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With a focus on computing system management, this book presents a variety of event mining approaches for improving the quality and efficiency of IT service and system management. It covers different components in the data-driven framework, from system monitoring and event generation to pattern discovery and summarization. The book explores recent developments in event mining, such as new clustering-based approaches, as well as various applications of event mining, including social media.
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.
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