Event Mining for Explanatory Modeling

• Record Nr.: UNINA9911046718503321
• Autore: Jalali Laleh
• Pubbl/distr/stampa: San Rafael : , : Morgan & Claypool Publishers, , 2021; ©2021
• ISBN: 9781450384858; 1450384854
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (163 pages)
• Collana: ACM Bks.
• Altri autori (Persone): JainRamesh
• Disciplina: 003.3
• Soggetti: Computer simulation; Data logging; Data mining
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Event Mining for Explanatory Modeling -- Contents -- Preface -- 1 Introduction -- 1.1 Correlation is the Mother of Causality -- 1.2 Explanatory Modeling versus Predictive Modeling -- 1.3 Logs are the Source of Knowledge -- 1.4 From Logs to Chronicles to Models -- 1.5 The Importance of an Event Language for Explanatory Modeling -- 2 Think Events: From Signals to Events -- 2.1 Events in the Human World -- 2.2 Events in the Cyber World -- 2.3 Why an Event Model? -- 2.4 An Overview of Event Models -- 3 Event Mining and Pattern Discovery -- 3.1 An Example of Asthma Risk Factor Patterns -- 3.2 Temporal Knowledge Representation -- 3.3 Temporal Data Prediction -- 3.3.1 Sequence Classification -- 3.3.2 Sequence Clustering -- 3.4 Pattern Discovery -- 3.4.1 Association Rule Mining -- 3.4.2 Sequence Mining -- 3.4.3 Frequent Episode Mining -- 3.5 Different Types of Patterns -- 3.5.1 T-Patterns -- 3.5.2 Cyclic Patterns -- 3.5.3 Sequential Patterns with Time Constraints -- 3.6 Revisiting Asthma Risk Factor Patterns -- 4 Design Principles of Event Mining Systems -- 4.1 Data Fusion and Transformation -- 4.2 Extensibility and Reusability -- 4.3 Interactive Process -- 4.4 Human-centered Analysis -- 4.5 Event Mining Architecture -- 5 Event Mining Applications -- 5.1 Healthcare and Medicine -- 5.1.1 Exploratory Techniques -- 5.1.2 Predictive ; Techniques -- 5.2 Biological Data Analysis -- 5.3 Predictive Maintenance -- 5.4 Business Intelligence -- 5.5 Computer Networks -- 6 EventMiner Framework -- 6.1 Data Models and Pattern Operators -- 6.1.1 Time Model -- 6.1.2 Event Model -- 6.1.3 Hypotheses-driven Operators -- 6.1.3.1 Selection Operation (ρ.P) -- 6.1.3.2 Sequence Operation (ρ1 -- ρ2) -- 6.1.3.3 Conditional Sequence Operation (ρ1 -- ωΔt1 ρ2) -- 6.1.3.4 Concurrency Operation (ρ1 ⊥⊥ ρ2) -- 6.1.3.5 Alternation (ρ1 | ρ2) -- 6.1.3.6 Time (ωτ ρ).; 6.1.4 Data-driven Operators -- 6.1.4.1 Sequential Co-occurrence SEQ_CO[Δt](ES, ES′) -- 6.1.4.2 Concurrent Co-occurrence CON_CO(ES, ES′) -- 6.2 Architecture -- 6.3 Core Processing and Language Syntax -- 6.4 Interactive Event Mining Process -- 6.5 Case Studies with EventMiner -- 6.5.1 Asthma Risk Management -- 6.5.1.1 Motivation of the Study -- 6.5.1.2 Applying EventMiner -- 6.5.1.3 Data Pre-processing -- 6.5.1.4 Topic Modeling -- 6.5.1.5 Environmental Event Stream Modeling -- 6.5.1.6 Data-driven Risk Factor Recognition -- 6.5.2 Objective Self: One Step Beyond Quantified Self -- 6.5.2.1 Quantified Self -- 6.5.2.2 Objective Self Has Arrived -- 6.5.2.3 An Architecture for Objective Self -- 6.5.2.4 Life Event Recognition -- 6.5.2.5 Formal Concept Analysis -- 6.5.2.6 Co-occurrence Behavior Patterns -- 6.5.2.7 Processing Co-occurrence Patterns -- 6.5.2.8 Data Collection -- 6.5.2.9 Sequential Co-occurrence: Commute Behavior and Activity Trends -- 6.5.2.10 Concurrent Co-occurrence: Multitasking Behavior -- 6.5.2.11 Patterns Across a Group of Users -- 6.5.2.12 The Effect of Environmental Factors on Behavior -- 7 Conclusion and Future Direction -- Bibliography -- Authors' Biographies -- Index.
• Sommario/riassunto: The book is intended as a primer on Event Mining for data-enthusiasts and information professionals interested in employing these event-based data analysis techniques in diverse applications. The reader is introduced to frameworks for temporal knowledge representation and reasoning, as well as temporal data mining and pattern discovery. Also discussed are the design principles of event mining systems. The approach is reified by the presentation of an event mining system called EventMiner, a computational framework for building explanatory models. The book contains case studies of using EventMiner in asthma risk management and an architecture for the objective self. The text can be used by researchers interested in harnessing the value of heterogeneous big data for designing explanatory event-based models in diverse application areas such as healthcare, biological data analytics, predictive maintenance of systems, computer networks, and business intelligence.