Databases / Data management Books

Book SynopsisEnterprises, as well as individuals, are racing to reap the benefits of AI. However, in most cases, they are doing so without understanding the technology or its implications and risks, which can be significant. Artificial Intelligence: A Guide for Everyone is a step in addressing that gap by providing information that readers can easily understand at every level. This book aims to provide useful information to those planning, developing, or using AI, which has the potential to transform industries and shape the future. Whether you are stepping into the world of AI for the first time or are a seasoned professional seeking deeper insights, this comprehensive guide ensures that both beginners and experienced individuals find value within its pages. Artificial Intelligence: A Guide for Everyone encompasses theoretical as well as practical aspects of AI across various industries and applications. It demystifies AI by explaining, in a language that non-techies can follow, its history, d

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Book SynopsisHistorical and Conceptual Foundations.- Evolving Libraries From Ancient Scrolls to Digital Archives.- The Evolving Role of Libraries From Repositories to Dynamic Hubs.- Algorithmic Innovations Pioneering the Future of Library Services.- Digital Transformations and Emerging Technologies.- Digital Transformation Reshaping Access and Engagement.- Future Vision Libraries as Digital Era Beacons.- Data Driven Library Management From Data to Insights.- Emerging Technologies in Smart Digital Libraries.- AI Driven Innovation in Smart Digital Libraries Empowering Users and Enhancing Services.- User Experience and Engagement in Smart Digital Libraries.- Knowledge Graphs in Smart Digital Libraries.- AI Powered Smart Digital Libraries.

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Book SynopsisIntelligent image analysis and multimedia technology.- information network and security.- digital communication and information systems.- and design and implementation of modern information management systems.

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Book Synopsis.- Effects of Imputation Techniques on Predictive Performance of Supervised Machine Learning Algorithms: Empirical Insights from Health Data Classification..- Predicting Air Quality in an Urban African City Using Four Comparative Novel Time Series Models..- Obesity Classification Using Weighted Hard and Soft Voting Ensemble Machine Learning Classifiers..- Predictive Modeling for Disease Diagnosis Using Calibrated Algorithms: A Comparative Study..- Predicting Precipitation Dynamics in Africa Using Deep Learning Models..- Enhancing Predictive Performance through Optimized Ensemble Stacking for Imbalanced Classification Problems..- A Comparative Exploration of SHAP and LIME for Enhancing the Interpretability of Machine Learning Models in BMI Classification..- Decision Tree Planning Strategies for Predicting Obesity..- Clustering Multiple Time Series with SSA..- Spine-Based Calibration for Classification Algorithms: An Experimental Comparison of Various Imbalanced Ratios..- Exploring the Applicability of Advanced Exponential Smoothing and NN Models for Climate Time Series Forecasting: Insights and Changepoint Prediction in the Brazilian Context..- A Comprehensive Forecasting Experiment on Temperature Trends Across Thirty-Two American Countries..- A Comparative Analysis of Sampling Methods for Imbalanced Data Classification in Machine Learning Health Applications..- Comparative Analysis of MCC, F1-Score, and Balanced Accuracy Metrics for Imbalanced Health Data Classification..- Basics of R- Shiny for developing Interactive Visualizations.

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Book Synopsis1. Introduction.- 2. Demand Forecasting.- 3. Inventory Management.- 4. Distribution Management.- 5. Capacity Planning.- 6. Optimization Theory.- 7. Special Optimization Problems.- 8. Workforce Planning And Scheduling.- 9. Heuristic Algorithms.- 10. Queuing Theory.- 11. Simulation.

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Book SynopsisStudy on Extracting Keywords that Reveal the Value of Research Data Through Comparisons between Academic and Data Paper.- Similar Experimental Flow Search with Operation Grouping for Chemical Material Development.- BP-Pearson Residual for Detecting Outliers in Beta Regression Model.- Reliability Calculation for Captured Images in Mobile Observation for Natural Environments.- Characteristic Analysis and Attack Group Identification of Phishing Sites Targeting Japan.- Evaluation of the Trust Algorithm to Prevent Illegal Information Flow in the Trust Zone Model.- Structuring Clinical Image Reports: Attempt to Implement a Local-Large Language Model.- V2X Communication based on Cellular Multi-Links Method for Autonomous Driving.- An Emotion Classification System Considering Analytic Hierarchy Process.- A Web-Based Collaborative Virtual Reality Class for Gamified Learning of Chemical Molecules.- A Transformer Based Behavioral Control Method for Quadruped Robot.

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Book SynopsisIntroduction.- Bring Your Own Device.- Theoretical Framework and Hypotheses Development.- Research Methodology.- Analysis, Results and Discussion.- Conclusion and Future Work.

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Book SynopsisScalable Blockchain and Robotics Technology for a Patient-Centric Next Generation Medical System.- Face Mask Detection using Multimedia Data and Deep Learning Techniques.- Digital Twin Robots for Securing IoT Based Healthcare 4.0 Application: Key Technologies, Integration Trends and Recent Advancement.- Evolution of FinFET SRAM Cells for Smart Robotics: A Comprehensive Review.- Robot Path Planning Using Triangular Inequality.- Smart Factory Architecture, Infrastructure and Design of Manufacturing Systems using Robotics.

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Book SynopsisEconomic Method for Sensing Data Offloading in the Metaverse.- Securing Maritime Critical Infrastructure: A Multi-Domain AODV-based Communication Protocol for UxVs.- End-to-End MACsec Encryption in a Two-Data Centre Network over Routed WAN.- A New Broadcasting Scheme Based on Reinforcement Learning-Driven Adaptive Fuzzy Logic in Mobile Ad Hoc Networks.- Mobile Traffic Prediction Method Based on Federated Proximal Algorithm.- Reinforcement Learning Based Trajectory Optimization for MEC-UAV.- Deep Reinforcement Learning Based Resource Allocation in Ultra Reliable 5G Core Networks.

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Book SynopsisFault diagnosis method for lithium-ion batteries in electric vehicles based on generalized dimensionless indicator and adaptive threshold.- Improved Subject and Purpose-Oriented Serializability with Multi-Version Concurrency Control.- AI4PM: Distributed and Intelligent Programmable Matter.- Racist Aesthetics in Digital Extremist Communities.- Causality-Based Approach in Retail for Finer Product Clustering.

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Book SynopsisWhen Colorimetry meets the Cloud: IoT-enabled Health Monitoring at Home via Highly-specific Gas Sensing.- Exploring NLP Techniques for Code Smell Detection: A Comparative Study.- AI-Based Noninvasive Pain Detection in Alzheimer’s Patients Using Facial Micro-Expression Analysis.- Cross-Model Federated Learning-based Network Traffic Classification.- Collaborative Warehouse Order Fulfillment via Goal-directed Curiosity and Hierarchical Reinforcement Learning.- The Value of Complaints: Churn Prediction in a Major Residential Internet Service Provider Using Textual Data.- TrPrNet: Early Parkinson Detection Network Using Marker-less Gait Analysis.- Enhanced Lung Cancer Detection Using Vision Transformer for Features Extraction and SVM Classification.

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Book SynopsisHoney-Proxy: Revealing Malicious Activities via Residential Proxies.- Privacy-Preserving News Recommendation Over Homomorphic Encryption.- Cooperative Trust Based Detection Mechanism for Fake Objects in Collective Perception Messages.- Weed Detection in Potato Fields: A Comparative Study of YOLOv8 and YOLOv9 with UAV Imagery.- Evaluation of Feeding Robots' Efficiency for Pig Fattening.- The Potential of Learning Analytics in Higher Education: A Student's Perspective.

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Book SynopsisPredicting the Employment Rate of Students after Graduation using Machine Learning Methods.- A Student Performance Prediction Model using Machine Learning Models in Mmultimodal Learning Analytics.- Predicting Employee Attrition Using Machine Learning: A Comparative Analysis of Traditional Models and Neural Networks.- Leveraging PubMed Abstracts for Identifying COVID19 Treatment Modalities.- Distinguishing AI Generated Text from Human-Written Text Using Machine Learning.

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Book SynopsisIntroduction.- Statistics of Six Sigma for Industrial Engineers.- Defining the Six-Sigma Project.- Measure.- Analyze.- Improve.- Control.- Epilogue.

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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 SynopsisTransactions are a concept related to the logical database as seen from the perspective of database application programmers: a transaction is a sequence of database actions that is to be executed as an atomic unit of work. The processing of transactions on databases is a well- established area with many of its foundations having already been laid in the late 1970s and early 1980s.The unique feature of this textbook is that it bridges the gap between the theory of transactions on the logical database and the implementation of the related actions on the underlying physical database. The authors relate the logical database, which is composed of a dynamically changing set of data items with unique keys, and the underlying physical database with a set of fixed-size data and index pages on disk. Their treatment of transaction processing builds on the “do-redo-undo” recovery paradigm, and all methods and algorithms presented are carefully designed to be compatible with this paradigm as well as with write-ahead logging, steal-and-no-force buffering, and fine-grained concurrency control.Chapters 1 to 6 address the basics needed to fully appreciate transaction processing on a centralized database system within the context of our transaction model, covering topics like ACID properties, database integrity, buffering, rollbacks, isolation, and the interplay of logical locks and physical latches. Chapters 7 and 8 present advanced features including deadlock-free algorithms for reading, inserting and deleting tuples, while the remaining chapters cover additional advanced topics extending on the preceding foundational chapters, including multi-granular locking, bulk actions, versioning, distributed updates, and write-intensive transactions.This book is primarily intended as a text for advanced undergraduate or graduate courses on database management in general or transaction processing in particular.Table of Contents1 Transactions on the Logical Database.- 2 Operations on the Physical Database.- 3 Logging and Buffering.- 4 Transaction Rollback and Restart Recovery.- 5 Transactional Isolation.- 6 Lock-Based Concurrency Control.- 7 B-Tree Traversals.- 8 B-Tree Structure Modifications.- 9 Advanced Locking Protocols.- 10 Bulk Operations on B-Trees.- 11 Online Index Construction and Maintenance.- 12 Concurrency Control by Versioning.- 13 Distributed Transactions.- 14 Transactions in Page-Server Systems.- 15 Processing of Write-Intensive Transactions.

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Book SynopsisThis guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques.This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets Table of Contents1. Preface 2. Introduction a. The Role of Data Wrangling i. Introduction to R 1. Open Source 2. Flexibility 3. Community ii. R Basics 1. Assignment & Evaluation 2. Vectorization 3. Getting help 4. Workspace 5. Working with packages 6. Style guide 3. Working with Different Types of Data in R a. Dealing with Numbers i. Integer vs. Double ii. Generating sequence of non-random numbers iii. Generating sequence of random numbers iv. Setting the seed for reproducible random numbers v. Comparing numeric values vi. Rounding numbers b. Dealing with Character Strings i. Character string basics ii. String manipulation with base R iii. String manipulation with stringr iv. Set operatons for character strings c. Dealing with Regular Expressions i. Regex Syntax ii. Regex Functions iii. Additional resources d. Dealing with Factors i. Creating, converting & inspecting factors ii. Ordering levels iii. Revalue levels iv. Dropping levels e. Dealing with Dates i. Getting current date & time ii. Converting strings to dates iii. Extract & manipulate parts of dates iv. Creating date sequences v. Calculations with dates vi. Dealing with time zones & daylight savings vii. Additional resources <4. Managing Data Structures in R a. Data Structure Basics i. Identifying the Structure ii. Attributes b. Managing Vectors i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting c. Managing Lists i. Creating iii. Adding attributes iv. Subsetting d. Managing Matrices i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting e. Managing Data Frames i. Creating ii. Adding on to iii. Adding attributes iv. Subsetting f. Dealing with Missing Values i. Testing for missing values ii. Recoding missing values iii. Excluding missing values 5. Importing, Scraping, and Exporting Data with R a. Importing Data i. Reading data from text files ii. Reading data from Excel files iii. Load data from saved R object file iv. Additional resources b. Scraping Data i. Importing tabular and Excel files stored online ii. Scraping HTML text iii. Scraping HTML table data iv. Working with APIs v. Additional Resources c. Exporting Data i. Writing data to text files ii. Writing data to Excel files iii. Saving data as an R object file iv. Additional resources 6. Creating Efficient & Readable Code in R a. Functions i. Function Components ii. Arguments iii. Scoping Rules iv. Lazy Evaluation v. Returning Multiple Outputs from a Function vi. Dealing with Invalid Parameters vii. Saving and Sourcing Functions viii. Additional Resources b. Loop Control Statements i. Basic control statements (i.e. if, for, while, etc.) ii. Apply family iii. Other useful “loop-like” functions iv. Additional Resources c. Simplify Your Code with %>% i. Pipe (%>%) Operator ii. Additional Functions iii. Additional Pipe Operators iv. Additional Resources 7. Shaping & Transforming Your Data with R a. Reshaping Your Data with tidyr i. Making wide data long ii. Making long data wide iii. Splitting a single column into multiple columns iv. Combining multiple columns into a single column v. Additional tidyr functions vi. Sequencing your tidyr operations vii. Additional resources b. Transforming Your Data with dplyr i. Selecting variables of interest ii. Filtering rows iii. Grouping data by categorical variables iv. Performing summary statistics on variables v. Arranging variables by value vi. Joining datasets vii. Creating new variables viii. Additional resources

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Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.Table of ContentsQuerying the Web of Data.- Semantic Web Solutions for Large-Scale Biomedical Data Analytics.- Scientometrics.- RDF Stream Processing.- Emotions, Modality, Sentiment Analysis and the Semantic Web.- Applications of Semantic Web Technologies in Robotics.- Linked Data and Distributed Ledgers.- Linked Data Quality.- Semantic Deep Learning.- Humanities in the Semantic Web.

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Book SynopsisIn a world of rapid and unpredictable change, the problem with strategic planning is that if you follow your plan through to the end, you will get exactly what you used to want. What you need is a framework for planning and implementing a strategy that is agile enough to adapt to a dynamic environment but focused enough to deliver. That framework is the Dynamic Balanced Scorecard. The original Balanced Scorecard system has proven the most popular, successful and enduring framework for strategy execution over the last 25 years. Comprising a Strategy Map and a scorecard of KPIs, targets and initiatives, the framework helped organizations distil a strategy into actionable components and measure progress towards a strategic vision, while also implementing and monitoring the actions that drove change. However, for all its success, the Balanced Scorecard system now needs to evolve for the digital age. Until now, building the system, rolling it out enterprise-wide and adapting it to external changes has been a lengthy process. While the fundamental principles of the system are still sound and relevant, it needs to become nimbler and more responsive. The book provides a step-by-step guide to agile strategy management: from formulation to implementation to learning and adapting. For each of the steps, the book explains how Dynamic Balanced Scorecards, fit for the digital age, are built and deployed.Table of Contents1 Digital Era Strategy Management: From planning to dynamic decision making 2 From Industrial to Digital-Era-based Strategies3 Agile Strategy Setting4 Strategy Mapping in Disruptive Times5 How to Build an Agile and Adaptive Balanced Scorecard6 Driving Rapid Enterprise Alignment7 Aligning the Financial and Operational Drivers of Strategic Success8 Developing Strategy-aligned Project Management Capabilities9 Unleashing the Power of Analytics for Strategic Learning and Adapting10 How to Ensure a Strategy-Aligned Leadership11 How to Ensure a Strategy-Aligned Culture12 Ensuring Employee Sense of Purpose in the Digital Era13 Further Developments, Driving Sustainable Value through Collaborative Strategy Maps and Scorecards14 Conclusion and 25 Key Strategic Questions

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Book SynopsisThis book brings order to the chaotic and rapidly evolving world of influencer marketing by providing readers with much needed context, frameworks, and best practices. Written for busy marketing professionals working in both domestic and international markets, it addresses these topics in a highly actionable and engaging manner. Digital Influence covers everything from how to identify the right influencers and determine “level of influence” to collaborating with influencers and measuring ROI. It turns out, it’s not all about paying online celebrities outrageous sums to post sponsored content. Backaler also provides much-needed context for why influencer marketing is flourishing today, and perspective into what a more technologically-enabled, globally-connected future will look like. Ultimately, people want to learn from trusted peers, not faceless companies. Better than any corporate marketing function, influencers understand how to make best use of social media platforms and tailor content for their respective cultures to engage consumers in their home markets. Business leaders should arm themselves with Backaler's book to ensure they're not left behind.Trade Review“This is a much-needed book, as many strictly academic or textbooks still have a weakness around all things digital and the so-called new media. … Digital Influence is a highly actionable book with well-researched studies and content. An in-depth guidance towards achieving meaningful results and focused on the understanding of influencers is a core goal that is achieved. This book is an easy read but with insightful content.” (Bradley Wilson, Information, Communication & Society, Vol. 22 (10), 2019)“The rich insights in this book will interest more than the digital marketing crowd. Using case studies, frameworks, and industry figures, the author presents a wealth of details in a relevant and effective manner. … Summing Up: Recommended. All readers.” (S. M. Mohammed, Choice, Vol. 56 (9), May, 2019)Table of ContentsChapter 1: IntroductionChapter 2: Then vs. Now: Influencer Marketing (Re-Defined)Chapter 3: Levels of Influence: Key Characteristics of Modern-Day InfluencersChapter 4: A Global Phenomenon: The Rise of Influencers Around the WorldChapter 5: Business to Consumer (B2C) Influencer Marketing LandscapeChapter 6: Business to Business (B2B) Influencer Marketing LandscapeChapter 7: Discover Influencers: Finding the Perfect MatchChapter 8: Engage Influencers: Developing an Effective Outreach StrategyChapter 9: Collaborate with Influencers: Potential Paths to TakeChapter 10: Know the Risks: The Dark Side of Influencer CollaborationChapter 11: Measure Success: What’s the Return on Investment?Chapter 12: Case Studies: Influencer Marketing Best Practices from Around the WorldChapter 13: What's Coming: The Future of Influencer Marketing

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Book SynopsisThis book is about innovation, big data, and data science seen from a business perspective. Big data is a buzzword nowadays, and there is a growing necessity within practitioners to understand better the phenomenon, starting from a clear stated definition. This book aims to be a starting reading for executives who want (and need) to keep the pace with the technological breakthrough introduced by new analytical techniques and piles of data. Common myths about big data will be explained, and a series of different strategic approaches will be provided. By browsing the book, it will be possible to learn how to implement a big data strategy and how to use a maturity framework to monitor the progress of the data science team, as well as how to move forward from one stage to the next. Crucial challenges related to big data will be discussed, where some of them are more general - such as ethics, privacy, and ownership – while others concern more specific business situations (e.g., initial public offering, growth strategies, etc.). The important matter of selecting the right skills and people for an effective team will be extensively explained, and practical ways to recognize them and understanding their personalities will be provided. Finally, few relevant technological future trends will be acknowledged (i.e., IoT, Artificial intelligence, blockchain, etc.), especially for their close relation with the increasing amount of data and our ability to analyse them faster and more effectively.Trade Review“The book presents a new viewpoint to approach the big data concept that tries to distance itself from previous approaches. It introduces a consistent framework to handle and apply data strategies. … The book would be interesting reading for big data analysts or company managers because it discloses some important facets of this new technological challenge, although some subjects are quite hastily explained.” (Svetlana Segarceanu, Computing Reviews, April, 2017)Table of ContentsIntroduction.- What Data Science Means to the Business.- Key Data Challenges to Strategic Business Decisions.- A Chimera Called Data Scientist: Why they don’t Exist (but they will in the Future).- Future Data Trends.- Where are we Going? The Path Toward an Artificial Intelligence.- Conclusions.

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Book SynopsisThis book explains the main problems related to digital preservation using examples based on a modern version of the well-known Cinderella fairy tale. Digital preservation is the endeavor to protect digital material against loss, corruption, hardware/software technology changes, and changes in the knowledge of the community.Τhe structure of the book is modular, with each chapter consisting of two parts: the episode and the technical background. The episodes narrate the story in chronological order, exactly as in a fairy tale. In addition to the story itself, each episode is related to one or more digital preservation problems, which are discussed in the technical background section of the chapter. To reveal a more general and abstract formulation of these problems, the notion of pattern is used. Each pattern has a name, a summary of the problem, a narrative describing an attempt to solve the problem, an explanation of what could have been done to avoid or alleviate this problem, some lessons learned, and lastly, links to related patterns discussed in other chapters.The book is intended for anyone wanting to understand the problems related to digital preservation, even if they lack the technical background. It explains the technical details at an introductory level, provides references to the main approaches (or solutions) currently available for tackling related problems, and is rounded out by questions and exercises appropriate for computer engineers and scientists. In addition, the book's website, maintained by the authors, presents the contents of Cinderella's “real USB stick,” and includes links to various tools and updates.Trade Review“‘Cinderella’s Stick’ is an excellent book for all readers in research libraries. It provides the right concepts in a very smart and innovative way, and it underlines that the amount of digital information that we alone produce is immense and the challenges of fragility are here to stay.” (Giannis Tsakonas, Liber Quarterly, Vol. 29(1), 2019)Table of Contents1 A Few Words About Digital Preservation And Book Overview.- 2 The Fairy Tale Of Cinderella.- 3 Daphne (A Modern Cinderella).- 4 Reading the Contents of the USB Stick.- 5 First Contact with the Contents of the USB Stick.- 6 The File Poem.html: On Reading Characters.- 7 The File MyPlace.png: On Getting the Provenance of a Digital Object.- 8 The File todo.csv – On Understanding Data Values.- 9 The File destroyAll.exe: On Executing Proprietary Software.- 10 The File Mymusic.class: On Decompiling Software.- 11 The File yyy.java: On Compiling And Running Software.- 12 The File myFriendsBook.war: On Running Web Applications.- 13 The File roulette.BAS: On Running Obsolete Software.- 14 The Folder myExperiment: On Verifying and Reproducing Data.- 15 The File MyContacts.con: On Reading Unknown Digital Resources.- 16 The File SecretMeeting.Txt: On Authenticity Checking.- 17 The Personal Archive Of Robert: On Preservation Planning.- 18 The Meta-Pattern: Toward a Common Umbrella.- 19 How Robert Eventually Found Daphne.- 20 Daphne’s Dream.- 21 Epilogue.

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Book SynopsisDatenbanken sind unverzichtbare Basis fast aller Informationssysteme. Dieses in dritter, erweiterter Auflage vorliegende Buch vermittelt Ihnen das notwendige Grundlagenwissen zu Datenbanksystemen. Dabei werden, nach einem allgemeinen Überblick über die verschiedenen Datenbankmodelle, vor allem der Entwurf und die Implementierung relationaler Datenbanken besprochen. Im Mittelpunkt des praktischen Teils steht die Datenbanksprache SQL, die anhand vieler Beispiele detailliert erläutert wird. Auch auf SQL-spezifische Besonderheiten der verbreiteten Datenbankmanagementsysteme MySQL, Oracle und PostgreSQL wird eingegangen. Mit den zahlreich vorhandenen Übungsaufgaben sowie einer Fallstudie zum Download können Sie Ihren Lernfortschritt überprüfen. Musterlösungen dazu sind vorhanden. Der Inhalt orientiert sich an den Empfehlungen der Gesellschaft für Informatik für das Studienfach Informatik.Table of ContentsEinleitung 21 Teil I: Einführung in Datenbanksysteme 29 Kapitel 1: Wofür Datenbanksysteme gut sind 31 Kapitel 2: Definition und Architektur von Datenbanksystemen 41 Kapitel 3: Überblick über Datenmodelle 51 Kapitel 4: Vorgehensweise bei der Datenbankentwicklung 71 Teil II: Von der Aufgabenstellung zur relationalen Datenbank 89 Kapitel 5: Semantische Modellierung einer Datenbank 91 Kapitel 6: Das relationale Datenbankmodell im Detail 115 Kapitel 7: Entwurf und Implementierung des Datenbankschemas 143 Teil III: Zugriff auf relationale Datenbanken mit SQL 177 Kapitel 8: Die DML-Befehle von SQL 179 Kapitel 9: Der SELECT-Befehl im Detail 191 Teil IV: Die Verwaltung einer relationalen Datenbank 225 Kapitel 10: Benutzersichten 227 Kapitel 11: Datensicherheit und Zugriffsrechte bei Datenbanken 241 Kapitel 12: Datenbankoptimierung 251 Teil V: Objektrelationale Datenbanksysteme 271 Kapitel 13: Objektorientierung und relationale Datenbanksysteme 273 Kapitel 14: Arbeiten mit objektrelationalen Tabellen 285 Teil VI: Grundlagen zur Erstellung von Datenbankanwendungen 297 Kapitel 15: Trigger und Stored Procedures 299 Kapitel 16: Programmieren von Datenbankanwendungen 313 Kapitel 17: Anwendungsbereiche relationaler Datenbanken 329 Kapitel 18: SQL oder nicht SQL 341 Teil VII: Der Top-10-Teil 355 Kapitel 19: Die 10 größten Fehler beim Entwurf relationaler Datenbanken 357 Kapitel 20: Die 10 wichtigsten SQL-Befehle und ihre Varianten 363 Kapitel 21: 10 Fragen bei der Entity-Relationship-Modellierung 369 Lösungen zu den Übungen 375 Literaturverzeichnis 401 Abbildungsverzeichnis 405 Stichwortverzeichnis 411

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Book SynopsisThis book constitutes the refereed proceedings of the 7th International Symposium, Latin American Theoretical Informatics, LATIN 2006, held in March 2006. The 66 revised full papers presented together with seven invited papers were carefully reviewed and selected from 224 submissions. The papers presented are devoted to a broad range of topics in theoretical computer science with a focus on algorithmics and computations related to discrete mathematics as well as on cryptography, data compression and Web applications.Table of ContentsKeynotes.- Algorithmic Challenges in Web Search Engines.- RNA Molecules: Glimpses Through an Algorithmic Lens.- Squares.- Matching Based Augmentations for Approximating Connectivity Problems.- Modelling Errors and Recovery for Communication.- Lossless Data Compression Via Error Correction.- The Power and Weakness of Randomness in Computation.- Regular Contributions.- A New GCD Algorithm for Quadratic Number Rings with Unique Factorization.- On Clusters in Markov Chains.- An Architecture for Provably Secure Computation.- Scoring Matrices That Induce Metrics on Sequences.- Data Structures for Halfplane Proximity Queries and Incremental Voronoi Diagrams.- The Complexity of Diffuse Reflections in a Simple Polygon.- Counting Proportions of Sets: Expressive Power with Almost Order.- Efficient Approximate Dictionary Look-Up for Long Words over Small Alphabets.- Relations Among Notions of Security for Identity Based Encryption Schemes.- Optimally Adaptive Integration of Univariate Lipschitz Functions.- Classical Computability and Fuzzy Turing Machines.- An Optimal Algorithm for the Continuous/Discrete Weighted 2-Center Problem in Trees.- An Algorithm for a Generalized Maximum Subsequence Problem.- Random Bichromatic Matchings.- Eliminating Cycles in the Discrete Torus.- On Behalf of the Seller and Society: Bicriteria Mechanisms for Unit-Demand Auctions.- Pattern Matching Statistics on Correlated Sources.- Robust Model-Checking of Linear-Time Properties in Timed Automata.- The Computational Complexity of the Parallel Knock-Out Problem.- Reconfigurations in Graphs and Grids.- -Varieties, Actions and Wreath Product.- Local Construction of Planar Spanners in Unit Disk Graphs with Irregular Transmission Ranges.- An Efficient Approximation Algorithm for Point Pattern Matching Under Noise.- Oblivious Medians Via Online Bidding.- Efficient Computation of the Relative Entropy of Probabilistic Automata.- A Parallel Algorithm for Finding All Successive Minimal Maximum Subsequences.- De Dictionariis Dynamicis Pauco Spatio Utentibus.- Customized Newspaper Broadcast: Data Broadcast with Dependencies.- On Minimum k-Modal Partitions of Permutations.- Two Birds with One Stone: The Best of Branchwidth and Treewidth with One Algorithm.- Maximizing Throughput in Queueing Networks with Limited Flexibility.- Network Flow Spanners.- Finding All Minimal Infrequent Multi-dimensional Intervals.- Cut Problems in Graphs with a Budget Constraint.- Lower Bounds for Clear Transmissions in Radio Networks.- Asynchronous Behavior of Double-Quiescent Elementary Cellular Automata.- Lower Bounds for Geometric Diameter Problems.- Connected Treewidth and Connected Graph Searching.- A Faster Algorithm for Finding Maximum Independent Sets in Sparse Graphs.- The Committee Decision Problem.- Common Deadline Lazy Bureaucrat Scheduling Revisited.- Approximate Sorting.- Stochastic Covering and Adaptivity.- Algorithms for Modular Counting of Roots of Multivariate Polynomials.- Hardness Amplification Via Space-Efficient Direct Products.- The Online Freeze-Tag Problem.- I/O-Efficient Algorithms on Near-Planar Graphs.- Minimal Split Completions of Graphs.- Design and Analysis of Online Batching Systems.- Competitive Analysis of Scheduling Algorithms for Aggregated Links.- A 4-Approximation Algorithm for Guarding 1.5-Dimensional Terrains.- On Sampling in Higher-Dimensional Peer-to-Peer Systems.- Mobile Agent Rendezvous in a Synchronous Torus.- Randomly Colouring Graphs with Girth Five and Large Maximum Degree.- Packing Dicycle Covers in Planar Graphs with No K 5–e Minor.- Sharp Estimates for the Main Parameters of the Euclid Algorithm.- Position-Restricted Substring Searching.- Rectilinear Approximation of a Set of Points in the Plane.- The Branch-Width of Circular-Arc Graphs.- Minimal Eulerian Circuit in a Labeled Digraph.- Speeding up Approximation Algorithms for NP-Hard Spanning Forest Problems by Multi-objective Optimization.- RISOTTO: Fast Extraction of Motifs with Mismatches.- Minimum Cost Source Location Problems with Flow Requirements.- Exponential Lower Bounds on the Space Complexity of OBDD-Based Graph Algorithms.- Constructions of Approximately Mutually Unbiased Bases.- Improved Exponential-Time Algorithms for Treewidth and Minimum Fill-In.

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Book SynopsisThe workshop on Computability and Complexity in Analysis, CCA 2000, was hosted by the Department of Computer Science of the University of Wales Swansea, September 17{19, 2000. It was the fourth workshop in a successful series of workshops: CCA’95 in Hagen, Germany, CCA’96 in Trier, Germany, and CCA’98 in Brno, Czech Republic. About 40 participants from the countries United Kingdom, Germany, Japan, Italy, Russia, France, Denmark, Greece, and Ireland contributed to the success of this meeting. Altogether, 28 talkswere p- sented in Swansea. These proceedings include 23 papers which represent a cro- section through recent research on computability and complexity in analysis. The workshop succeeded in bringing together people interested in computability and complexity aspects of analysis and in exploring connections with nume- cal methods, physics and, of course, computer science. It was rounded o by a number of talks and papers on exact computer arithmetic and by a competition of v e implemented systems. A report on this competition has been included in these proceedings. We would like to thank the authors for their contributions and the referees for their careful work, and we hope for further inspiring and constructive meetings of the same kind. April 2001 Jens Blanck Vasco Brattka Peter Hertling Organization CCA2000was hosted by the Department of Computer Science of the University of Wales Swansea and took place on September 17{19, 2000.Table of ContentsComputability and Complexity in Analysis.- Effectivity of Regular Spaces.- The Degree of Unsolvability of a Real Number.- A Survey of Exact Arithmetic Implementations.- Standard Representations of Effective Metric Spaces.- Banach-Mazur Computable Functions on Metric Spaces.- A Generic Root Operation for Exact Real Arithmetic.- Effective Contraction Theorem and Its Application.- Polynomially Time Computable Functions over p-Adic Fields.- On the Computational Content of the Krasnoselski and Ishikawa Fixed Point Theorems.- Formalisation of Computability of Operators and Real-Valued Functionals via Domain Theory.- Computing a Required Absolute Precision from a Stream of Linear Fractional Transformations.- ?-Approximable Functions.- Computabilities of Fine-Continuous Functions.- The iRRAM: Exact Arithmetic in C++.- The Uniformity Conjecture.- Admissible Representations of Limit Spaces.- Characterization of the Computable Real Numbers by Means of Primitive Recursive Functions.- Effective Fixed Point Theorem over a Non-Computably Separable Metric Space.- Computational Dimension of Topological Spaces.- Some Properties of the Effective Uniform Topological Space.- On Computable Metric Spaces Tietze-Urysohn Extension Is Computable.- Is the Linear Schrödinger Propagator Turing Computable?.- A Computable Spectral Theorem.- Report on Competition.- Exact Real Arithmetic Systems: Results of Competition.

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Book SynopsisFormanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized. The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).Table of ContentsLearning.- From Theoretical Learnability to Statistical Measures of the Learnable.- ALM: A Methodology for Designing Accurate Linguistic Models for Intelligent Data Analysis.- A “Top-Down and Prune” Induction Scheme for Constrained Decision Committees.- Mining Clusters with Association Rules.- Evolutionary Computation to Search for Strongly Correlated Variables in High-Dimensional Time-Series.- The Biases of Decision Tree Pruning Strategies.- Feature Selection as Retrospective Pruning in Hierarchical Clustering.- Discriminative Power of Input Features in a Fuzzy Model.- Learning Elements of Representations for Redescribing Robot Experiences.- “Seeing“ Objects in Spatial Datasets.- Intelligent Monitoring Method Using Time Varying Binomial Distribution Models for Pseudo-Periodic Communication Traffic.- Visualization.- Monitoring Human Information Processing via Intelligent Data Analysis of EEG Recordings.- Knowledge-Based Visualization to Support Spatial Data Mining.- Probabilistic Topic Maps: Navigating through Large Text Collections.- 3D Grand Tour for Multidimensional Data and Clusters.- Classification and Clustering.- A Decision Tree Algorithm for Ordinal Classification.- Discovering Dynamics Using Bayesian Clustering.- Integrating Declarative Knowledge in Hierarchical Clustering Tasks.- Nonparametric Linear Discriminant Analysis by Recursive Optimization with Random Initialization.- Supervised Classification Problems: How to Be Both Judge and Jury.- Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification.- Exploiting Similarity for Supporting Data Analysis and Problem Solving.- Multiple Prototype Model for Fuzzy Clustering.- A Comparison of Genetic Programming Variants for Data Classification.- Fuzzy Clustering Based on Modified Distance Measures.- Building Classes in Object-Based Languages by Automatic Clustering.- Integration.- Adjusted Estimation for the Combination of Classifiers.- Data-Driven Theory Refinement Using KBDistAl.- Reasoning about Input-Output Modeling of Dynamical Systems.- Undoing Statistical Advice.- A Method for Temporal Knowledge Conversion.- Applications.- Intrusion Detection through Behavioral Data.- Bayesian Neural Network Learning for Prediction in the Australian Dairy Industry.- Exploiting Sample-Data Distributions to Reduce the Cost of Nearest-Neighbor Searches with Kd-Trees.- Pump Failure Detection Using Support Vector Data Descriptions.- Data Mining for the Detection of Turning Points in Financial Time Series.- Computer-Assisted Classification of Legal Abstracts.- Sequential Control Logic Inferring Method from Observed Plant I/O Data.- Evaluating an Eye Screening Test.- Application of Rough Sets Algorithms to Prediction of Aircraft Component Failure.- Media Mining.- Exploiting Structural Information for Text Classification on the WWW.- Multi-agent Web Information Retrieval: Neural Network Based Approach.- Adaptive Information Filtering Algorithms.- A Conceptual Graph Approach for Video Data Representation and Retrieval.

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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 SynopsisThis book constitutes the refereed proceedings of the 13th Algorithms and Data Structures Symposium, WADS 2013, held in London, ON, Canada, August 2013. The Algorithms and Data Structures Symposium - WADS (formerly "Workshop on Algorithms and Data Structures") is intended as a forum for researchers in the area of design and analysis of algorithms and data structures. The 44 revised full papers presented in this volume were carefully reviewed and selected from 139 submissions. The papers present original research on algorithms and data structures in all areas, including bioinformatics, combinatorics, computational geometry, databases, graphics, and parallel and distributed computing.Table of ContentsAlgorithms and data structures in bioinformatics.- Algorithms and data structures in combinatorics.- Algorithms and data structures in computational geometry.- Algorithms and data structures in databases.- Algorithms and data structures in graphics.- Parallel and distributed computing.

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Book SynopsisData matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases.Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today.By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.Trade Review"The book is very well organized and exceptionally well written. Because of the depth, amount, and quality of the material that is covered, I would expect this book to be one of the standard references in future years." William E. Winkler, U.S. Bureau of the Census, Washington, DC, USATable of ContentsPart I Overview.- Introduction.- The Data Matching Process.- Part II Steps of the Data Matching Process.- Data Pre-Processing.- Indexing.- Field and Record Comparison.- Classification.- Evaluation of Matching Quality and Complexity.- Part III Further Topics.- Privacy Aspects of Data Matching.- Further Topics and Research Directions.- Data Matching Systems.

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Book SynopsisRecent achievements in hardware and software development, such as multi-core CPUs and DRAM capacities of multiple terabytes per server, enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of enterprise data. Professor Hasso Plattner and his research group at the Hasso Plattner Institute in Potsdam, Germany, have been investigating and teaching the corresponding concepts and their adoption in the software industry for years.This book is based on an online course that was first launched in autumn 2012 with more than 13,000 enrolled students and marked the successful starting point of the openHPI e-learning platform. The course is mainly designed for students of computer science, software engineering, and IT related subjects, but addresses business experts, software developers, technology experts, and IT analysts alike. Plattner and his group focus on exploring the inner mechanics of a column-oriented dictionary-encoded in-memory database. Covered topics include - amongst others - physical data storage and access, basic database operators, compression mechanisms, and parallel join algorithms. Beyond that, implications for future enterprise applications and their development are discussed. Step by step, readers will understand the radical differences and advantages of the new technology over traditional row-oriented, disk-based databases.In this completely revised 2nd edition, we incorporate the feedback of thousands of course participants on openHPI and take into account latest advancements in hard- and software. Improved figures, explanations, and examples further ease the understanding of the concepts presented. We introduce advanced data management techniques such as transparent aggregate caches and provide new showcases that demonstrate the potential of in-memory databases for two diverse industries: retail and life sciences. Table of ContentsIntroduction.- Part I: The Future of Enterprise Computing.- Part II: Foundations of Database Storage Techniques.- Part III: In-Memory Database Operators.- Part IV: Advanced Database Storage Techniques.- Part V: Principles for Enterprise Application Development.- Self Test Solutions.

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