Li Event Mining

Theory, Algorithms, and Applications
Erscheinungsjahr 2015
ISBN: 978-1-4665-6859-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 332 Seiten

Reihe: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

This book presents a variety of approaches and applications for using data mining and machine learning techniques in the context of event mining. It offers an introductory overview on recent developments and discusses the challenges and common tasks that need to be addressed in practical applications. The book extensively covers complex event processing, event mining and summarization, and applications in ITIL event management, intelligent cloud management, health care, and smart homes.

Zielgruppe

Researchers, practitioners, and graduate students interested in event mining, data mining, and machine learning.

Autoren/Hrsg.

Li, Tao

Fachgebiete

• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Introduction
Tao Li
Data-Driven System Management
Overview of the Book
Content of the Book
Conclusion

Event Generation and System Monitoring
Event Generation: From Logs to Events
Liang Tang and Tao Li
Chapter Overview
Log Parser
Log Message Classification
Log Message Clustering
Tree Structure-Based Clustering
Message Signature-Based Event Generation
Summary

Optimizing System Monitoring Configurations
Liang Tang and Tao Li
Chapter Overview
Automatic Monitoring
Eliminating False Positive
Eliminating False Negative
Evaluation
Summary

Pattern Discovery and Summarization
Event Pattern Mining
Chunqiu Zeng and Tao Li
Introduction
Sequential Pattern
Fully Dependent Pattern
Partially Periodic Dependent Pattern
Mutually Dependent Pattern
T-Pattern
Frequent Episode
Event Burst
Rare Event
Correlated Pattern between Time Series and Event
A Case Study
Conclusion

Mining Time Lags
Chunqiu Zeng, Liang Tang, and Tao Li
Introduction
Nonparametric Method
Parametric Method
Empirical Studies
Summary

Log Event Summarization
Yexi Jiang and Tao Li
Introduction
Summarizing with Frequency Changing
Summarizing with Temporal Dynamics
Facilitating the Summarization Tasks
Summary

Applications
Data-Driven Applications in System Management
Wubai Zhou, Chunqiu Zeng, Liang Tang, and Tao Li
System Diagnosis
Searching Similar Sequential Textual Event Segments
Hierarchical Multi-Label Ticket Classification
Tickets Resolution Recommendation
Summary

Social Media Event Summarization Using Twitter Streams
Chao Shen and Tao Li
Introduction
Problem Formulation
Tweet Context Analysis
Sub-Event Detection Methods
Multi-Tweet Summarization
Experiments
Conclusion and Future Work

A Glossary appears at the end of each chapter.