Event mining : algorithms and applications / edited by Tao Li.Material type: TextSeries: Chapman & Hall/CRC data mining and knowledge discovery seriesPublication details: New York, CRC Press, c2016.Description: xxv, 308 pages, 8 unnumbered pages of plates : illustrations (some color) ; 25 cmISBN:
- 006.3/12 23
- QA76.9.D343 E983 2016
|Item type||Current library||Call number||Status||Date due||Barcode|
|General Books||CUTN Central Library Sciences||006.3/12 (Browse shelf(Opens below))||Available||25831|
Browsing CUTN Central Library shelves, Shelving location: Sciences Close shelf browser (Hides shelf browser)
|005.8 KAH Cryptography and network security||006.3 Statistical mechanics of learning /||006.3/12 Data mining with R :||006.3/12 Event mining :||006.3 KON Artificial intelligence and soft computing :||006.301 GAV Generalized Voronoi diagram :||006.31 Machine learning :|
Includes bibliographical references (pages 283-303) and index.
1. Introduction / Tao Li -- 2. Event generation: from logs to events / Liang Tang and Tao Li -- 3. Optimizing system monitoring configurations / Liang Tang and Tao Li -- 4. Event pattern mining / Chunqui Zeng and Tao Li -- 5. Mining time lags / Chungiu Zeng, Liang Tang, and Tao Li -- 6. Log event summarization / Yexi Jiang and Tao Li -- 7. Data-driven applications in system management / Wubai Zhou, Chunqiu Zeng, Liang Tang, and Tao Li -- 8. Social media event summarization using twitter streams / Chao Shen and Tao Li.
"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 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 if you are not familiar with the topics and a comprehensive reference if you are already working in this area"--Back cover.
There are no comments on this title.