Event Mining: Algorithms and Applications (Hardcover)

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.

事件挖掘涵蓋了從歷史事件/日誌數據中自動且高效地提取有價值知識的技術。因此，該領域在數據驅動的系統管理中扮演著重要角色。《事件挖掘：算法與應用》介紹了最新的事件挖掘方法和應用，重點關注計算系統管理。

本書首先解釋了如何將不同格式和內容的日誌數據轉換為規範形式，以及如何優化系統監控。然後展示了如何從數據中提取有用的知識。它描述了智能和高效的方法和算法，用於進行數據驅動的模式發現和問題確定，以管理複雜系統。本書還討論了用於詳細診斷系統問題的數據驅動方法，並探討了在Twitter消息（推文）中應用事件摘要的方法。

理解跨學科領域的事件挖掘可能具有挑戰性，因為它需要熟悉多個研究領域，相關文獻分散在不同的出版物中。本書通過為不熟悉這些主題的讀者提供良好的起點，以及為已在該領域工作的人提供全面的參考，使探索該領域變得更加容易。