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Preface; Acknowledgments; Contents; A Machine Learning Perspective on Big Data Analysis; 1 Preliminaries; 2 What Do We Call Big Data Analysis?; 2.1 General Definitions of Big Data; 2.2 Machine Learning and Data Mining Versus Big Data Analysis; 2.3 Some Well-Known and Successful Applications of Big Data Analysis; 2.4 Machine Learning Innovations Driven by Big Data Analysis; 3 Is Big Data Analysis a Game Changer?; 3.1 Big Data Analysis and the Scientific Method; 3.2 Big Data Analysis and Society; 4 Edited Volume's Contributions; 4.1 Problem Centric Contributions
4.2 Domain Centric ContributionsReferences; An Insight on Big Data Analytics; 1 Introduction; 2 Is Big Data Fit for Purpose?; 2.1 Do We Need Big Data?; 2.2 What About Big Data Do We Need?; 3 Basic Toolbox for Analysing Big Data; 4 Dividing the Analytical Task Up into Manageable Chunks; 4.1 Generalised Linear Models Example; 4.2 Forecasting Counts in Complex Tabular Settings; 5 Reducing the Size of the Data that Needs to Be Modeled; 6 The Tension Between Data Mining and Statistics; 7 Does the New Big Data Initiative Need No Theory?; 8 Who Owns Big Data?; 9 Discussion; References
Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement1 Introduction; 2 Agent Language for Basic Tasks in BDT; 3 Postulates about Physical World and Agents; 3.1 Physical Character of Agents, C-Granule, and Interaction Models; 3.2 Efficiency Management of Task Realization by a Single Agent and Agent Society; 4 Interactive Granular Computing (IGrC); 5 Interactive Computations on Complex Granules Realized by Agents; 6 BDT and Problem Solving; 6.1 Problem Specification by Users
6.2 Construction and Discovery of Relevant Granules6.3 Risk Management by Agents in BDT; 6.4 Adaptive Judgement; 7 Conclusions; References; An Overview of Concept Drift Applications; 1 Introduction; 2 Knowledge Discovery Process and Industry Standards; 3 Categorization of Concept Drift Tasks and Applications; 3.1 Characterization of Application Tasks; 3.2 A Landscape of Concept Drift Application Areas; 4 An Overview of Application Oriented Studies on Learning from Evolving Data; 4.1 Monitoring and Control; 4.2 Information Management; 4.3 Analytics and Diagnostics; 5 Discussion and Conclusions
4.2 Domain Centric ContributionsReferences; An Insight on Big Data Analytics; 1 Introduction; 2 Is Big Data Fit for Purpose?; 2.1 Do We Need Big Data?; 2.2 What About Big Data Do We Need?; 3 Basic Toolbox for Analysing Big Data; 4 Dividing the Analytical Task Up into Manageable Chunks; 4.1 Generalised Linear Models Example; 4.2 Forecasting Counts in Complex Tabular Settings; 5 Reducing the Size of the Data that Needs to Be Modeled; 6 The Tension Between Data Mining and Statistics; 7 Does the New Big Data Initiative Need No Theory?; 8 Who Owns Big Data?; 9 Discussion; References
Toward Problem Solving Support Based on Big Data and Domain Knowledge: Interactive Granular Computing and Adaptive Judgement1 Introduction; 2 Agent Language for Basic Tasks in BDT; 3 Postulates about Physical World and Agents; 3.1 Physical Character of Agents, C-Granule, and Interaction Models; 3.2 Efficiency Management of Task Realization by a Single Agent and Agent Society; 4 Interactive Granular Computing (IGrC); 5 Interactive Computations on Complex Granules Realized by Agents; 6 BDT and Problem Solving; 6.1 Problem Specification by Users
6.2 Construction and Discovery of Relevant Granules6.3 Risk Management by Agents in BDT; 6.4 Adaptive Judgement; 7 Conclusions; References; An Overview of Concept Drift Applications; 1 Introduction; 2 Knowledge Discovery Process and Industry Standards; 3 Categorization of Concept Drift Tasks and Applications; 3.1 Characterization of Application Tasks; 3.2 A Landscape of Concept Drift Application Areas; 4 An Overview of Application Oriented Studies on Learning from Evolving Data; 4.1 Monitoring and Control; 4.2 Information Management; 4.3 Analytics and Diagnostics; 5 Discussion and Conclusions