Computer science Books

Book SynopsisData is becoming the world's new "natural resource," transforming industries and professions across the board. Smart, innovative organizations know that data is the new basis of gaining a competitive advantage. DB2 for z/OS remains the leading database for storing mission-critical data. This book explains how DB2 for z/OS and supporting products enable businesses to use their data to gain a competitive advantage.

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Book SynopsisThis book introduces technology and business professionals to IBM's cloud-computing product offerings, including IBM PureApplication System, Bluemix, and SoftLayer. IT professionals and decision makers will learn concepts they need to prepare them to develop, deploy, and manage business applications in an IBM cloud environment. The primer teaches novice to intermediate users how to work with private, public, and hybrid clouds; employ cloud software patterns; and architect and orchestrate cloud topologies. This book is particularly geared toward people working in IT organizations that are ready to implement cloud solutions or are considering adopting the cloud platform. The core cloud topics—development, deployment, provisioning, operations, monitoring, self-service, and APIs—are discussed with hands-on, real-world examples. No other cloud book covers the spectrum of topics that truly show Platform as a Service (PaaS) as a viable and cost-effective option. You’ll learn the essence of the cloud platform: rapid deployment of enterprise-level software in a secure environment.

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Book SynopsisThe concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far. Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata – supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes. A variety of case studies are also presented, thus providing the reader with practical examples of data lake management.Table of ContentsPreface xi Chapter 1. Introduction to Data Lakes: Definitions and Discussions 1Anne LAURENT, Dominique LAURENT and Cédrine MADERA 1.1. Introduction to data lakes 1 1.2. Literature review and discussion 3 1.3. The data lake challenges 7 1.4. Data lakes versus decision-making systems 10 1.5. Urbanization for data lakes 13 1.6. Data lake functionalities 17 1.7. Summary and concluding remarks 20 Chapter 2. Architecture of Data Lakes 21Houssem CHIHOUB, Cédrine MADERA, Christoph QUIX and Rihan HAI 2.1. Introduction 21 2.2. State of the art and practice 25 2.2.1. Definition 25 2.2.2. Architecture 25 2.2.3. Metadata 26 2.2.4. Data quality 27 2.2.5. Schema-on-read 27 2.3. System architecture 28 2.3.1. Ingestion layer 29 2.3.2. Storage layer 31 2.3.3. Transformation layer 32 2.3.4. Interaction layer 33 2.4. Use case: the Constance system 33 2.4.1. System overview 33 2.4.2. Ingestion layer 35 2.4.3. Maintenance layer 35 2.4.4. Query layer 37 2.4.5. Data quality control 38 2.4.6. Extensibility and flexibility 38 2.5. Concluding remarks 39 Chapter 3. Exploiting Software Product Lines and Formal Concept Analysis for the Design of Data Lake Architectures 41Marianne HUCHARD, Anne LAURENT, Thérèse LIBOUREL, Cédrine MADERA and André MIRALLES 3.1. Our expectations 41 3.2. Modeling data lake functionalities 43 3.3. Building the knowledge base of industrial data lakes 46 3.4. Our formalization approach 49 3.5. Applying our approach 51 3.6. Analysis of our first results 53 3.7. Concluding remarks 55 Chapter 4. Metadata in Data Lake Ecosystems 57Asma ZGOLLI, Christine COLLET† and Cédrine MADERA 4.1. Definitions and concepts 57 4.2. Classification of metadata by NISO 58 4.2.1. Metadata schema 59 4.2.2. Knowledge base and catalog 60 4.3. Other categories of metadata 61 4.3.1. Business metadata 61 4.3.2. Navigational integration 63 4.3.3. Operational metadata 63 4.4. Sources of metadata 64 4.5. Metadata classification 65 4.6. Why metadata are needed 70 4.6.1. Selection of information (re)sources 70 4.6.2. Organization of information resources 70 4.6.3. Interoperability and integration 70 4.6.4. Unique digital identification 71 4.6.5. Data archiving and preservation 71 4.7. Business value of metadata 72 4.8. Metadata architecture 75 4.8.1. Architecture scenario 1: point-to-point metadata architecture 75 4.8.2. Architecture scenario 2: hub and spoke metadata architecture 76 4.8.3. Architecture scenario 3: tool of record metadata architecture 78 4.8.4. Architecture scenario 4: hybrid metadata architecture 79 4.8.5. Architecture scenario 5: federated metadata architecture 80 4.9. Metadata management 82 4.10. Metadata and data lakes 86 4.10.1. Application and workload layer 86 4.10.2. Data layer 88 4.10.3. System layer 90 4.10.4. Metadata types 90 4.11. Metadata management in data lakes 92 4.11.1. Metadata directory 93 4.11.2. Metadata storage 93 4.11.3. Metadata discovery 94 4.11.4. Metadata lineage 94 4.11.5. Metadata querying 95 4.11.6. Data source selection 95 4.12. Metadata and master data management 96 4.13. Conclusion 96 Chapter 5. A Use Case of Data Lake Metadata Management 97Imen MEGDICHE, Franck RAVAT and Yan ZHAO 5.1. Context 97 5.1.1. Data lake definition 98 5.1.2. Data lake functional architecture 100 5.2. Related work 103 5.2.1. Metadata classification 104 5.2.2. Metadata management 105 5.3. Metadata model 106 5.3.1. Metadata classification 106 5.3.2. Schema of metadata conceptual model 110 5.4. Metadata implementation 111 5.4.1. Relational database 112 5.4.2. Graph database 115 5.4.3. Comparison of the solutions 119 5.5. Concluding remarks 121 Chapter 6. Master Data and Reference Data in Data Lake Ecosystems 123Cédrine MADERA 6.1. Introduction to master data management 125 6.1.1. What is master data? 125 6.1.2. Basic definitions 125 6.2. Deciding what to manage 126 6.2.1. Behavior 126 6.2.2. Lifecycle 127 6.2.3. Cardinality 127 6.2.4. Lifetime 128 6.2.5. Complexity 128 6.2.6. Value 128 6.2.7. Volatility 129 6.2.8. Reuse 129 6.3. Why should I manage master data? 130 6.4. What is master data management? 131 6.4.1. How do I create a master list? 136 6.4.2. How do I maintain a master list? 138 6.4.3. Versioning and auditing 139 6.4.4. Hierarchy management 140 6.5. Master data and the data lake 141 6.6. Conclusion 143 Chapter 7. Linked Data Principles for Data Lakes 145Alessandro ADAMOU and Mathieu D’AQUIN 7.1. Basic principles 145 7.2. Using Linked Data in data lakes 148 7.2.1. Distributed data storage and querying with linked data graphs 151 7.2.2. Describing and profiling data sources 153 7.2.3. Integrating internal and external data 156 7.3. Limitations and issues 159 7.4. The smart cities use case 162 7.4.1. The MK Data Hub 163 7.4.2. Linked data in the MK Data Hub 165 7.5. Take-home message 169 Chapter 8. Fog Computing 171Arnault IOUALALEN 8.1. Introduction 171 8.2. A little bit of context 171 8.3. Every machine talks 172 8.4. The volume paradox 173 8.5. The fog, a shift in paradigm 174 8.6. Constraint environment challenges 176 8.7. Calculations and local drift 177 8.7.1. A short memo about computer arithmetic 178 8.7.2. Instability from within 179 8.7.3. Non-determinism from outside 180 8.8. Quality is everything 181 8.9. Fog computing versus cloud computing and edge computing 184 8.10. Concluding remarks: fog computing and data lake 185 Chapter 9. The Gravity Principle in Data Lakes 187Anne LAURENT, Thérèse LIBOUREL, Cédrine MADERA and André MIRALLES 9.1. Applying the notion of gravitation to information systems 187 9.1.1. Universal gravitation 187 9.1.2. Gravitation in information systems 189 9.2. Impact of gravitation on the architecture of data lakes 193 9.2.1. The case where data are not moved 195 9.2.2. The case where processes are not moved 197 9.2.3. The case where the environment blocks the move 198 Glossary 201 References 207 List of Authors 217 Index 219

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Book SynopsisA major challenge in constraint programming is to develop efficient generic approaches to solve instances of the constraint satisfaction problem (CSP). With this aim in mind, this book provides an accessible synthesis of the author's research and work in this area, divided into four main topics: representation, inference, search, and learning. The results obtained and reproduced in this book have a wide applicability, regardless of the nature of the problem to be solved or the type of constraints involved, making it an extremely user-friendly resource for those involved in this field.Table of ContentsAcknowledgements 11 Notation 13 Main Acronyms 19 List of Algorithms 21 Introduction 27 Chapter 1. Constraint Networks 39 1.1 . Variables and constraints 39 1.2. Networks of variables and constraints . 51 1.2. Examples of constraint networks 65 1.4. Partial orders, decisions, nogoods and properties 74 1.5. Data structures to represent constraint networks 86 Chapter 2. Random and Structured Networks 93 2.1. Random constraint networks 94 2.2. Structured constraint networks 109 PART ONE. INFERENCE 133 Chapter 3. Consistencies 137 3.1. Basic consistencies 138 3.2. Stability of consistencies 143 3.3. Domain-filtering consistencies 150 3.4. Higher-order consistencies 162 3.5. Global consistency 173 3.6. Caveats about node, arc and path consistencies 184 Chapter 4. Generic GAC Algorithms 185 4.1.Coarse-grained propagation schemes 186 4.2. Iterating over valid tuples 97 4.3. GAC3 and GAC2001 200 4.4. More about general-purpose GAC algorithms 205 4.5. Improving the efficiency of generic GAC algorithms 214 4.6. Experimental results 233 4.7. Discussion 236 Chapter 5. Generalized Arc Consistency for Table Constraints 239 5.1. Classical schemes 240 5.2. Indexing-based approaches 244 5.3. Compression-based approaches 253 5.4. GAC-valid+allowed scheme 264 5.5. Simple tabular reduction 269 5.6. GACfor negative table constraints 279 5.7. Experimental results 283 5.8. Conclusion 286 Chapter 6. Singleton Arc Consistency 287 6.1. SAC1 and SAC2 289 6.2. SAC-Opt and SAC-SDS 290 6.3. SAC3 292 6.4. SAC3+ 296 6.5. Illustration 299 6.6. Weaker and stronger forms of SAC 300 6.7. Experimental results 313 6.8. Conclusion 316 Chapter 7. Path and Dual Consistency 319 7.1. Qualitative study 321 7.2. Enforcing (conservative) path consistency 331 7.3. Enforcing strong (conservative) dual consistency 336 7.4. Experimental results 348 7.5. Conclusion 353 PART TWO. SEARCH 355 Chapter 8. Backtrack Search 359 8.1. General description 361 8.2. Maintaining (generalized) arc consistency 367 8.3. Classical look-ahead and look-back schemes 370 8.4. Illustrations 378 8.5. The role of explanations 383 Chapter 9. Guiding Search toward Conflicts 391 9.1. Search-guiding heuristics 392 9.2. Adaptive heuristics 398 9.3. Strength of constraint weighting 405 9.4. Guiding search to culprit decisions 415 9.5. Conclusion 427 Chapter 10. Restarts and Nogood Recording 431 10.1. Restarting search 432 10.2. Nogood recording from restarts 436 10.3. Managing standard nogoods 441 10.4. Minimizing nogoods 450 10.5. Experimental results 454 10.6. Conclusion 457 Chapter 11. State-based Reasoning 459 11.1. Inconsistent partial states 460 11.2. Learning from explanations and failed values 470 11.3. Reducing elementary inconsistent partial states 476 11.4. Equivalence detection 487 11.5. Experimental results 492 11.6. Conclusion 494 Chapter 12. Symmetry Breaking 495 Christophe LECOUTRE, Sébastien TABARY 12.1. Group theory 496 12.2. Symmetries on constraint networks 499 12.3. Symmetry-breaking methods 503 12.4. Automatic symmetry detection 508 12.5. Lightweight detection of variable symmetries 511 12.6. A GAC algorithm for lexicographic ordering constraints 520 12.7. Experimental results 527 Appendices 531 Bibliography 547 Index 571

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Book SynopsisInformation systems have an enormous potential for improving business performance. With this in mind, companies must set out to exploit and optimize this potential without delay in order to improve their efficiency and continue to set themselves apart from the competition. This comprehensive text provides the information needed to understand and implement these systems at a practical level.Table of ContentsPreface xi Acknowledgements xv Chapter 1. Innovation for Business Value and Cost-killing 1 1.1. Supporting profit and growth 3 1.2. Assessing innovation 9 1.3. Agility and alignment 15 1.4. Sustainable development and information assets 24 Chapter 2. The Transverse Information System 29 2.1. A regular increase in power 30 2.2. Optimizing business unit assets 36 2.3. The impact on the IT department agenda 41 Chapter 3. Master Data 49 3.1. An unclaimed asset 50 3.2. Master data management: centralization 65 3.3. Enterprise information integration: federation 74 3.4. Between centralization and federation 78 3.5. Data governance 80 3.6. Towards information management 83 3.7. Bibliography 84 Chapter 4. Service-Oriented Architectures 85 4.1. Basic impacts 87 4.2. A major lever for a change in progress 93 4.3. A new experiment in the finance bank 102 4.4. Technologies and architecture 106 4.5. Flexibility is an event? Yes, agent! 118 Chapter 5. Business Process Management 121 5.1. From managing business processes to BPM 122 5.2. Understanding BPM 137 5.3. The business process from the IS perspective 147 5.4. BPM promises and prospects 155 5.5. Conclusion: the place of BPM in the company and in the IS 157 Chapter 6. Exchange Platforms 159 6.1. The development of data exchanges 159 6.2. Technologies and architectures 172 6.3. Project typology 185 6.4. A common foundation 188 Chapter 7. Complex, Innovative Business Architectures 189 7.1. Natural connections 190 7.2. An investigation into the distribution sector 198 7.3. A project in the energy sector 205 7.4. A program in retail banking 209 Chapter 8. The Impact of NISS on Software Implementation 219 8.1. The process standard 221 8.2. Towards assembly and beyond 225 8.3. Model-driven architecture and docking 241 8.4. A “sourcing” process to be defined 244 Chapter 9. From Implementation to Measurment 247 9.1. Towards operational excellence 248 9.2. Business activity monitoring 253 9.3. SOA management 269 9.4. The loop is completed 272 Chapter 10. Contribution and Impact of NISS on Organization 275 10.1. From the business unit to IT: a new fluency 276 10.2. Governance of information assets 283 10.3. Organization methods 297 10.4. Managing change and maturity stages 305 Chapter 11. How to Get the Best Out of NISS 307 11.1. The initial phases 307 11.2. The foundations: creating platforms 318 11.3. From the process angle 322 11.4. Here and now 328 11.5. Bibliography 329 Index 331

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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 SynopsisCrowdsourcing is a relatively recent phenomenon that only appeared in 2006, but it continues to grow and diversify (crowdfunding, crowdcontrol, etc.). This book aims to review this concept and show how it leads to the creation of value and new business opportunities. Chapter 1 is based on four examples: the online-banking sector, an informative television channel, the postal sector and the higher education sector. It shows that in the current context, for a company facing challenges, the crowd remains an untapped resource. The next chapter presents crowdsourcing as a new form of externalization and offers definitions of crowdsourcing. In Chapter 3, the authors attempt to explain how a company can create value by means of a crowdsourcing operation. To do this, authors use a model linking types of value, types of crowd, and the means by which these crowds are accessed. Chapter 4 examines in detail various forms that crowdsourcing may take, by presenting and discussing ten types of crowdsourcing operation. In Chapter 5, the authors imagine and explore the ways in which the dark side of crowdsourcing might be manifested and Chapter 6 offers some insight into the future of crowdsourcing. Contents 1. A Turbulent and Paradoxical Environment. 2. Crowdsourcing: A New Form of Externalization. 3. Crowdsourcing and Value Creation. 4. Forms of Crowdsourcing. 5. The Dangers of Crowdsourcing. 6. The Future of Crowdsourcing. About the Authors Jean-Fabrice Lebraty is Professor of management sciences at IAE (Business School) at Jean Moulin – Lyon 3 University in France and a member of the research laboratory Magellan EA3713. He specializes in the management of information and communication systems and his research notably concerns decision-making and the links between crowd and information technology. Katia Lobre-Lebraty is Associate Professor of management sciences at IAE (Business School) at Jean Moulin – Lyon 3 University in France and a member of the research laboratory Magellan EA3713. She specializes in management control and strategic management and her research concerns both the modes of governance of organizations and Open DataTable of ContentsINTRODUCTION ix I.1. A typology of management situations x I.2. Crowdsourcing: a multifaceted concept xii ACKNOWLEDGMENTS xv CHAPTER 1. A TURBULENT AND PARADOXICAL ENVIRONMENT 1 1.1. Economic financialization and its challenges 1 1.2. The mass diffusion of the Internet and its consequences 3 1.3. The paradoxical coexistence of scarcity and abundance around data 4 1.4. Unique simultaneity of crisis and immobilism 8 1.4.1. The online banking sector 9 1.4.2. The postal sector 10 1.4.3. The television sector 11 1.4.4. The training sector: French universities 12 1.4.5. The conclusion to be drawn from these cases: the crowd remains an underexploited resource 13 CHAPTER 2. CROWDSOURCING: A NEW FORM OF EXTERNALIZATION 15 2.1. The concept of externalization 17 2.2. The idea of relationships 18 2.3. The concept of a crowd 19 2.3.1. The connected crowd 21 2.3.2. Understanding the crowd 23 2.3.3. Crowds and experts 26 CHAPTER 3. CROWDSOURCING AND VALUE CREATION 29 3.1 Creation of value 30 3.2. What type of value? 34 3.3 What type of crowd? 35 3.4 Towards an adapted business model 40 CHAPTER 4. FORMS OF CROWDSOURCING 47 4.1. Crowdjobbing 49 4.1.1. What is it? 50 4.1.2. Why it works 51 4.1.3. Limitations 52 4.1.4. The future 53 4.2. Crowdwisdom 55 4.2.1. What is it? 56 4.2.2. Why it works 56 4.2.3. Limitations 57 4.2.4. The future 57 4.3. Crowdfunding 58 4.3.1. What is it? 58 4.3.2. An illustrative example 59 4.3.3. Why it works 61 4.3.4. Limitations 63 4.3.5. The future 64 4.4. Crowdsourcing and forecasting 65 4.4.1.What is it? 65 4.4.2. An illustrative example 66 4.4.3. Why it works 68 4.4.4. Limitations 68 4.4.5. The future 68 4.5. Crowdsourcing and innovation 69 4.5.1. What is it? 70 4.5.2. Why it works 72 4.5.3. Limitations 73 4.5.4. The future 75 4.6. Crowdsourcing and authenticity (C&A) 75 4.6.1. What is it? 76 4.6.2. Why it works 77 4.6.3. Limitations 78 4.6.4. The future 79 4.7. Crowdauditing 80 4.7.1. What is it? 81 4.7.2. Why it works 82 4.7.3. Limitations 83 4.7.4. The future 84 4.8. Crowdcontrol 85 4.8.1. What is it? 85 4.8.2. Why it works 86 4.8.3. Limitations 86 4.8.4. The future 87 4.9. Crowdcuration 88 4.9.1. What is it? 88 4.9.2. An illustrative example 89 4.9.3. Why it works 90 4.9.4. Limitations 91 4.9.5. The future 92 4.10. Crowdcare 93 4.10.1. What is it? 93 4.10.2. An illustrative example 94 4.10.3. Why it works 96 4.10.4. Limitations 96 4.10.5. The future 96 CHAPTER 5. THE DANGERS OF CROWDSOURCING 97 CHAPTER 6. THE FUTURE OF CROWDSOURCING 101 CONCLUSION 107 BIBLIOGRAPHY 109 INDEX 119

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Book SynopsisAutomatic Control with Interactive Tools is a textbook for undergraduate study of automatic control. Providing a clear course structure, and covering concepts taught in engineering degrees, this book is an ideal companion to those studying or teaching automatic control. The authors have used this text successfully to teach their students.By providing unique interactive tools, which have been designed to illustrate the most important automatic control concepts, Automatic Control with Interactive Tools helps students overcome the potential barriers presented by the significant mathematical content of automatic control courses. Even when they have previously had only the benefit of an introductory control course, the software tools presented will help readers to get to grips with the use of such techniques as differential equations, linear algebra, and differential geometry. This textbook covers the breadth of automatic control topics, including time responses of dynamic systems, the Nyquist criterion and PID control. It switches smoothly between analytical and practical approaches. Automatic Control with Interactive Tools offers a clear introduction to automatic control, ideal for undergraduate students, instructors and anyone wishing to familiarize themselves with the fundamentals of the subjectTable of Contents1. Introduction.- 2. From Nonlinear Physical Models to Linear Models.- 3. Time Response.- 4. Frequency Response.- 5. Relationship Between Model Parameters with Physical Models.- 6. Closed-Loop Systems and Stability.- 7. Control Systems Design.- 8. Control of Physical Systems.- 9. Introduction to Control Systems Design in State Space.

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Book SynopsisThis two-volume work focuses on the mathematical aspects of Darwinian evolution starting from the basic model of stochastic evolution of a single isolated locus in the presence of mutation to the multi–locus models of sexual and asexual populations. Volume 2 discusses the inference of fi tness landscape from DNA sequence data, discovery of the evolutionary roles of enygmatic traits, co-evolution of adversarial species, and various applications to virus evolution.

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Book SynopsisThe amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the "problems of time". The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public.

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