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Event Mining
Algorithms and Applications

By

Tao Li




ISBN 9781466568570
Published October 20, 2015 by Chapman and Hall/CRC
304 Pages 129 B/W Illustrations

 
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Book Description

Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management.

The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then shows how to extract useful knowledge from data. It describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets).

Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point for readers not familiar with the topics and a comprehensive reference for those already working in this area.

Table of Contents

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.

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Editor(s)

Biography

Dr. Tao Li is a professor and Graduate Program Director in the School of Computing and Information Sciences at Florida International University (FIU) and a professor in the School of Computer Science at Nanjing University of Posts and Telecommunication. He is on the editorial boards of ACM Transactions on Knowledge Discovery from Data, IEEE Transactions on Knowledge and Data Engineering, and Knowledge and Information System Journal. He has received numerous honors, including an NSF CAREER Award, IBM Faculty Research Awards, an FIU Excellence in Research and Creativities Award, and IBM Scalable Data Analytics Innovation Award and Mentorship Awards. His research interests are in data mining, information retrieval, and computing system management. He received a PhD in computer science from the University of Rochester.