistent Query Answering.- Constraint Databases.- Constraint Query Languages.- Constraint-Driven Database Repair.- Content-and-Structure Query.- Content-Based Publish/Subscribe.- Content-Based Video Retrieval.- Content-Only Query.- Context.- Contextualization in Structured Text Retrieval.- Continuous Data Protection.- Continuous Monitoring of Spatial Queries.- Continuous Multimedia Data Retrieval.- Continuous Queries in Sensor Networks.- Continuous Query.- ConTract.- Control Data.- Convertible Constraints.- Coordination.- Copyright Issues in Databases.- CORBA.- Correctness Criteria Beyond Serializability.- Cost and quality trade-offs in crowdsourcing.- Cost Estimation.- Count-Min Sketch.- Coupling and De-coupling.- Covering Index.- Crash Recovery.- Cross-Language Mining and Retrieval.- Cross-Modal Multimedia Information Retrieval.- Cross-Validation.- Crowd Database Operators.- Crowd Database Systems.- Crowd Mining and Analysis.- Crowdsourcing Geographic Information Systems.- Cube.- Cube Implementations.- Current Semantics.- Curse of Dimensionality.- Daplex.- Data Acquisition and Dissemination in Sensor Networks.- Data Aggregation in Sensor Networks.- Data Broadcasting, Caching and Replication in Mobile Computing.- Data Cleaning.- Data Compression in Sensor Networks.- Data Conflicts.- Data Definition.- Data Definition Language (DDL).- Data Dictionary.- Data Encryption.- Data Estimation in Sensor Networks.- Data Exchange.- Data Fusion.- Data Fusion in Sensor Networks.- Data Generation.- Data Governance.- Data Integration Architectures and Methodology for the Life Sciences.- Data Integration in Web Data Extraction System.- Data Management for VANETs.- Data Management Fundamentals: Database Management System.- Data Management in Data Centers.- Data Manipulation.- Data Manipulation Language (DML).- Data Mart.- Data Migration Management.- Data Mining.- Data Partitioning.- Data Privacy and Patient Consent.- Data Profiling.- Data Provenance.- Data Quality Assessment.- Data Quality Dimensions.- Data Quality Models.- Data Rank/Swapping.- Data Reduction.- Data Replication.- Data Sampling.- Data Scrubbing.- Data Sketch/Synopsis.- Data Skew.- Data Storage and Indexing in Sensor Networks.- Data Stream.- Data Stream Management Architectures and Prototypes.- Data Types in Scientific Data Management.- Data Uncertainty Management in Sensor Networks.- Data Visualization.- Data Warehouse.- Data Warehouse Life-Cycle and Design.- Data Warehouse Maintenance, Evolution and Versioning.- Data Warehouse Metadata.- Data Warehouse Security.- Data Warehousing for Clinical Research.- Data Warehousing in Cloud Environments.- Data Warehousing on Non-Conventional Data.- Data Warehousing Systems: Foundations and Architectures.- Data, Text, and Web Mining in Healthcare.- Database.- Database Adapter and Connector.- Database Administrator (DBA).- Database Appliances.- Database Benchmarks.- Database Clustering Methods.- Database Clusters.- Database Dependencies.- Database Design.- Database Languages for Sensor Networks.- Database Machine.- Database Management System.- Database Middleware.- Database Repair.- Database Reverse Engineering.- Database Schema.- Database Security.- Database System.- Database Techniques to Improve Scientific Simulations.- Database Trigger.- Database Tuning using Combinatorial Search.- Database Tuning using Online Algorithms.- Database Tuning using Trade-off Elimination.- Database Use in Science Applications.- Datalog.- DBMS Component.- DBMS Interface.- DCE.- DCOM.- Decay Models.- Decision Rule Mining in Rough Set Theory.- Decision Tree Classification.- Decision Trees.- Declarative Networking.- Deductive Data Mining using Granular Computing.- Deduplication.- Deduplication in Data Cleaning.- Deep Instantiation.- Deep-Web Search.- Dense Index.- Dense Pixel Displays.- Density-based Clustering.- Description Logics.- Design for Data Quality.- Dewey Decimal System.- Diagram.- Difference.- Differential Privacy.- Digital Archives and Preservation.- Digital Curation.- Digital Elevation Models.- Digital Libraries.- Digital Rights Management.- Digital Signatures.- Dimension.- Dimension Reduction Techniques for Clustering.- Dimensionality Reduction.- Dimensionality Reduction Techniques For Nearest Neighbor Computations.- Dimension-Extended Topological Relationships.- Direct Attached Storage.- Direct Manipulation.- Disaster Recovery.- Disclosure Risk.- Discounted Cumulated Gain.- Discovery.- Discrete Wavelet Transform and Wavelet Synopses.- Discretionary Access Control.- Disk.- Disk Power Saving.- Distortion Techniques.- Distributed Architecture.- Distributed Concurrency Control.- Distributed Data Streams.- Distributed Database Design.- Distributed Database Systems.- Distributed DBMS.- Distributed Deadlock Management.- Distributed File Systems.- Distributed Hash Table.- Distributed Join.- Distributed Machine Learning.- Distributed Query Optimization.- Distributed Query Processing.- Distributed Recovery.- Distributed Spatial Databases.- Distributed Transaction Management.- Divergence from Randomness Models.- D-measure.- Document.- Document Clustering.- Document Databases.- Document Field.- Document Length Normalization.- Document Links and Hyperlinks.- Document Representations (Inclusive Native and Relational).- Dublin Core.- Dynamic Graphics.- Dynamic Web Pages.- eAccessibility.- ECA Rule Action.- ECA Rule Condition.- ECA Rules.- e-Commerce Transactions.- Effectiveness Involving Multiple Queries.- Ehrenfeucht-Fraïssé Games.- Elasticity.- Electronic Dictionary.- Electronic Encyclopedia.- Electronic Health Record.- Electronic Ink Indexing.- Electronic Newspapers.- Eleven Point Precision-recall Curve.- Emergent Semantics.- Emerging Pattern Based Classification.- Emerging Patterns.- Energy Efficiency in Data Centers.- Ensemble.- Enterprise Application Integration.- Enterprise Content Management.- Enterprise Service Bus.- Enterprise Terminology Services.- Entity Relationship Model.- Entity Resolution.- Entity Retrieval.- Equality-Generating Dependencies.- ERR- Expected Reciprocal Rank.- ERR-IA Intent-aware ERR.- Escrow Transactions.- European Law in Databases.- Evaluation Metrics for Structured Text Retrieval.- Evaluation of Relational Operators.- Event.- Event and Pattern Detection over Streams.- Event Causality.- Event Channel.- Event Cloud.- Event Detection.- Event Driven Architecture.- Event Flow.- Event in Active Databases.- Event in Temporal Databases.- Event Lineage.- Event Pattern Detection.- Event Prediction.- Event Processing Agent.- Event Processing Network.- Event Sink.- Event Source.- Event Specification.- Event Stream.- Event Transformation.- Event-Driven Business Process Management.- Eventual Consistency.- Evidence Based Medicine.- Executable Knowledge.- Execution Skew.- Explicit Event.- Exploratory Data Analysis.- Expressive Power of Query Languages.- Extended Entity-Relationship Model.- Extended Transaction Models and the ACTA Framework.- Extendible Hashing.- Extraction, Transformation, and Loading.- Faceted Search.- Fault-Tolerance and High Availability in Data Stream Management Systems.- Feature Extraction for Content-Based Image Retrieval.- Feature Selection for Clustering.- Feature-Based 3D Object Retrieval.- Field-Based Information Retrieval Models.- Field-Based Spatial Modeling.- First-Order Logic: Semantics.- First-Order Logic: Syntax.- Fixed Time Span.- Flex Transactions.- FM Synopsis.- F-Measure.- Focused Web Crawling.- FOL Modeling of Integrity Constraints (Dependencies).- Forever.- Form.- Fourth Normal Form.- FQL.- Fractal.- Frequency Moments.- Frequent Graph Patterns.- Frequent Items on Streams.- Frequent Itemset Mining with Constraints.- Frequent Itemsets and Association Rules.- Frequent Partial Orders.- Fully-Automatic Web Data Extraction.- Functional Data Model.- Functional Dependencies for Semi-Structured Data.- Functional Dependency.- Functional Query Language.- Fuzzy Models.- Fuzzy Relation.- Fuzzy Set.- Fuzzy Set Approach.- Fuzzy/Linguistic IF-THEN Rules and Linguistic Descriptions.- Gazetteers.- Gene Expression Arrays.- Generalization of ACID Properties.- Generalized Search Tree.- Genetic Algorithms.- Geographic Information System.- Geographical Information Retrieval.- Geography Markup Language.- Geometric Stream Mining.- GEO-RBAC Model.- Georeferencing.- Geosocial Networks.- Geospatial Metadata.- Geo-Targeted Web Search.- GMAP.- Grammar Inference.- Graph.- Graph Data Management in Scientific Applications.- Graph Database.- Graph Management in the Life Sciences.- Graph Mining.- Graph Mining on Streams.- Graph OLAP.- Graphical Models for Uncertain Data Management.- Grid and Workflows.- Grid File (and Family).- GUIs for Web Data Extraction.- Hash Functions.- Hash Join.- Hash-based Indexing.- Healthcare Metrics.- Hierarchial Clustering.- Hierarchical Data Model.- Hierarchical Data Summarization.- Hierarchical Heavy Hitter Mining on Streams.- Hierarchy.- High Dimensional Indexing.- Histogram.- Histograms on Streams.- History in Temporal Databases.- Homomorphic Encryption.- Horizontally Partitioned Data.- Human Factors Modeling in Crowdsourcing.- Human-centered Computing: Application to Multimedia.- Human-Computer Interaction.- Hypertexts.- I/O Model of Computation.- Icon.- Iconic Displays.- Image.- Image Content Modeling.- Image Database.- Image Management for Biological Data.- Image Metadata.- Image Querying.- Image Representation.- Image Retrieval and Relevance Feedback.- Image Segmentation.- Image Similarity.- Implementation of Database Operators (Joins, Group by, etc.).- Implication of Constraints.- Implications of Genomics for Clinical Informatics.- Implicit Event.- Incomplete Information.- Inconsistent Databases.- Incremental Computation of Queries.- Incremental Crawling.- Incremental Maintenance of Views with Aggregates.- Index Creation and File Structures.- Index Join.- Index Structures for Biological Sequences.- Index Tuning.- Indexed Sequential Access Method.- Indexing and Similarity Search.- Indexing Compressed Text.- Indexing Historical Spatio-Temporal Data.- Indexing in pub/sub systems.- Indexing Metric Spaces.- Indexing of Data Warehouses.- Indexing of the Current and Near-Future Positions of Moving Objects.- Indexing Techniques for Multimedia Data Retrieval.- Indexing the Web.- Indexing Uncertain Data.- Indexing Units of Structured Text Retrieval.- Indexing with Crowds.- Individually Identifiable Data.- Inference Control in Statistical Databases.- Information Extraction.- Information Filtering.- Information Foraging.- Information Integration.- Information Integration Techniques for Scientific Data.- Information Lifecycle Management.- Information Loss Measures.- Information Navigation.- Information Quality.- Information Quality and Decision Making.- Information Quality Assessment.- Information Quality Policy and Strategy.- Information Quality: Managing Information as a Product.- Information Retrieval.- Information Retrieval Models.- Information Retrieval Operations.- Infrastructure As-A-Service (IaaS).- Initiative for the Evaluation of XML Retrieval.- Initiator.- In-Network Query Processing.- Integrated DB and IR Approaches.- Integration of Rules and Ontologies.- Intelligent Storage Systems.- Interactive Analytics in Social Media.- Interface.- Interface Engines in Healthcare.- Interoperability in Data Warehouses.- Interoperation of NLP-based Systems with Clinical Databases.- Inter-Operator Parallelism.- Inter-Query Parallelism.- Intra-operator Parallelism.- Intra-Query Parallelism.- Intrusion Detection Technology.- Inverse Document Frequency.- Inverted Files.- IP Storage.- Iterator.- Java Database Connectivity.- Java Enterprise Edition.- Java Metadata Facility.- Join.- Join Dependency.- Join Index.- Join Order.- k-Anonymity.- Karp-Luby Sampling.- KDD Pipeline.- Key.- K-Means and K-Medoids.- Knowledge Base.- Knowledge Base Extraction.- Language Models.- Languages for Web Data Extraction.- Learning Distance Measures.- Lexical Analysis of Textual Data.- Licensing and Contracting Issues in Databases.- Lifespan.- Lightweight Ontologies.- Linear Hashing.- Linear Regression.- Linked Open Data.- Linking and Brushing.- Load Balancing in Peer-to-Peer Overlay Networks.- Load Shedding.- LOC METS.- Locality.- Locality of Queries.- Location Based Recommendation.- Location Management in Mobile Environments.- Location Update Management.- Location-Based Services.- Locking Granularity and Lock Types.- Logging and Recovery.- Logging/Recovery Subsystem.- Logical and Physical Data Independence.- Logical Database Design: from Conceptual to Logical Schema.- Logical Document Structure.- Logical Foundations of Web Data Extraction.- Logical Models of Information Retrieval.- Logical Unit Number.- Logical Unit Number Mapping.- Logical Volume Manager.- Log-Linear Regression.- Loop.- Loose Coupling.- Machine Learning in Computational Biology.- Main Memory.- Main Memory DBMS.- Maintenance of Materialized Views with Outer-Joins.- Maintenance of Recursive Views.- Managing Compressed Structured Text.- Managing Data Integration Uncertainty.- Managing Probabilistic Entity Extraction.- Mandatory Access Control.- MANET Databases.- MAP.- Map Matching.- MapReduce.- Markup Language.- MashUp.- Massive Array of Idle Disks.- Matrix Masking.- Max-Pattern Mining.- Mean Reciprocal Rank.- Measure.- Mediation.- Membership Query.- Memory Hierarchy.- Memory Locality.- Merkle Trees.- Message Authentication Codes.- Message Queuing Systems.- Meta Data Repository.- Meta Object Facility.- Metadata.- Metadata Interchange Specification.- Metadata Registry, ISO/IEC 11179.- Metamodel.- Metasearch Engines.- Metric Space.- Microaggregation.- Microbenchmark.- Microdata.- Microdata Rounding.- Middleware Support for Database Replication and Caching.- Middleware Support for Precise Failure Semantics.- Mining of Chemical Data.- Mobile Database.- Mobile Interfaces.- Mobile resource search.- Mobile Sensor Network Data Management.- Model Management.- Model-based Querying in Sensor Networks.- Monotone Constraints.- Monte Carlo Methods for Uncertain Data.- Moving Object.- Moving Objects Databases and Tracking.- MRR.- Multi-Data Center Consistency Properties.- Multi-Data Center Replication Protocols.- Multidimensional Data Formats.- Multidimensional Modeling.- Multidimensional Scaling.- Multi-Level Modeling.- Multi-Level Recovery and the ARIES Algorithm.- Multilevel Secure Database Management System.- Multilevel Transactions and Object-Model Transactions.- Multimedia Data.- Multimedia Data Buffering.- Multimedia Data Indexing.- Multimedia Data Querying.- Multimedia Data Storage.- Multimedia Databases.- Multimedia Information Retrieval Model.- Multimedia Metadata.- Multimedia Presentation Databases.- Multimedia Resource Scheduling.- Multimedia Retrieval Evaluation.- Multimedia Tagging.- Multimodal Interfaces.- Multi-Pathing.- Multiple Representation Modeling.- Multi-Query Optimization.- Multi-Resolution Terrain Modeling.- Multi-Step Query Processing.- Multitenancy.- Multi-Tier Architecture.- Multi-tier Storage Systems.- Multivalued Dependency.- Multivariate Visualization Methods.- Multi-version Serializability and Concurrency Control.- Naive Tables.- Narrowed Extended XPath I.- Natural Interaction.- Near-duplicate Retrieval.- Nearest Neighbor Classification.- Nearest Neighbor Query.- Nearest Neighbor Query in Spatio-temporal Databases.- Nested Loop Join.- Nested Transaction Models.- Network Attached Secure Device.- Network Attached Storage.- Network Data Model.- Neural Networks.- N-Gram Models.- Noise Addition.- Nonparametric Data Reduction Techniques.- Non-Perturbative Masking Methods.- Non-relational Streams.- Nonsequenced Semantics.- Normal Form ORA-SS Schema Diagrams.- Normal Forms and Normalization.- NoSQL Stores.- Now in Temporal Databases.- Null Values.- OASIS.- Object Constraint Language.- Object Data Models.- Object Identity.- Object Recognition.- Object Relationship Attribute Data Model for Semi-structured Data.- Object Storage Protocol.- Object-Role Modeling.- OLAM.- OLAP Personalization and Recommendation.- OLAP Personalization and Recommendation_old.- One-Copy-Serializability.- One-Pass Algorithm.- On-Line Analytical Processing.- Online Recovery in Parallel Database Systems.- Ontologies and Life Science Data Management.- Ontology.- Ontology Elicitation.- Ontology Engineering.- Ontology Visual Querying.- Ontology-Based Data Access and Integration.- Open Database Connectivity.- Open Information Extraction.- Open Nested Transaction Models.- Operator-Level Parallelism.- Opinion Mining.- Optimistic Replication and Resolution.- Optimization and Tuning in Data Warehouses.- OQL.- Orchestration.- Order Dependency.- OR-Join.- OR-Split.- OSQL.- Outlier Detection.- Overlay Network.- OWL: Web Ontology Language.- P/FDM.- Parallel and Distributed Data Warehouses.- Parallel Coordinates.- Parallel Data Placement.- Parallel Database Management.- Parallel Hash Join, Parallel Merge Join, Parallel Nested Loops Join.- Parallel Query Execution Algorithms.- Parallel Query Optimization.- Parallel Query Processing.- Parameterized Complexity of Queries.- Parametric Data Reduction Techniques.- Partial Replication.- Path Query.- Pattern-Growth Methods.- Peer Data Management System.- Peer to Peer Overlay Networks: Structure, Routing and Maintenance.- Peer-To-Peer Content Distribution.- Peer-to-Peer Data Integration.- Peer-to-Peer Publish-Subscribe Systems.- Peer-to-Peer Storage.- Peer-to-Peer System.- Peer-to-Peer Web Search.- Performance Analysis of Transaction Processing Systems.- Performance Monitoring Tools.- Period-Stamped Temporal Models.- Personalized Web Search.- Petri Nets.- Physical Clock.- Physical Database Design for Relational Databases.- Physical Layer Tuning.- Pipeline.- Pipelining.- Platform As-A-Service (PaaS).- Point-in-Time Copy.- Point-Stamped Temporal Models.- Polytransactions.- Positive Relational Algebra.- Possible Answers.- PRAM.- Precision.- Precision and Recall.- Precision at n.- Precision-Oriented Effectiveness Measures.- Predictive Analytics.- Preference Queries.- Preference Specification.- Prescriptive Analytics.- Presenting Structured Text Retrieval Results.- Primary Index.- Principal Component Analysis.- Privacy.- Privacy Metrics.- Privacy Policies and Preferences.- Privacy through Accountability.- Privacy-Enhancing Technologies.- Privacy-Preserving Data Mining.- Privacy-Preserving DBMSs.- Private Information Retrieval.- Probabilistic Databases.- Probabilistic Entity Resolution.- Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model.- Probabilistic Skylines.- Probabilistic Spatial Queries.- Probabilistic Temporal Databases.- Probability Ranking Principle.- Probability Smoothing.- Process Life Cycle.- Process Mining.- Process Modeling.- Process Optimization.- Process Structure of a DBMS.- Processing Overlaps in Structured Text Retrieval.- Processing Structural Constraints.- Processor Cache.- Profiles and Context for Structured Text Retrieval.- Projection.- Propagation-based Structured Text Retrieval.- Protection from Insider Threats.- Provenance.- Provenance and Reproducibility.- Provenance in Databases.- Provenance in Scientific Databases.- Provenance in Workflows.- Provenance Management.- Provenance Standards.- Provenance Storage.- Provenance: Privacy and Security.- Pseudonymity.- Publish/Subscribe.- Publish/Subscribe over Streams.- Punctuations.- Q-measure.- Quadtrees (and Family).- Qualitative Temporal Reasoning.- Quality and Trust of Information Content and Credentialing.- Quality of Data Warehouses.- Quantiles on Streams.- Quantitative Association Rules.- QUEL.- Query by Humming.- Query Containment.- Query Evaluation Techniques for Multidimensional Data.- Query Expansion for Information Retrieval.- Query Expansion Models.- Query Language.- Query Languages and Evaluation Techniques for Biological Sequence Data.- Query Languages for the Life Sciences.- Query Load Balancing in Parallel Database Systems.- Query Optimization.- Query Optimization (in Relational Databases).- Query Optimization in Sensor Networks.- Query Plan.- Query Point Movement Techniques for Content-Based Image Retrieval.- Query Processing.- Query Processing (in Relational Databases).- Query Processing and Optimization in Object Relational Databases.- Query Processing in data integration systems.- Query Processing in Data Warehouses.- Query Processing in Deductive Databases.- Query Processing over Uncertain Data.- Query Processor.- Query Rewriting.- Query Rewriting Using Views.- Query Translation.- Quorum Systems.- Randomization Methods to Ensure Data Privacy.- Range Query.- Rank-aware Query Processing.- Ranked XML Processing.- Ranking Functions.- Ranking Views.- Rank-Join.- Rank-Join Indices.- Raster Data Management and Multi-Dimensional Arrays.- RDF Stores.- RDF Technology.- Real and Synthetic Test Datasets.- Real-Time Transaction Processing.- Recall.- Receiver Operating Characteristic.- Recommender Systems.- Record Linkage.- Record Matching.- Redundant Arrays of Independent Disks.- Reference Knowledge.- Region Algebra.- Regulatory Compliance in Data Management.- Relational Algebra.- Relational Calculus.- Relational Model.- Relationships in Structured Text Retrieval.- Relative Time.- Relevance.- Relevance Feedback.- Relevance Feedback for Content-Based Information Retrieval.- Relevance Feedback for Text Retrieval.- Replica Control.- Replica Freshness.- Replicated Data Types.- Replicated Database Concurrency Control.- Replication.- Replication Based on Group Communication.- Replication for Availability and Fault-Tolerance.- Replication for High Availability.- Replication for Paxos.- Replication for Scalability.- Replication in Multi-Tier Architectures.- Replication with Snapshot Isolation.- Reputation and Trust.- Request Broker.- Residuated Lattice.- Resource Allocation Problems in Spatial Databases.- Resource Description Framework.- Resource Description Framework (RDF) Schema (RDFS).- Resource Identifier.- Result Display.- Retrospective Event Processing.- Reverse Nearest Neighbor Query.- Reverse Top-k Queries.- Rewriting Queries using Views.- RMI.- Road Networks.- Rocchio's Formula.- Role Based Access Control.- R-Precision.- R-Tree (and Family).- Rule-based Classification.- Safety and Domain Independence.- Sagas.- Sampling Techniques for Statistical Databases.- SAN File System.- Scalable Decision Tree Construction.- Scheduler.- Scheduling Strategies for Data Stream Processing.- Schema Evolution.- Schema Mapping.- Schema Mapping Composition.- Schema Matching.- Schema Tuning.- Schema Versioning.- Scheme/Ontology Extraction.- Scientific Databases.- Scientific Visualization.- Scientific Workflows.- Score Aggregation.- Screen Scraper.- SCSI Target.- SDC Score.- Search Engine Metrics.- Searching Digital Libraries.- Second Normal Form (2NF).- Secondary Index.- Secure Data Outsourcing.- Secure Database Development.- Secure Multiparty Computation Methods.- Secure Transaction Processing.- Security Services.- Segmentation and Stratification.- Segmentation and Stratification_old.- Selection.- Selectivity Estimation.- Self-Maintenance of Views.- Self-Management Technology in Databases.- Semantic Atomicity.- Semantic Crowd Sourcing.- Semantic Data Integration for Life Science Entities.- Semantic Data Model.- Semantic Matching.- Semantic Modeling and Knowledge Representation for Multimedia Data.- Semantic Modeling for Geographic Information Systems.- Semantic Overlay Networks.- Semantic Social Web.- Semantic Streams.- Semantic Web.- Semantic Web Query Languages.- Semantic Web Services.- Semantics-based Concurrency Control.- Semijoin.- Semijoin Program.- Semi-Structured Data.- Semi-Structured Data Model.- Semi-Structured Database Design.- Semi-Structured Query Languages.- Semi-Supervised Learning.- Sensor Networks.- Sequenced Semantics.- Sequential Patterns.- Serializability.- Serializable Snapshot Isolation.- Service Component Architecture (SCA).- Service Oriented Architecture.- Session.- Shared-Disk Architecture.- Shared-Memory Architecture.- Shared-Nothing Architecture.- Side-Effect-Free View Updates.- Signature Files.- Similarity and Ranking Operations.- Simplicial Complex.- Singular Value Decomposition.- Skyline Queries and Pareto Optimality.- Snapshot Equivalence.- Snapshot Isolation.- Snippet.- Snowflake Schema.- SOAP.- Social Applications.- Social influence.- Social Media Analysis.- Social Media Analytics.- Social Media Harvesting.- Social network analysis.- Social Networks.- Software As-A-Service (SaaS).- Software Transactional Memory.- Software-Defined Storage.- Solid State Drive (SSD).- Sort-Merge Join.- Space-Filling Curves.- Space-Filling Curves for Query Processing.- SPARQL.- Sparse Index.- Spatial and Spatio-Temporal Data Models and Languages.- Spatial and Temporal Data Warehouses .- Spatial Anonymity.- Spatial Data Analysis.- Spatial Data Mining.- Spatial Data Types.- Spatial Datawarehousing.- Spatial Indexing Techniques.- Spatial Join.- Spatial Keyword Search.- Spatial Matching Problems.- Spatial Network Databases.- Spatial Operations and Map Operations.- Spatial Queries in the Cloud.- Spatio-Temporal Data Mining.- Spatio-Temporal Data Types.- Spatio-Temporal Data Warehouses.- Spatiotemporal Interpolation Algorithms.- Spatio-Temporal Selectivity Estimation.- Spatio-Temporal Trajectories.- Specialization and Generalization.- Specificity.- Spectral Clustering.- Split.- Split Transactions.- SQL.- SQL Analytics on Big Data.- SQL Isolation Levels.- SQL-Based Temporal Query Languages.- Stable Distribution.- Stack-based Query Language.- Staged DBMS.- Standard Effectiveness Measures.- Star Index.- Star Schema.- State-based Publish/Subscribe.- Statistical Data Management.- Statistical Disclosure Limitation For Data Access.- Steganography.- Stemming.- Stop-&-go Operator.- Stoplists.- Storage Access Models.- Storage Area Network.- Storage Consolidation.- Storage Devices.- Storage Grid.- Storage Management.- Storage Management Initiative-Specification.- Storage Manager.- Storage Network Architectures.- Storage Networking Industry Association.- Storage of Large Scale Multidimensional Data.- Storage Power Management.- Storage Protection.- Storage Protocols.- Storage Resource Management.- Storage Security.- Storage Virtualization.- Stored Procedure.- Stream Mining.- Stream Models.- Stream Processing.- Stream processing on modern hardware.- Stream Reasoning.- Stream Sampling.- Stream Similarity Mining.- Streaming Analytics.- Streaming Applications.- Stream-Oriented Query Languages and Operators.- Strong Consistency Models for Replicated Data.- Structural Indexing.- Structure Analytics in Social Media.- Structure Weight.- Structured Data in Peer-to-Peer Systems.- Structured Document Retrieval.- Structured Text Retrieval Models.- Subject Spaces.- Subspace Clustering Techniques.- Success at n.- Succinct Constraints.- Suffix Tree.- Summarizability.- Summarization.- Support Vector Machine.- Supporting Transaction Time Databases.- Symbolic Representation.- Symmetric Encryption.- Synopsis Structure.- Synthetic Microdata.- System R (R*) Optimizer.- Table.- Tabular Data.- Taxonomy: Biomedical Health Informatics.- tBench.- Telic Distinction in Temporal Databases.- Telos.- Temporal Access Control.- Temporal Aggregation.- Temporal Algebras.- Temporal Analytics in Social Media.- Temporal Benchmarks.- Temporal Coalescing.- Temporal Compatibility.- Temporal Conceptual Models.- Temporal Constraints.- Temporal Data Mining.- Temporal Data Models.- Temporal Database.- Temporal Datawarehousing.- Temporal Dependencies.- Temporal Element.- Temporal Expression.- Temporal Generalization.- Temporal Granularity.- Temporal Homogeneity.- Temporal Indeterminacy.- Temporal Integrity Constraints.- Temporal Joins.- Temporal Logic in Database Query Languages.- Temporal Logical Models.- Temporal Object-Oriented Databases.- Temporal Periodicity.- Temporal Projection.- Temporal PSM.- Temporal Query Languages.- Temporal Query Processing.- Temporal Relational Calculus.- Temporal Specialization.- Temporal Strata.- Temporal Support in the SQL Standard.- Temporal Vacuuming.- Temporal Visual Languages.- Temporal XML.- Term Proximity.- Term Statistics for Structured Text Retrieval.- Term Weighting.- Test Collection.- Text Analytics.- Text Analytics in Social Media.- Text Categorization.- Text Clustering.- Text Compression.- Text Generation.- Text Index Compression.- Text Indexing and Retrieval.- Text Indexing Techniques.- Text Mining.- Text Mining of Biological Resources.- Text Representation.- Text Segmentation.- Text Semantic Representation.- Text Stream Processing.- Text Streaming Model.- Text Summarization.- Text Visualization.- TF*IDF.- Thematic Map.- Third Normal Form.- Three-Dimensional GIS and Geological Applications.- Three-Phase Commit.- Tight Coupling.- Time Aggregated Graphs.- Time and Information Retrieval.- Time Domain.- Time in Philosophical Logic.- Time Instant.- Time Interval.- Time Period.- Time Series Query.- Time Span.- Time-Line Clock.- Timeslice Operator.- Topic Detection and Tracking.- Topic Maps.- Topic-based Publish/Subscribe.- Top-k Queries.- Top-K Selection Queries on Multimedia Datasets.- Topological Data Models.- Topological Relationships.- Trajectory.- Transaction.- Transaction Chopping.- Transaction Management.- Transaction Manager.- Transaction Models - the Read/Write Approach.- Transaction Time.- Transactional Middleware.- Transactional Processes.- Transactional Stream Processing.- Transaction-Time Indexing.- Tree-based Indexing.- Treemaps.- Triangular Norms.- Triangulated Irregular Network.- Trie.- Trip Planning Queries.- Trust and Reputation in Peer-to-Peer Systems.- Trust in Blogosphere.- Trusted Hardware.- TSQL2.- Tuning Concurrency Control.- Tuple-Generating Dependencies.- Two-Dimensional Shape Retrieval.- Two-Phase Commit.- Two-Phase Commit Protocol.- Two-Phase Locking.- Two-Poisson model.- Type-based Publish/Subscribe.- U-measure.- Uncertain Data Lineage.- Uncertain Data Mining.- Uncertain Data Models.- Uncertain Data Streams.- Uncertain Data Summarization.- Uncertain Graph Data Management.- Uncertain Spatial Data Management.- Uncertain Top-k Queries.- Uncertainty in Events.- Uncertainty Management in Scientific Database Systems.- Unicode.- Unified Modeling Language.- Union.- Unobservability.- Updates and Transactions in Peer-to-Peer Systems.- Updates through Views.- Usability.- User-Defined Time.- Valid Time.- Valid-Time Indexing.- Value Equivalence.- Variable Time Span.- Vector-Space Model.- Vertically Partitioned Data.- Video.- Video Content Analysis.- Video Content Modeling.- Video Content Structure.- Video Metadata.- Video Querying.- Video Representation.- Video Scene and Event Detection.- Video Segmentation.- Video Sequence Indexing.- Video Shot Detection.- Video Summarization.- View Adaptation.- View Definition.- View Maintenance.- View Maintenance Aspects.- View-based Data Integration.- Views.- Virtual Partitioning.- Visual Analytics.- Visual Association Rules.- Visual Classification.- Visual Clustering.- Visual Content Analysis.- Visual Data Mining.- Visual Formalisms.- Visual Interaction.- Visual Interfaces.- Visual Interfaces for Geographic Data.- Visual interfaces for streaming data.- Visual Metaphor.- Visual On-Line Analytical Processing (OLAP).- Visual Perception.- Visual Query Language.- Visual Representation.- Visualization for Information Retrieval.- Visualization Pipeline.- Visualizing Categorical Data.- Visualizing Clustering Results.- Visualizing Hierarchical Data.- Visualizing Network Data.- Visualizing Quantitative Data.- Volume.- Voronoi Diagrams.- W3C.- WAN Data Replication.- Wavelets on Streams.- Weak Consistency Models for Replicated Data.- Weak Equivalence.- Web 2.0/3.0.- Web Advertising.- Web Characteristics and Evolution.- Web Crawler Architecture.- Web Data Extraction System.- Web ETL.- Web Harvesting.- Web Information Extraction.- WEB Information Retrieval Models.- Web Mashups.- Web Page Quality Metrics.- Web Question Answering.- Web Search Query Rewriting.- Web Search Relevance Feedback.- Web Search Relevance Ranking.- Web Search Result Caching and Prefetching.- Web Search Result De-duplication and Clustering.- Web Services.- Web Services and the Semantic Web for Life Science Data.- Web Spam Detection.- Web Transactions.- Web Views.- What-If Analysis.- WIMP Interfaces.- Window operator in RDBMS.- Window-based Query Processing.- Windows.- Workflow Constructs.- Workflow Evolution.- Workflow Join.- Workflow Management.- Workflow Management and Workflow Management System.- Workflow Management Coalition.- Workflow Model.- Workflow Model Analysis.- Workflow Patterns.- Workflow Schema.- Workflow Transactions.- Wrapper Induction.- Wrapper Maintenance.- Wrapper Stability.- Write Once Read Many.- XML.- XML Access Control.- XML Attribute.- XML Benchmarks.- XML Compression.- XML Document.- XML Element.- XML Indexing.- XML Information Integration.- XML Integrity Constraints.- XML Metadata Interchange.- XML Metadata Interchange Specification (XMI).- XML Parsing, SAX/DOM.- XML Process Definition Language.- XML Programming.- XML Publish/Subscribe.- XML Publishing.- XML Retrieval.- XML Schema.- XML Selectivity Estimation.- XML Storage.- XML Stream Processing.- XML Tree Pattern, XML Twig Query.- XML Tuple Algebra.- XML Typechecking.- XML Types.- XML Updates.- XML Views.- XPath/XQuery.- XQuery Full-Text.- XQuery Processors.- XSL/XSLT.- Zero-One Laws.- Zooming Techniques.- α-nDCG.-

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Book SynopsisGain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data proTable of ContentsPart I. Getting Started1. The Problem with Data Science2. Data StrategyPart II. Toward DataOps3. Lean Thinking4. Agile Collaboration5. Build Feedback and MeasurementPart III. Further Steps6. Building Trust7. DevOps for DataOps8. Organizing for DataOpsPart IV. The Self-Service Organization9. DataOps Technology10. The DataOps Factory

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Book SynopsisBeginning-Intermediate user levelTable of ContentsChapter 1: Introducing Power BI Chapter 2: Importing Data into Power BI Desktop Chapter 3: Data Munging with Power Query Chapter 4: Creating the Data Model Chapter 5: Creating Calculations with DAX Chapter 6: Creating Measures with DAX Chapter 7: Incorporating Time Intelligence Chapter 8: Creating Reports with Power BI Desktop Chapter 9: Publishing Reports and Creating Dashboards in the Power BI Portal Chapter 10: Introducing Power Pivot in Excel Chapter 11: Data Analysis with Pivot Tables and Charts Chapter 12: Creating a Complete Solution Chapter 13: Advanced Topics in Power Query Chapter 14: Advanced Topics in Power BI DesktopChapter 15: Advanced Topics in Power BI Data Modeling

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Book Synopsis Deliver eye-catching and insightful business intelligence with Microsoft Power BI Desktop. This new edition has been updated to cover all the latest features of Microsoft''s continually evolving visualization product. New in this edition is help with storytelling-adapted to PCs, tablets, and smartphones-and the building of a data narrative. You will find coverage of templates and JSON style sheets, data model annotations, and the use of composite data sources. Also provided is an introduction to incorporating Python visuals and the much awaited Decomposition Tree visual. Pro Power BI Desktop shows you how to use source data to produce stunning dashboards and compelling reports that you mold into a data narrative to seize your audience''s attention. Slice and dice the data with remarkable ease and then add metrics and KPIs to project the insights that create your competitive advantage. Convert raw data into clear, accurTable of Contents1. Discovering and Loading Data with Power BI Desktop2. Discovering and Loading File-Based Data with Power BI Desktop3. Discovering and Loading File-Based Data with Power BI Desktop4. DirectQuery and Connect Live5. Loading Data from the Web and the Cloud6. Loading Data from Other Data Sources7. Structuring Imported Data8. Data Transformation and Cleansing9. Restructuring Data10. Complex Data Loads11. Organizing, Managing, and Parameterizing Queries12. The M Language13. Creating a Data Model14. Table Visuals15. Matrix and Card Visuals16. Charts in Power BI Desktop17. Formatting Charts in Power BI Desktop18. Other Types of Visuals19. Third-Party Visuals20. Maps in Power BI Desktop21. Filtering Data22. Using Slicers23. Enhancing Dashboards24. Advanced Dashboarding Techniques25. Appendix A: Sample Data

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Book SynopsisBuild a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fastfast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and AzTable of Contents1. The Rise of the Modern Data Warehouse2. The SQL Engine3. The Integration Engine4. The Ingestion Architecture5. The Role of the Data Lake6. The Role of the Data Contract7. Logging, Auditing, and Resilience8. Using Scripting & Automation9. Beyond the Modern Data Warehouse

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Book SynopsisImplement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You'll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. What You Will Learn Program in Java using a high-performance numerical library Learn the mathematics for a wide range of numerical computing algorithms Trade Review“The book is primarily a user’s guide to the NM DEV commercial software library … .” (Anthony J. Duben, Computing Reviews, December 6, 2022)Table of ContentsTable of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Java?..............................................................................................................61.1. Java in 2020.....................................................................................................61.2. Java vs. C++....................................................................................................61.3. Java vs. Python................................................................................................61.4. Java in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................

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Book SynopsisLearn the fundamentals of SAP Enterprise Project and Portfolio management Project Systems (PS), Portfolio and Project Management (PPM) and Commercial Project Management (CPM) and their integration with other SAP modules. This book covers various business scenarios from different industries including the public sector, engineering and construction, professional services, telecom, mining, chemical, and pharmaceutical.Author Joseph Alexander Soosaimuthu will help you understand common business challenges and pain areas faced in portfolio, program and project management, and will provide suitable recommendations to overcome these challenges. This book not only suggests solutions within SAP, but also provides workarounds or integrations with third-party tools based on various Industry-specific business requirements.SAP Portfolio and Project Management addresses commonly asked questions regarding SAP EPPM implementation and deployment, and conveys a framework to facilTable of ContentsChapter 1: Project, Program and Portfolio Management - FundamentalsChapter Goal: To familiarise project, program and portfolio management structures, which subsequent chapters are based on. This chapter will act as building block for further concepts discussed in this book. Sub -Topics 1. Enterprise and Organisation Structure 2. Project Work Breakdown Structure 3. Portfolio and Program Structure 4. Synchronisation of Project, Program and Portfolio Structures 5. Prioritisation Framework Chapter 2: Project Life Cycle – Concept to Closure Chapter Goal: This chapter discusses in detail the various functionalities that will be used during the lifecycle of the project. Sub - Topics 1. Project Planning, Forecasting and Budgeting 2. Project Variation Management 3. Project Commentary 4. Project Issue, Risk and Action item Registers 5. Project Procurement 6. Project Resourcing 7. Project Billing 8. Project Capitalisation 9. Project Closure Chapter 3: Integration Chapter Goal: This chapter will cover critical integration touch points with 3rd party application and also other modules within SAP. Sub - Topics: 1. Detailed level planning of dates and schedules planning with integration to procurement and resourcing. 2. Integration with Schedule Management Applications such as MS project and Oracle Primavera 3. Integration with estimation and costing applications. 4. Integration with Forecasting Application or Excel Integration.Chapter 4: Industry Best Practise and RecommendationChapter Goal: The goal of this chapter is to provide the target audience with insight on business challenges faced during the implementation of Industry best practise and to discuss various solution options with recommendations. Sub - Topics: 1. Industry Best Practise 2. Business Challenges 3. Solution Options 4. Recommendation 5. Commonly asked questions 6. Standard RICEFW List by Industry 7. Standard Functionality List by Industry. Chapter 5: ReportingChapter Goal: This chapter covers reporting related to project, program and portfolio management. It also covers usage of standard ECC and BW Reports/Contents. Sub - Topics: 1. Operational Reporting 2. Month End Reporting 3. Strategic Reporting 4. Long Term Trend Analysis

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