Event Mining: Algorithms and Applications, 1st Edition (Hardback) book cover

Event Mining

Algorithms and Applications, 1st Edition

Edited by Tao Li

Chapman and Hall/CRC

304 pages | 129 B/W Illus.

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Hardback: 9781466568570
pub: 2015-10-20
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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


Tao Li

Data-Driven System Management

Overview of the Book

Content of the Book


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


Optimizing System Monitoring Configurations

Liang Tang and Tao Li

Chapter Overview

Automatic Monitoring

Eliminating False Positive

Eliminating False Negative



Pattern Discovery and Summarization

Event Pattern Mining

Chunqiu Zeng and Tao Li


Sequential Pattern

Fully Dependent Pattern

Partially Periodic Dependent Pattern

Mutually Dependent Pattern


Frequent Episode

Event Burst

Rare Event

Correlated Pattern between Time Series and Event

A Case Study


Mining Time Lags

Chunqiu Zeng, Liang Tang, and Tao Li


Nonparametric Method

Parametric Method

Empirical Studies


Log Event Summarization

Yexi Jiang and Tao Li


Summarizing with Frequency Changing

Summarizing with Temporal Dynamics

Facilitating the Summarization Tasks



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


Social Media Event Summarization Using Twitter Streams

Chao Shen and Tao Li


Problem Formulation

Tweet Context Analysis

Sub-Event Detection Methods

Multi-Tweet Summarization


Conclusion and Future Work

A Glossary appears at the end of each chapter.

About the Editor

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.

About the Series

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Programming / Games
COMPUTERS / Database Management / Data Mining
COMPUTERS / Machine Theory