| Autore |
Jalali Laleh
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
San Rafael : , : Morgan & Claypool Publishers, , 2021
|
| Descrizione fisica |
1 online resource (163 pages)
|
| Disciplina |
003.3
|
| Altri autori (Persone) |
JainRamesh
|
| Collana |
ACM Bks.
|
| Soggetto topico |
Computer simulation
Data logging
Data mining
|
| ISBN |
9781450384858
1450384854
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| 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.
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| Record Nr. | UNINA-9911046718503321 |
Info
Event Mining for Explanatory Modeling
