Databases / Data management Books

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Book SynopsisThis book is for executives, practitioners, and project managers who are tasked with the movement of data from old systems to a new repository. It is designed as a practical guide and uses a series of steps developed in real-life situations that will get you from an empty new system to one that is populated, working and backed by the user population. This new edition is updated to take account of changes in technology and the maturing of the market for data migration services, with two brand new chapters. It guarantees to get the dirty old data out of your legacy systems and transform it into clean new data for your new system.Trade ReviewFor any practitioner faced with the challenge of delivering a successful data migration, this book is an absolute necessity. -- Dylan Jones * Founder, Data Migration Pro *The book Practical Data Migration by Johny Morris is essential reading for any practitioner facing a challenging data migration project. The book covers the most important theoretical aspects but is at the same time very practice-oriented and pragmatic. PDMv3 strategy is the basis for being able to complete projects in time and to budget. -- Dr Andreas Martens * Executive Director, qurix Technology GmbH *PDMv3 provides a valuable resource for project managers or IT practitioners seeking structured and consistent approaches to data migration, whether with regards to strategy, governance, data analysis, migration design, or testing. PDMv3 guidance, advice, and methodology, where followed, enhances the professionalism of anyone undertaking a data migration role. -- Dr David Hannell MBCS CITP * Writer, Research Analyst, Project Manager *Throughout this book, Johny Morris provides practical experience-based guidance to both executive and practitioner on how to overcome the challenges of delivering a successful data migration. This edition updated to cover Waterfall, Agile and blended approaches brings this work fully up to date. An absolute ‘must read’ for any individual or team before starting or rescuing a data migration. -- Ian Chapman MBCS CITP * Enterprise Data Architect, Expertechnix Ltd & Committee Member, DAMA UK *Johny Morris continues to provide clear and honest guidance to anyone who finds themselves having to struggle with the complexities of large-scale data migration. The PDM approach has proved invaluable in guiding the data migration team at Guy’s & St Thomas’ NHS Foundation Trust. -- Dr Tito Castillo MBCS CITP * Founder, Agile Health Informatics Ltd & Associate Vice Chair (Standards), BCS Health & Care Executive *Digital transformation are impacting all organisation and a big part of the challenge is migrating from your current IT platforms and services. This easy to read book is the must have ‘how to’ guide on data migration for executives, data professionals and practitioners. It provides an accessible and easy to read guide that covers the many forms and dimensions to data migration programmes, from someone who has spent years on the sharp side of PDM. -- Jason B Perkins * Head of Data & Analytics Architecture, BT *This is one of very few books I’ve read that I have thoroughly enjoyed from start to finish. Johny Morris’ style of writing is one I really adapt well too, a great mix of theory and experience with highlighted summary, risks and advice passages throughout. Over many years I have stressed the importance that PDM activity is owned and driven by the business, something that is overlooked when technology solutions are designed, tested and implemented. A ‘must have’ book for any BA or IT professional’s library! -- Mark Thompson * Lead Business Analyst (Banking Division), Close Brothers *My first comments on reading Practical Data Migration were ‘AMEN’ and ‘YES’. The key differentiator of PDM from many other methodologies is the word ‘Practical’. Theory has a simple goal of migrating data from a tired old system to a shiny new one. Practical covers ‘user enhancements’. -- Sean Barker FBCS CEng * Retired (previously with BAE Systems) *This book is THE authoritative guide to data migration principles and practices and an essential ‘go-to’ guide for anyone in business or IT involved in data migration projects. It is refreshingly jargon-free, and provides a systematic and proven methodology to address both the technical and cultural challenges of data migration. -- Nigel Turner * Principal Consultant, Global Data Strategy Ltd *Whether this is your first or twenty-first data migration, PDMv3 will gives practical and experienced advice to improve the success of your data migration project. Johny Morris highlights useful tips, real-life experience and golden rules. An essential read for any manager or practitioner. -- Mark Dodd CITP * Think Smart Limited *Data migrations are infrequent, and expertise in this challenging but important field is rare. This new edition of a well-established book, updated to reflect technical advances, is indispensable not only for members of project teams working on data migration but also for their managers. -- Mike Andersson MBCS * Director, Andstrom Consulting & Vice Chair (Standards), BCS Health and Care Executive *PDMv3 is the perfect guide to data migration operations! Its comprehensive coverage of data migration procedures and themes is incredibly structured and fluid. It is clear, concise, and deliberate in its delivery of what you need to know to move your data from one system to another successfully. -- Kudzai Muchenje * Regional Operations Analyst, African Development Bank Group *Table of ContentsINTRODUCTION SECTION 1: EXECUTIVE OVERVIEW1 Data migration: what's all the fuss2 Golden rules and super smart tasks3 PDMv3 overview 4 Creating a data migration strategy SECTION 2: TOOLS AND TECHNIQUES5 Project initiation6 Key data stakeholder management and demilitarised zone7 Landscape analysis8 Business transformation plan9 Data quality rules10 Gap analysis and mapping11 Migration design and execution12 Legacy decommissioning13 Waterfall versus Agile SECTION 3: FAILING DATA MIGRATION PROJECTS 14 Rescuing failing data migration projects APPENDICES A1 Data migration strategy checklistA2 Fields on a DQR document/formA3 Mapping exampleA4 PDMv3 process flow

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Book SynopsisData is a valuable corporate asset and its effective management is vital to an organisation’s success and survival. With this book you will learn to master the key principles of data management and use them to implement best practices in your organization. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This fully updated new edition provides new chapters on the most important data topics such as big data, artificial intelligence, linked data and concept systems. Principles of Data Management is fully aligned with syllabus for the BCS Professional Certificate in Data Management Essentials, making this the go-to text to unlocking the value of your data. Ideal for business managers and all involved in the development of information systems as well as data management professionals Comprehensive and descriptive view of data management Suitable for all levels, from beginners to advanced learners Must-read for anyone involved in the development of systems to manage data Trade ReviewThis book is an excellent guide to understanding data management theory and techniques. It works at all levels: from beginner to advanced, and from reference source to the practicalities of implementation. I would highly recommend to anyone wanting to get to grips with data management, regardless of experience in the field. -- Ian Wallis, Managing Director, Data Strategists LtdKeith has developed a broad and thorough understanding of all aspects of data management over many years, so is without doubt one of the authorities on data management. This updated book includes reference to a number of new techniques as well as refining existing guidance on data modelling and database structures. Keith clearly explains both the importance of planning and analysis of databases and repositories and an explanation of key techniques to achieve this. A ‘must buy’ for the bookshelf of any data management practitioner. -- Julian Schwarzenbach, Chair of the BCS Data Management Specialist GroupThis book provides a comprehensive and descriptive view of data management within a database setting. This is a must read for anyone involved in the development of systems to manage data. This book is as useful as it is interesting. It covers everything you need to know about getting the most out of your data management processes and architecture. -- Ian Rush, Data & Process Advantage LtdTable of ContentsPart 1: Preliminaries Chapter 1 Data and the enterprise Chapter 2 Databases and their development Chapter 3 What is data management? Part 2: Data Administration Chapter 4 Corporate data modelling Chapter 5 Data definition and naming Chapter 6 Metadata Chapter 7 Data quality Chapter 8 Data accessibility Chapter 9 Master data management Part 3: Database and Repository Administration Chapter 10 Database administration Chapter 11 Repository administration Part 4: The Data Management Environment Chapter 12 The use of packaged application software Chapter 13 Distributed data and databases Chapter 14 Business intelligence Chapter 15 Object orientation Chapter 16 Multimedia Chapter 17 Integrating data and web technology Chapter 18 Linked data Chapter 19 Concept systems Chapter 20 Big data and artificial intelligence Appendices Appendix A Comparison of data modelling notations Appendix B Generic data models Appendix C HTML and XML Appendix D Techniques and skills for data management Appendix E Data strategy Appendix F International standards for data management Appendix G The BCS Data Management Essentials syllabus

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Book SynopsisIs the Brexit vote successful big data politics or the end of democracy? Why do airlines overbook, and why do banks get it wrong so often? How does big data enable Netflix to forecast a hit, CERN to find the Higgs boson and medics to discover if red wine really is good for you? And how are companies using big data to benefit from smart meters, use advertising that spies on you and develop the gig economy, where workers are managed by the whim of an algorithm?The volumes of data we now access can give unparalleled abilities to make predictions, respond to customer demand and solve problems. But Big Brother's shadow hovers over it. Though big data can set us free and enhance our lives, it has the potential to create an underclass and a totalitarian state. With big data ever-present, you can't afford to ignore it. Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) - brings big data to life.Trade ReviewAs always, Clegg writes with an easy clarity that draws us in - no technical expertise required to understand his exploration of this essential subject - and throughout Big Data's highly enjoyable pages, the spread and range of material is highly impressive - dizzying in fact. I personally found entirely new perspectives on the subject that will keep me pondering for quite some time. I should add that, if I were still a statistics lecturer at Oxford, I would recommend the book to my students as bedside reading. -- Peet Morris * Former Lecturer in Statistics (St Hilda’s College Oxford), Lecturer/Researcher in software development *Clegg provides an engaging insight, reflecting on its positives and negatives. A holiday workout for the brain. * Saga Magazine *Acclaimed science writer Brian Clegg - a habitual early adopter of new technology (and the owner of the second-ever copy of Windows in the UK) brings big data to life. * Laboratory News *

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Book SynopsisSolve all big data problems by learning how to create efficient data modelsKey Features Create effective models that get the most out of big data Apply your knowledge to datasets from Twitter and weather data to learn big data Tackle different data modeling challenges with expert techniques presented in this book Book DescriptionModeling and managing data is a central focus of all big data projects. In fact, a database is considered to be effective only if you have a logical and sophisticated data model. This book will help you develop practical skills in modeling your own big data projects and improve the performance of analytical queries for your specific business requirements.To start with, you’ll get a quick introduction to big data and understand the different data modeling and data management platforms for big data. Then you’ll work with structured and semi-structured data with the help of real-life examples. Once you’ve got to grips with the basics, you’ll use the SQL Developer Data Modeler to create your own data models containing different file types such as CSV, XML, and JSON. You’ll also learn to create graph data models and explore data modeling with streaming data using real-world datasets.By the end of this book, you’ll be able to design and develop efficient data models for varying data sizes easily and efficiently.What you will learn Get insights into big data and discover various data models Explore conceptual, logical, and big data models Understand how to model data containing different file types Run through data modeling with examples of Twitter, Bitcoin, IMDB and weather data modeling Create data models such as Graph Data and Vector Space Model structured and unstructured data using Python and R Who this book is forThis book is great for programmers, geologists, biologists, and every professional who deals with spatial data. If you want to learn how to handle GIS, GPS, and remote sensing data, then this book is for you. Basic knowledge of R and QGIS would be helpful.Table of ContentsTable of Contents Introduction to Big Data and Data Management Data Modeling and Data Management platforms for Big Data Defining Data Model Categorizing Data Model Structures of Data Model Modeling Structured Data Modeling with Unstructured Data Modeling with Steaming Data Streaming Sensors Data Concept and Approaches of Big Data Management DBMS to BDMS Big Data Management Services and Vendors Modeling Twitter Feeds using Python Modeling Weather Data Points with Python Modeling IMDB Data Points with Python

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Book SynopsisIn the second edition of this very successful book, Tony Sammes and Brian Jenkinson show how the contents of computer systems can be recovered, even when hidden or subverted by criminals. Equally important, they demonstrate how to insure that computer evidence is admissible in court. Updated to meet ACPO 2003 guidelines, Forensic Computing: A Practitioner's Guide offers: methods for recovering evidence information from computer systems; principles of password protection and data encryption; evaluation procedures used in circumventing a system’s internal security safeguards, and full search and seizure protocols for experts and police officers.Trade ReviewFrom the reviews of the second edition: "This book was the product of an ‘arms race’. … It is now listed as the standard text around which all the Forensic Computing courses at Cranfield and some other universities are based. … It is filled with good practical advice and is especially good on interpreting partition tables. … All in all this is a useful … guide to the discipline. … Truly the forensic computing expert is living in interesting times." (Alikelman, June, 2009)Table of ContentsForensic Computing Understanding Information IT Systems Concepts PC Hardware and Inside The Box Disk Geometry The New Technology File System The Treatment of PCs The Treatment of Electronic Organisers Looking Ahead (Just a little bit more) Appendices: Common Character Codes; Some Common File Format Signatures; A Typical Set of POST codes; Typical BIOS Beep Codes and Error Messages; Disk Partition Table Types; Ezxtended Partitions; Registers and Order Code for the INtel 8086; NFTS Boot Sector and BIOS Parameter Block; MFT Header and Attribute Maps; The Relationship Between CHS and LBA Addressing; Alternate Data Streams - a Brief Explanation

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Book SynopsisCombining multiple sensors in order to better grasp a tricky, or even critical, situation is an innate human reflex. Indeed, humans became aware, very early on, of the need to combine several of our senses so as to acquire a better understanding of our surroundings when major issues are at stake. On the basis of this need, we have naturally sought to equip ourselves with various kinds of artificial sensors to enhance our perceptive faculties. The association of multiple heterogeneous sensors provides a reliable and efficient situation assessment in difficult operational contexts, but imperfect local observations need to be managed in a suitable way (uncertainty, imprecision, incompleteness, unreliability, etc.). The theories of uncertainty make it possible to benefit from such information, but the implementation of these theories requires specific developments to meet the needs of multisensor data fusion. This book first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms (probability, fuzzy set theory, possibility theory, belief function theory), a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book. Furthermore, the implementation of these operators is illustrated and discussed within the framework of generic applications.Table of ContentsINTRODUCTION ix CHAPTER 1. MULTISENSOR DATA FUSION 1 1.1. Issues at stake 1 1.2. Problems 4 1.2.1. Interpretation and modeling of data 8 1.2.2. Reliability handling 10 1.2.3. Knowledge propagation 11 1.2.4. Matching of ambiguous data 12 1.2.5. Combination of sources14 1.2.6. Decision-making 16 1.3. Solutions 21 1.3.1. Panorama of useful theories 21 1.3.2. Process architectures 24 1.4. Position of multisensor data fusion 27 1.4.1. Peculiarities of the problem 27 1.4.2. Applications of multisensor data fusion 28 CHAPTER 2. REFERENCE FORMALISMS 31 2.1. Probabilities 31 2.2. Fuzzy sets 35 2.3. Possibility theory 39 2.4. Belief functions theory 43 2.4.1. Basic functions 44 2.4.2. A few particularly useful cases 47 2.4.3. Conditioning/deconditioning 49 2.4.4. Refinement/coarsening 50 CHAPTER 3. SET MANAGEMENT AND INFORMATION PROPAGATION 53 3.1. Fuzzy sets: propagation of imprecision 53 3.2. Probabilities and possibilities: the same approach to uncertainty 56 3.3. Belief functions: an overarching vision in terms of propagation 57 3.3.1. A generic operator: extension 58 3.3.2. Elaboration of a mass function with minimum specificity 61 3.3.3. Direct exploitation of the operator of extension 64 3.4. Example of application: updating of knowledge over time 66 CHAPTER 4. MANAGING THE RELIABILITY OF INFORMATION 71 4.1. Possibilistic view 72 4.2. Discounting of belief functions 73 4.3. Integrated processing of reliability 75 4.4. Management of domains of validity of the sources 77 4.5. Application to fusion of pixels from multispectral images 82 4.6. Formulation for problems of estimation 87 CHAPTER 5. COMBINATION OF SOURCES 91 5.1. Probabilities: a turnkey solution, Bayesian inference 92 5.2. Fuzzy sets: a grasp of axiomatics 94 5.3. Possibility theory: a simple approach to the basic principles 102 5.4. Theory of belief functions: conventional approaches 106 5.5. General approach to combination: any sets and logics 113 5.6. Conflict management 118 5.7. Back to Zadeh’s paradox 122 CHAPTER 6. DATA MODELING 127 6.1. Characterization of signals 127 6.2. Probabilities: immediate taking into account 130 6.3. Belief functions: an open-ended and overarching framework 131 6.3.1. Integration of data into the fusion process 132 6.3.2. Generic problem: modeling of Cij values 135 6.3.3. Modeling measurements with stochastic learning 139 6.3.4. Modeling measurements with fuzzy learning 144 6.3.5. Overview of models for belief functions 148 6.4. Possibilities: a similar approach 153 6.5. Application to a didactic example of classification 157 CHAPTER 7. CLASSIFICATION: DECISION-MAKING AND EXPLOITATION OF THE DIVERSITY OF INFORMATION SOURCES 165 7.1. Decision-making: choice of the most likely hypothesis 166 7.2. Decision-making: determination of the most likely set of hypotheses 168 7.3. Behavior of the decision operator: some practical examples 171 7.4. Exploitation of the diversity of information sources: integration of binary comparisons 175 7.5. Exploitation of the diversity of information sources: classification on the basis of distinct but overlapping sets 179 7.6. Exploitation of the diversity of the attributes: example of application to the fusion of airborne image data 189 CHAPTER 8. SPATIAL DIMENSION: DATA ASSOCIATION 193 8.1. Data association: a multiform problem, which is unavoidable in multisensor data fusion 194 8.2. Construction of a general method for data association 197 8.3. Simple example of the implementation of the method 203 CHAPTER 9. TEMPORAL DIMENSION: TRACKING 211 9.1. Tracking: exploitation of the benefits of multisensory data fusion 211 9.2. Expression of the Bayesian filter 218 9.2.1. Statistical gating 218 9.2.2. Updating 219 9.2.3. Prediction 220 9.3. Signal discrimination process 221 9.3.1. Fusion at the level of each resolution cell 222 9.3.2. Fusion at the level of the validation gate 224 9.3.3. Overview of a practical implementation of the discrimination method 226 9.4. Extensions of the basic MSF 228 9.4.1. Data association 228 9.4.2. Joint tracking of multiple targets 229 9.4.3. Multi-model filtering 231 9.5. Examples of application 232 9.5.1. Extraction power 233 9.5.2. Handling of unfamiliar signatures 235 9.5.3. Tracking on spatially ambiguous observations 238 CONCLUSION 241 BIBLIOGRAPHY 249 INDEX 257

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Book SynopsisFor 200 years, industry mastered iron, fire, strength and energy. Today, electronics shapes our everyday objects, integrating chips: computers, phones, keys, games, household appliances, etc. Data, software and calculation frame the conduct of humankind, and everything is translated into data. The first volume in this series analyzes the stakes of the massive data which accumulate on the Internet, keeping track of our actions and gestures, the state of the world and our knowledge.Table of ContentsNote to Reader ixJean-Pierre CHAMOUX Preface xiJean-Pierre CHAMOUX Introduction xviiJean-Pierre CHAMOUX Part 1 What’s New and Why? 1 Introduction to Part 1 3 Chapter 1 Digital Omnipresence: Its Causes and Consequences 5Jean-Pierre CHAMOUX From analog to digital 7 Computerization, prior to the digital revolution 8 The Internet, a real breakthrough factor 9 Importance of competition 11 Highly regulated communication infrastructure and services 14 Changes in European markets 18 From crisis to American rebound 20 Telephone and Internet companies 22 Summary 23 Bibliography 27 Appendix The 2000 crisis: lessons and industrial consequences 28 Chapter 2 Mathematical Culture and Massive Data 35Jean DHOMBRES The Turing machine, the inspiration for massive data? 39 What does “calculable” mean? 42 From natural to mathematics 46 An ancient algorithm and its programming: gcd 48 Styles of mathematization 51 Mathematics, algorithms and measures of complexity 55 Representations of communities and massive data 58 Constraints of mathematization 62 The notion of the invariant: perspective,vectorial, linear algebra and matrix representation 66 Graphs, their calculations and some algorithms 69 Conclusion 72 Bibliography 74 Chapter 3 From Sample to Big Data: Competing or Complementary Paradigms? 77Philippe TASSI Sampling and big data: useful synergy 79 Is this an ephemeral or sustainable phenomenon 84 How can data confidentiality be guaranteed? 89 Protecting personal data 95 How to conclude? 101 Bibliography 102 Chapter 4 Researching Forms and Correlations:the Big Data Approach 105Gilles SANTINI Deconstruction and accumulation of data 107 Massive databases 107 Playing games with the devil? 109 A case study 110 The protection of privacy 112 Automatic processing 113 Conclusion 113 Bibliography 114 Chapter 5 Bitcoin: an Innovative System 115Gérard DRÉAN Bitcoin and bitcoin: some key concepts 117 The Bitcoin system 118 The transactions register 121 Overview 124 Reliability and safety 125 Software changes 127 Fraud and control takeover 128 Evolution of Bitcoin and peer-to-peer payment 129 In summary 131 Bibliography 131 Part 2 Tactics and Strategies 133 Introduction to Part 2 135 Chapter 6 Bitcoin and Other Cyber-currency 137Gérard DRÉAN Introduction 139 Bitcoin, the first cyber-currency 139 New tools of exchange 143 The problem of growth 144 Towards a refoundation? 146 Cyberspace and sovereign currencies 151 Competition between payment systems 153 Competition between units of account 156 Conclusion 159 Bibliography 161 Chapter 7 Health and Care: Digital Challenges 163Isabelle HILALI What are we talking about? 165 Major changes for physicians 165 What about the patients? 168 New challenges for medication 168 Hospitals, clinics and nursing homes 171 Will we be able to insure health tomorrow? 172 The challenges of digital healthcare 174 Conclusion 178 Bibliography 180 Chapter 8 Access to Health Data:Debates and Controversies in France 183Joumana BOUSTANY, Gabriella SALZANO and Christian BOURRET Methodology 185 Literature review 186 Information systems in France 188 Restricted and controlled access systems 190 Restricted access systems 193 Open information systems 194 Going towards an opening of health data? 196 Conclusion 197 Bibliography 198 Appendix Main acronyms used in this chapter 199 Chapter 9 Artificial Intelligence: Utopia or Progress? 203Jean-Pierre CHAMOUX Before computers, there were robots 205 What does artificial intelligence cover? 207 Ambitions of artificial intelligence 209 From machines to humanity 212 Myths associated with artificial intelligence 215 Assessing the intelligence of a machine? 217 Concluding reflections 218 Bibliography 221 Appendices 223 Appendix 1 225 Appendix 2 227 List of Authors 229 Index of Names and Brands 231 Index of Notions 233

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Book SynopsisDo you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices. This book describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices.

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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 constitutes the workshop proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019. The 14 full papers presented were carefully selected and reviewed from 26 submissions to the three following workshops: the 6th International Workshop on Big Data Management and Service, BDMS 2019; the 4th International Workshop on Big Data Quality Management, BDQM 2019; and the Third International Workshop on Graph Data Management and Analysis, GDMA 2019. This volume also includes the short papers, demo papers, and tutorial papers of the main conference DASFAA 2019.Table of ContentsThe 6th International Workshop on Big Data Management and Service (BDSM 2019).- A Probabilistic Approach for Inferring Latent Entity Associations in Textual Web Contents.- UHRP Uncertainty-Based Pruning Method for Anonymized Data Linear Regression.- Meta-path based MiRNA-disease Association Prediction.- Medical Question Retrieval based on Siamese Neural Network and Transfer learning method.- An adaptive Kalman filter based Ocean Wave Prediction Model using Motion Reference Unit Data.- ASLM: Adaptive Single Layer Model for Learned Index.- SparseMAAC: Sparse Attention for Multi-Agent Reinforcement Learning.- The 4th International Workshop on Big Data Quality Management (BDQM 2019).- Identifying Reference Relationship of Desktop Files Based on Access Logs.- Visualization of Photo Album: Selecting a Representative Photo of a Specific Event.- Data Quality Management in Institutional Research Output Data Center.- Generalized Bayesian Structure Learning from Noisy Datasets.- The Third International Workshop on Graph Data Management and Analysis (GDMA 2019).- ANDMC: An Algorithm for Author Name Disambiguation Based on Molecular Cross Clustering.- Graph Based Aspect Extraction and Rating Classification of Customer Review Data.- Streaming Massive Electric Power Data Analysis Based on Spark Streaming.- Short Papers.- Deletion Robust k-Coverage Queries.- Episodic Memory Network with Self-Attention for Emotion Detection.- Detecting Suicidal Ideation with Data Protection in Online Communities.- Hierarchical Conceptual Labeling.- Anomaly Detection in Time-Evolving Attributed Networks.- A Multi-task Learning Framework for Automatic Early Detection of Alzheimer’s.- Top-k Spatial Keyword Query with Typicality and Semantics.- Align Reviews with Topics in Attention Network for Rating Prediction.- PSMSP: A Parallelized Sampling-based Approach for Mining Top-k Sequential Patterns in Database Graphs.- Value-Oriented Ranking of Online Reviews Based on Reviewer-influenced Graph.- Ancient Chinese Landscape Painting Composition Classification by Using Semantic Variational Autoencoder.- Learning Time-Aware Distributed Representations of Locations from Spatio-Temporal Trajectories.- Hyper2vec: Biased Random Walk for Hyper-Network Embedding.- Privacy-preserving and dynamic spatial range aggregation query processing in wireless sensor networks.- Adversarial Discriminative Denoising for Distant Supervision Relation Extraction.- Nonnegative Spectral Clustering for Large-Scale Semi-Supervised Learning.- Distributed PARAFAC Decomposition Method based on In-Memory Big Data System.- GPU-Accelerated Dynamic Graph Coloring.- Relevance-based Entity Embedding.- An Iterative Map-Trajectory Co-Optimisation Framework Based on Map-Matching and Map Update.- Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding.- AGREE: Attentive Tour Group Recommendation with Multi-Modal Data.- Random Decision DAG: An Entropy Based Compression Approach for Random Forest.- Generating Behavior Features for Cold-Start Spam Review Detection.- TCL: Tensor-CNN-LSTM for Travel Time Prediction with Sparse Trajectory Data.- A Semi-supervised Classification Approach for Multiple Time-varying Networks with Total Variation.- Multidimensional Skylines Over Streaming Data.- A domain adaptation approach for multistream classification.- Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time Bidding.- Deep Sequential Multi-task Modeling for Next Check-in Time and Location Prediction.- SemiSync: Semi-supervised Clustering by Synchronization.- Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors.- MVS-match: An Efficient Subsequence Matching Approach Based on the Series Synopsis.- Temporal-Spatial Recommendation for On-demand Cinemas.- Finding the key influences on the house price by Finite Mixture Model based on the real estate data in Changchun.- Semi-supervised Clustering with Deep Metric Learning.- Spatial Bottleneck Minimum Task Assignment with Time-delay.- A Mimic Learning Method for Disease Risk Prediction with Incomplete Initial Data.- Hospitalization Behavior Prediction Based on Attention and Time Adjustment Factors in Bidirectional LSTM.- Modeling Item Category for Effective Recommendation.- Distributed Reachability Queries on Massive Graphs.- Edge-Based Shortest Path Caching in Road Networks.- Extracting Definitions and Hypernyms with a Two-Phase Framework.- Tag Recommendation by Word-Level Tag Sequence Modeling.- A New Statistics Collecting Method with Adaptive Strategy.- Word Sense Disambiguation with Massive Contextual Texts.- Learning DMEs from Positive and Negative Examples.- Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition.- DRGAN: A GAN-based Framework for Doctor Recommendation in Chinese On-line QA Communities.- Attention-based Abnormal-Aware Fusion Network for Radiology Report Generation.- LearningTour: A Machine Learning Approach for Tour Recommendation based on Users’ Historical Travel Experience.- TF-Miner: Topic-specific Facet Mining by Label Propagation.- Fast Raft Replication for Transactional Database Systems over Unreliable Networks.- Parallelizing Big De Bruijn Graph Traversal for Genome Assembly on GPU Clusters.- GScan: Exploiting Sequential Scans for Subgraph Matching.- SIMD Accelerates the Probe Phase of Star Joins in Main Memory Databases.- A Deep Recommendation Model Incorporating Adaptive Knowledge-based Representations.- BLOMA: Explain Collaborative Filtering via Boosted Local Rank-One Matrix Approximation.- Spatiotemporal-Aware Region Recommendation with Deep Metric Learning.- On the Impact of the Length of Subword Vectors on Word Embeddings.- Using Dilated Residual Network to Model Distant Supervision Relation Extraction.- Modeling More Globally: A Hierarchical Attention Network via Multi-Task Learning for Aspect-Based Sentiment Analysis.- A Sparse Matrix-based Join for SPARQL Query Processing.- Change Point Detection for Streaming High-Dimensional time series.- Demo Papers.- Distributed Query Engine for Multiple-Query Optimization over Data Stream.- Adding Value by Combining Business and Sensor Data: An Industry 4.0 Use Case.- AgriKG: An Agricultural Knowledge Graph and Its Applications.- KGVis: An Interactive Visual Query Language for Knowledge Graphs.- OperaMiner: Extracting Character Relations from Opera Scripts using Deep Neural Networks.- GparMiner: A System to mine Graph Pattern Association Rules.- A Data Publishing System Based on Privacy Preservation.- Privacy as a Service: Publishing Data and Models.- Dynamic Bus Route Adjustment Based on Hot Bus Stop Pair Extraction.- DHDSearch: A Framework for Batch Time Series Searching on MapReduce.- Bus Stop Refinement based on Hot Spot Extraction.- Adaptive Transaction Scheduling for Highly Contended Workloads.- IMOptimizer: An Online Interactive Parameter Optimization System based on Big Data.- Tutorial Papers.- Cohesive Subgraphs with Hierarchical Decomposition on Big Graphs.- Tracking User Behaviours: Laboratory-Based and In-The-Wild User S.- Mining Knowledge Graphs for Vision Tasks.- Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management.- Knowledge Graph Data Management.- Deep learning for Healthcare Data Processing.

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Book SynopsisThe two-volume set of LNCS 11778 and 11779 constitutes the refereed proceedings of the 18th International Semantic Web Conference, ISWC 2019, held in Auckland, New Zealand, in October 2019. The ISWC conference is the premier international forum for the Semantic Web / Linked Data Community.The total of 74 full papers included in this volume was selected from 283 submissions. The conference is organized in three tracks: for the Research Track 42 full papers were selected from 194 submissions; the Resource Track contains 21 full papers, selected from 64 submissions; and the In-Use Track features 11 full papers which were selected from 25 submissions to this track.The chapter "The SEPSES knowledge graph: An integrated resource for cybersecurity" is open access under a CC BY 4.0 license at link.springer.com.Table of ContentsResources Track.- The KEEN Universe: An Ecosystem for Knowledge Graph Embeddings with a Focus on Reproducibility and Transferability.- VLog: A Rule Engine for Knowledge Graphs.- ArCo: the Italian Cultural Heritage Knowledge Graph.- Making Study Populations Visible through Knowledge Graphs.- LC-QuAD 2.0: A large dataset for complex question answering over Wikidata and DBpedia.- SEO: A Scientific Events Data Model.- DBpedia FlexiFusion - The Best of Wikipedia > Wikidata > Your Data.- The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data.- The RealEstateCore Ontology.- FoodKG: Semantics-Driven Knowledge Graph for Food Recommendation.- BTC-2019: The 2019 Billion Triple Challenge Dataset.- Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge.- The SEPSES knowledge graph: An integrated resource for cybersecurity.- SemanGit: A Linked Dataset from git.- Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources.- List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists.- A Scalable Framework for Quality Assessment of RDF Datasets.- QaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge Graphs.- Sparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasets.- ClaimsKG: A Knowledge Graph of Fact-Checked Claims.- CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison.- In-Use Track.- Semantically-enabled Optimization of Digital Marketing Campaigns.- An End-to-end Semantic Platform For Nutritional Diseases Management.- VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection.- Personalized Knowledge Graphs for the Pharmaceutical Domain.- Use of OWL and Semantic Web Technologies at Pinterest.- An Assessment of Adoption and Quality of Linked Data in European Open Government Data.- Easy Web API Development with SPARQL Transformer.- Benefit graph extraction from healthcare policies.- Knowledge Graph Embedding for Ecotoxicological Effect Prediction.- Improving Editorial Workflow and Metadata Quality at Springer Nature.- A Pay-as-you-go Methodology to Design and Build Enterprise Knowledge Graphs from Relational Databases.

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Book SynopsisThis book constitutes the refereed post-conference proceedings of the Special Event on the Analysis of Experimental Algorithms, SEA² 2019, held in Kalamata, Greece, in June 2019.The 35 revised full papers presented were carefully reviewed and selected from 45 submissions. The papers cover a wide range of topics in both computer science and operations research/mathematical programming. They focus on the role of experimentation and engineering techniques in the design and evaluation of algorithms, data structures, and computational optimization methods.

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Book SynopsisThis Festschrift was published in honor of Hans L. Bodlaender on the occasion of his 60th birthday. The 14 full and 5 short contributions included in this volume show the many transformative discoveries made by H.L. Bodlaender in the areas of graph algorithms, parameterized complexity, kernelization and combinatorial games. The papers are written by his former Ph.D. students and colleagues as well as by his former Ph.D. advisor, Jan van Leeuwen.Chapter “Crossing Paths with Hans Bodlaender: A Personal View on Cross-Composition for Sparsification Lower Bounds” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Table of ContentsSeeing Arboretum for the (partial k) Trees.- Collaborating With Hans: Some Remaining Wonderments.- Hans Bodlaender and the Theory of Kernelization Lower Bounds.- Algorithms, Complexity, and Hans.- Lower Bounds for Dominating Set in Ball Graphs and for Weighted Dominating Set in Unit-Ball Graphs.- As Time Goes By: Reflections on Treewidth for Temporal Graphs.- Possible and Impossible Attempts to Solve the Treewidth Problem via ILPs.- Crossing Paths with Hans Bodlaender: A Personal View on Cross-Composition for Sparsification Lower Bounds.- Efficient Graph Minors Theory and Parameterized Algorithms for (Planar) Disjoint Paths.- Four shorts stories on surprising algorithmic uses of treewidth.- Algorithms for NP-Hard Problems via Rank-related Parameters of Matrices.- A Survey on Spanning Tree Congestion.- Surprising Applications of Treewidth Bounds for Planar Graphs.- Computing tree decompositions.- Experimental analysis of treewidth.- A Retrospective on (Meta) Kernelization.- Games, Puzzles and Treewidth.- Fast Algorithms for Join Operations on Tree Decompositions.

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Book SynopsisThis book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike. Table of ContentsElements of Probability Theory.- Descriptive and Inferential Statistics.- Linear Regression and Correlation.

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Book SynopsisThis book is intended for specialists in systems engineering interested in new, general techniques and for students and practitioners interested in using these techniques for solving specific practical problems. For many real-world, complex systems, it is possible to create easy-to-compute explicit analytical models instead of time-consuming computer simulations. Usually, however, analytical models are designed on a case-by-case basis, and there is a scarcity of general techniques for designing such easy-to-compute models. This book fills this gap by providing general recommendations for using analytical techniques in all stages of system design, implementation, testing, and monitoring. It also illustrates these recommendations using applications in various domains, such as more traditional engineering systems, biological systems (e.g., systems for cattle management), and medical and social-related systems (e.g., recommender systems).Table of Contents​Formulation of the Problem.- Analytical Techniques for Describing User Preferences: 80/20 Rule Partially Explains 7 Plus Minus 2 Law.- Analytical Techniques Help Enhance the Results of Data Mining: Case Study of Cow Insemination.- Case When Analytical Techniques Invalidate the Conclusions of Data Mining: Reversed Flynn Effect of Decreasing IQ Test Scores.- Analytical Techniques for Taking into Account Several Aspects of a Designed Systems: Case Study of Computation-Communication Tradeoff.- Analytical Techniques for Testing: Optimal Distribution of Testing Resources Between Different System Levels.- Index.

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Book SynopsisThis book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics.The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book.Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed.The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.Trade Review“The authors have written a comprehensive broad-audience treatment of sequential Monte Carlo (SMC) methods, covering all its major and diverse applications. … The book is structured as an advanced Ph.D.-level textbook.” (Michael Ludkovski, Mathematical Reviews, May, 2022)Table of Contents1 Preface.- 2 Introduction to state-space models.- 3 Beyond state-space models.- 4 Introduction to Markov processes.- 5 Feynman-Kac models: definition, properties and recursions.- 6 Finite state-spaces and hidden Markov models.- 7 Linear-Gaussian state-space models.- 8 Importance sampling.- 9 Importance resampling.- 10 Particle filtering.- 11 Convergence and stability of particle filters.- 12 Particle smoothing.- 13 Sequential quasi-Monte Carlo.- 14 Maximum likelihood estimation of state-space models.- 15 Markov chain Monte Carlo.- 16 Bayesian estimation of state-space models and particle MCMC.- 17 SMC samplers.- 18 SMC2, sequential inference in state-space models.- 19 Advanced topics and open problems.

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Book SynopsisThis textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.Trade Review“It was very interesting to go through the pages of this book. The authors should be commended for writing a thorough book about complex concepts of data analysis in R that could, however, be read easily. I warmly recommend this book to students of statistics but also to professionals who would like to acquire advanced analytical skills or improve their competencies in R, especially nowadays with R very popular amongst data analysts.” (Georgios Nikolopoulos, ISCB News, iscb.info, Issue 71, June, 2021)Table of ContentsPreface.- 1 Introduction.- 2 Introduction to R.- 3 Databases in R.- 4 Visualization.- 5 Data Analysis with R.- R Packages and Funtions.

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Book SynopsisThis book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.​ Table of ContentsChapter 1. Cities in Transition. - Chapter 2. Data Augmented Design (DAD): Definitions, Dimensions, Performance, and Applications. - Chapter 3. Human-scale Urban Form and its Application in DAD. - Chapter 4. Data Adaptive Urban Design: A Case Study of Shanghai Hengfu Historical District. - Chapter 5. Multidimensional Data-based City Images: Cultural Reactivation of Waterfront Industrial Heritage Design in Shanghai. - Chapter 6. Fine-Scale Recognition-based Design Guidelines for Dealing with Shrinking Cities: A Case Study of Hegang. - Chapter 7. Quantifying Urban Form as a Case Study in Expansion-oriented Design: Design Practices in the Tongzhou Subcenter. - Chapter 8. Defining the Density of the Xiong’an New Area based on Global Experience. - Chapter 9. The Next Form of Human Settlement: A Design for Future Yilong City. - Chapter 10. The Future of the Smart Island: A Design for a Natural and Technological Experience District on Huangguan Island.

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Book SynopsisThis two-volume set LNCS 12131 and LNCS 12132 constitutes the refereed proceedings of the 17th International Conference on Image Analysis and Recognition, ICIAR 2020, held in Póvoa de Varzim, Portugal, in June 2020.The 54 full papers presented together with 15 short papers were carefully reviewed and selected from 123 submissions. The papers are organized in the following topical sections: image processing and analysis; video analysis; computer vision; 3D computer vision; machine learning; medical image and analysis; analysis of histopathology images; diagnosis and screening of ophthalmic diseases; and grand challenge on automatic lung cancer patient management.Due to the corona pandemic, ICIAR 2020 was held virtually only.Table of ContentsMachine Learning.- Medical Image and Analysis.- Analysis of Histopathology Images.- Diagnosis and Screening of Ophthalmic Diseases.- Grand Challenge on Automatic Lung Cancer Patient Management.

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Book SynopsisThis open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.Table of ContentsFoundations.- Chapter 1. Ecosystem of Big Data.- Chapter 2. Knowledge Graphs: The Layered Perspective.- Chapter 3. Big Data Outlook, Tools, and Architectures.- Architecture.- Chapter 4. Creation of Knowledge Graphs.- Chapter 5. Federated Query Processing.- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight.- Methods and Solutions.- Chapter 7. Scalable Knowledge Graph Processing using SANSA.- Chapter 8. Context-Based Entity Matching for Big Data.- Applications.- Chapter 9. Survey on Big Data Applications.- Chapter 10. Case Study from the Energy Domain.

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Book SynopsisThis book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.Table of Contents1 Introduction.- 2 Prerequisites from Matrix Analysis.- 3 Multivariate Distributions and Moments.- 4 Dimensionality Reduction.- 5 Classification and Clustering.- 6 Support Vector Machines.- 7 Machine Learning.- Index.

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Book SynopsisThis book presents a unique, step-by-step approach for monitoring, detecting, analyzing and mitigating complex network cyber threats. It includes updated processes in response to asymmetric threats, as well as descriptions of the current tools to mitigate cyber threats. Featuring comprehensive computer science material relating to a complete network baseline with the characterization hardware and software configuration, the book also identifies potential emerging cyber threats and the vulnerabilities of the network architecture to provide students with a guide to responding to threats. The book is intended for undergraduate and graduate college students who are unfamiliar with the cyber paradigm and processes in responding to attacks. Table of ContentsPre-incident Planning and Analysis.- Incident Detection and Characterization.- Vulnerability/Consequence Analysis.- Incident Response and Recovery.- Cloud Architecture.- Lessons Learned.

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Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.

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Book SynopsisThis book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading. Design science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice. Chapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism, and Chapter 13 discusses ethical issues and principles for design science research. The new Chapter 14 showcases a study on digital health consultations and illustrates the whole process in one comprehensive example. Also added to this 2nd edition are a number of sections on practical guidelines for carrying out basic design science tasks, a discussion on design thinking and its relationship to design science, and the description of artefact classifications. Eventually, both the references in each chapter and the companion web site were updated to reflect recent findings.Table of Contents1 Introduction.- 2 Knowledge Types and Forms.- 3 Research Strategies and Methods.- 4 A Method Framework for Design Science Research.- 5 Explicate Problem.- 6 Define Requirements.- 7 Design and Develop Artefact.- 8 Demonstrate Artefact.- 9 Evaluate Artefact.- 10 Communicate Artefact Knowledge.- 11 Systems Development and the Method Framework for Design Science Research.- 12 Research Paradigms.- 13 Ethics and Design Science. 14 Digital Consultations — a Case Study.

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Book SynopsisThis book is a greatly supplemented translation from Portuguese, originally published in 2015. It discusses the most appropriate ways to create databases for research on history and other humanities, including an extensive debate about the usages that historians have made of computing since the 1950s. It has four chapters: the first is dedicated to theoretical and methodical questions about the usage of databases in history; the second is about technical issues; the third presents the concept of research engineering (how to improve research in groups); the last is about the construction of databases. The author states that the use of technology in research in history and humanities should be preceded and mediated by theories and methods which deal with these disciplines and not by technical issues. The historian must know how to think “correctly” in order to use the technological tools in an autonomous way. The book provides a background, demonstrating how theory, methodology, and technique are always articulated in historical research, and will appeal to history students and researchers.Table of ContentsIntroductionSome theoretical and methodical questions One craftsman, one operationDismantling things in a organized way On the shoulders of giants Some computer issues Data structureRelational databasesConceptual, logical and physical modelsPhysical model Visual aspects concerning databases Colors Technique and theory: everyday problems and practical decisions Update, standardize or maintain the original? Fuzzy informationResearch Engineering Initial surveyCollection Strategies Manual filling of the database Importing data (digital) Database administration Constructing databases: some concrete examples Source-centered databases Method-centered databases Conclusion Bibliografia

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users. 1. Highlights recent applications and techniques in cyber intelligence2. Includes the proceedings of the 2021 International Conference on Applications and Techniques in Cyber Intelligence (ATCI 2021) 3. Presents a broad range of scientific research on cyber intelligence

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Book SynopsisThis book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

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Book SynopsisThis book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics. Table of ContentsPrediction of Liver Cancer by Artificial Neural Network.- Remarks on the limit-circle classification of Conformable Fractional Sturm-Liouville Operator.- Improving Search Relevance with Word Embedding Based Clusters.- Improving Search Relevance with Word Embedding Based Clusters.- Diagnosis of Parkinson's Disease with Acoustic Sounds by Rule Based Model.- Development of Face Recognition System by Using Deep Learning and FaceNet Algorithm in the Operations Processes.- Mobile Assisted Travel Planning Software: The Case of Burdur.- Optimal Coordination of Directional Overcurrent Relays Using Artificial Ecosystem-based Optimization.- The Effect of Auscultation Areas on Nonlinear Classifiers in Computerized Analysis of Chronic Obstructive Pulmonary Disease.

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Book SynopsisDatabase technology is an important subject in Computer Science. Every large company and nation needs a database to store information. The technology has evolved from file systems in the 60’s, to Hierarchical and Network databases in the 70’s, to relational databases in the 80’s, object-oriented databases in the 90’s, and to XML documents and NoSQL today. As a result, there is a need to reengineer and update old databases into new databases. This book presents solutions for this task.In this fourth edition, Chapter 9 - Heterogeneous Database Connectivity (HDBC) offers a database gateway platform for companies to communicate with each other not only with their data, but also via their database. The ability of sharing a database can contribute to the applications of Big Data and surveys for decision support systems. The HDBC gateway solution collects input from the database, transfers the data into its middleware storage, converts it into a common data format such as XML documents, and then distributes them to the users. HDBC transforms the common data into the target database to meet the user’s requirements, acting like a voltage transformer hub. The voltage transformer converts the voltage to a voltage required by the users. Similarly, HDBC transforms the database to the target database required by the users.This book covers reengineering for data conversion, integration for combining databases and merging databases and expert system rules, normalization for eliminating duplicate data from the database, and above all, HDBC connects all legacy databases to one target database for the users.The authors provide a forum for readers to ask questions and the answers are given by the authors and the other readers on the Internet. Table of ContentsPreface.- Information Systems Reengineering, Integration and Normalization.- Database and Expert System Technology.- Schema Transition.- Data Conversion.- Database Program Translation.- Schema Integration.- Database and Expert-Systems Integration.- Data Normalization.- Heterogeneous Database Connectivity.- Conclusion.

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Book SynopsisThis book includes proceedings of the 15th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2021), which took place in Asan, Korea, on July 1-3, 2021. With the proliferation of wireless technologies and electronic devices, there is a fast-growing interest in Ubiquitous and Pervasive Computing (UPC). The UPC enables to create a human-oriented computing environment where computer chips are embedded in everyday objects and interact with physical world. Through UPC, people can get online even while moving around, thus, having almost permanent access to their preferred services. With a great potential to revolutionize our lives, UPC also poses new research challenges.The aim of the book is to provide the latest research findings, methods, development techniques, challenges, and solutions from both theoretical and practical perspectives related to UPC with an emphasis on innovative, mobile, and Internet services.

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Book SynopsisThis book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.Table of ContentsForeword.- 1 Introduction.- 2 Traditional Estimators and Standard Asymptotics.- 3 Finite Sample Performance of Traditional Estimators.- 4 Traditional Estimators and High-Dimensional Asymptotics.- 5 Summary and Outlook.- Appendices.

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Book SynopsisThis book comprises high-quality refereed research papers presented at The Second International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS2020), held in Moscow, Russia, on December 18–20, 2020, organized jointly by Moscow State Technical University and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in computer science and their technological applications; intelligent systems and intellectual approaches; digital economics and methodological approaches. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and their applications in engineering and management.Table of ContentsOn Mixed Forced and Self-oscillations with Delays in Elasticity and Friction.- An Extensible Network Traffic Classifier Based on Machine Learning Methods.- Intelligent Information Systems Based on Notional Models without Relationships.- Study of Properties of Growing Random Graphs with Neuron-like Structure.- Planning of Computational Experiments in Verification of Mathematical Models of Dynamic Machine Systems.- Optimization of Network Transmission of Multimedia Data Stream in a Cloud System.

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Book SynopsisThis book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprise of research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given in advances of machine learning (ML) focusing on the following algorithms-approaches: Augmented ML, autoencoders, adversarial neural networks, blockchain-adaptive methods, convolutional neural networks, deep learning, ensemble methods, learning-federated learning, neural networks, recurrent – long short-term memory. The application domains are related to: Anomaly detection, bio-medical AI, cyber-security, data fusion, e-learning, emotion recognition, environment, hyperspectral imaging, fraud detection, image analysis, inverse kinematics, machine vision, natural language, recommendation systems, robotics, sentiment analysis, simulation, stock market prediction.Table of ContentsAutomatic Facial Expression Neutralisation Using Generative Adversarial Network.- Creating Ensembles of Generative Adversarial Network Discriminators for One-class Classification.- A Hybrid Deep Learning Ensemble for Cyber Intrusion Detection.- Anomaly Detection by Robust Feature Reconstruction.- Deep Learning of Brain Asymmetry Images and Transfer Learning for Early Diagnosis of Dementia.- Deep learning topology-preserving EEG-based images for autism detection in infants.- Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors.- Liver cancer trait detection and classification through Machine Learning on smart mobile devices.

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Book SynopsisThis book describes how to use expert knowledge—which is often formulated by using imprecise (fuzzy) words from a natural language. In the 1960s, Zadeh designed special "fuzzy" techniques for such use. In the 1980s, fuzzy techniques started controlling trains, elevators, video cameras, rice cookers, car transmissions, etc. Now, combining fuzzy with neural, genetic, and other intelligent methods leads to new state-of-the-art results: in aerospace industry (from drones to space flights), in mobile robotics, in finances (predicting the value of crypto-currencies), and even in law enforcement (detecting counterfeit banknotes, detecting online child predators and in creating explainable AI systems). The book describes these (and other) applications—as well as foundations and logistics of fuzzy techniques. This book can be recommended to specialists—both in fuzzy and in various application areas—who will learn latest techniques and their applications, and to students interested in innovative ideas.Table of ContentsPowerset operators in categories with fuzzy relations dened by monads.- Improved Fuzzy Q-Learning with Replay Memory.- The ulem package: underlining for emphasis.- A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network.- Fuzzy Mathematical Morphology and Applications in Image Processing.

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Book SynopsisThis book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general.

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Book SynopsisThis book describes the utilization of different soft computing techniques and their optimization for providing an accurate and efficient medical diagnosis. The proposed method provides a precise and timely diagnosis of the risk that a person has to develop a particular disease, but it can be adaptable to provide the diagnosis of different diseases. This book reflects the experimentation that was carried out, based on the different optimizations using bio-inspired algorithms (such as bird swarm algorithm, flower pollination algorithms, and others). In particular, the optimizations were carried out to design the fuzzy classifiers of the nocturnal blood pressure profile and heart rate level. In addition, to obtain the architecture that provides the best result, the neurons and the number of neurons per layers of the artificial neural networks used in the model are optimized. Furthermore, different tests were carried out with the complete optimized model. Another work that is presented in this book is the dynamic parameter adaptation of the bird swarm algorithm using fuzzy inference systems, with the aim of improving its performance. For this, different experiments are carried out, where mathematical functions and a monolithic neural network are optimized to compare the results obtained with the original algorithm. The book will be of interest for graduate students of engineering and medicine, as well as researchers and professors aiming at proposing and developing new intelligent models for medical diagnosis. In addition, it also will be of interest for people working on metaheuristic algorithms and their applications on medicine.

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Book SynopsisThis edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.Table of ContentsEngagement Analysis of Students in Online Learning Environments.- Application of Artificial Intelligence to predict the Degradation of Potential mRNA Vaccines Developed To Treat SARS-CoV-2.- An Application of Transfer Learning: Fine-Tuning BERT for Spam Email Classification.- MMAP : A Multi-Modal Automated Online Proctor.- Applying Extreme Gradient Boosting for Surface EMG based Sign Language recognition.- Review of Security Aspects of 51 Percent Attack on Blockchain.- Integrated Micro-video Recommender based on Hadoop and Web-Scrapper.- Automated Sleep Staging System based on Ensemble Learning Model using Single-Channel EEG signal.- Segregation and User Interactive Visualization of Covid- 19 Tweets using Text Mining Techniques.- Software Fault Prediction using Data Mining Techniques on Software Metrics.

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Book SynopsisThis book presents the general objective of the REV2021 conference which is to contribute and discuss fundamentals, applications, and experiences in the field of Online and Remote Engineering, Virtual Instrumentation, and other related new technologies like Cross Reality, Data Science & Big Data, Internet of Things & Industrial Internet of Things, Industry 4.0, Cyber Security, and M2M & Smart Objects. Nowadays, online technologies are the core of most fields of engineering and the whole society and are inseparably connected, for example, with Internet of Things, Industry 4.0 & Industrial Internet of Things, Cloud Technologies, Data Science, Cross & Mixed Reality, Remote Working Environments, Online & Biomedical Engineering, to name only a few.Since the first REV conference in 2004, we tried to focus on the upcoming use of the Internet for engineering tasks and the opportunities as well as challenges around it. In a globally connected world, the interest in online collaboration, teleworking, remote services, and other digital working environments is rapidly increasing. Another objective of the conference is to discuss guidelines and new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning, MOOCs & MOOLs, and Open Resources.REV2021 on "Online Engineering and Society 4.0" was the 17th in a series of annual events concerning the area of Remote Engineering and Virtual Instrumentation. It has been organized in cooperation with the International Engineering and Technology Institute (IETI) as an online event from February 24 to 26, 2021.Table of ContentsOn the Development of a Unified Remote Laboratory Framework.- GOLDi 2.0: Beyond Raw Digital Signals – Electrical Interface Emulation.- A Reliable Real-time Web Interface for an Online Laboratory.- Remote Labs For Communications.- Automated Testing for Sustainable Remote Laboratory System.- Interactive Lab Experimentation And Simulation Tools For Remote Laboratories.- Aligning Technic with Didactic – A Remote Laboratory Infrastructure for Study, Teaching and Research.- Simulation on Motion of A Trebuchet.- Human-Centered Design in Online Laboratories for Graduate Engineering Students.

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Book SynopsisThese lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches. Table of ContentsUncertainty and Decisions.- Prior and Likelihood Representation.- Graphical Modeling.- Parametric Models.- Computational Inference.- Bayesian Software Packages.- Model choice.- Linear Models.- Nonparametric Models.- Nonparametric Regression.- Clustering and Latent Factor Models.- Conjugate Parametric Models.

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Book SynopsisThis book presents findings from the papers accepted at the Cyber Security Education Stream and Cyber Security Technology Stream of The National Cyber Summit’s Research Track, reporting on latest advances on topics ranging from software security to cyber-attack detection and modelling to the use of machine learning in cyber security to legislation and policy to surveying of small businesses to cyber competition, and so on. Understanding the latest capabilities in cyber security ensures users and organizations are best prepared for potential negative events. This book is of interest to cyber security researchers, educators and practitioners, as well as students seeking to learn about cyber security.Table of ContentsPart I – Cyber Security EducationAn Integrated System for Connecting Cybersecurity Competency, Student Activities and Career Building Li-Chiou Chen, Andreea Cotoranu, Praviin Mandhare and Darren Hayes Simulating Industrial Control Systems using Node-RED and Unreal Engine 4 Steven Day, William Smallwood and Joshua Kuhn Student Educational Learning Experience Through Cooperative Research Melissa Hannis, Idongesit Mkpong-Ruffin and Drew Hamilton Digital Forensics Education: Challenges and Future Opportunities Megan Stigall and Kim-Kwang Raymond Choo Designing a Cybersecurity Curriculum Library: Best Practices from Digital Library Research Blair Taylor, Sidd Kaza and Melissa Dark Design of a Virtual Cybersecurity Escape Room Tania Williams and Omar El-Gayar Part II – Cyber Security Technology A Novel Method for the Automated Generation for JOP Chain Exploits Bramwell Brizendine, Austin Babcock and Josh Stroschien Increasing Log Availability in Unmanned Vehicle Systems Nicholas Carter, Peter Pommer, Duane Davis and Cynthia Irvine Testing Detection of K-Ary Code Obfuscated by Metamorphic and Polymorphic Techniques George Harter and Neil Rowe Enhancing Secure Coding Assistant System with Design by Contract and Programming Logic Wenhui Liang, Cui Zhang and Jun Dai Social Engineering Attacks in Healthcare Systems: A Survey Christopher Nguyen, Walt Williams, Brandon Didlake, Donte Mitchell, James McGinnis and Dipankar Dasgupta Identifying Anomalous Industrial-Control-System Network Flow Activity Using Cloud Honeypots Neil Rowe, Thuy Nguyen, Jeffrey Dougherty, Matthew Bieker and Darry Pilkington Risks of Electric Vehicle Supply Equipment Integration within Building Energy Management System Environments: A Look at Remote Attack Surface and Implications Roland Varriale, Michael Jaynes and Ryan Crawford

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Book SynopsisThis book introduces higher-degree research students and early career academics to scientific research as occurring in the field of information systems and adjacent fields, such as computer science, management science, organization science, and software engineering. Instead of focusing primarily on research methods as many other textbooks do, it covers the entire research process, from start to finish, placing particular emphasis on understanding the cognitive and behavioural aspects of research, such as motivation, modes of inquiry, theorising, planning for research, planning for publication, and ethical challenges in research. Comprehensive but also succinct and compact, the book guides beginning researchers in their quest to do scholarly work and to assist them in developing their own answers and strategies over the course of their work. Jan Recker explains in this book the fundamental concepts that govern scientific research and then moves on to introduce the basic steps every researcher undertakes: choosing research questions, developing theory, building a research design, employing research methods, and finally writing academic papers. He also covers essentials of ethical conduct of scientific research. This second edition contains major updates on all these elements plus significant expansions on relevant research methods such as design research and computational methods, a rewritten and extended chapter on theory development, and expansions to the chapters on research methods, scientific publishing, and research ethics. A companion website provides pedagogical materials and instructions for using this book in teaching.Table of ContentsPart I: Basic Principles of Research.- Introduction.- Information Systems Research as a Science.- Part II: Conducting Research.- Planning Your Research.- Theorising.- Research Methods.- Part III: Publishing Research.- Writing IS Research Articles.- Ethical Considerations in Research.- Concluding Remarks.

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Book SynopsisThis book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems. Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry level textbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.Table of Contents1-Introduction to Mechanistic Data Science 2-Multimodal Data Generation and Collection 3-Optimization and Regression 4-Extraction of Mechanistic Features 5-Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models 6-Deep Learning for Regression and Classification 7-System and Design

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