Muutke küpsiste eelistusi

E-raamat: Event Mining: Algorithms and Applications

Edited by (Florida International University, Miami, USA)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 58,49 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.
Preface xiii
List of Figures
xv
List of Tables
xxi
Editor xxiii
Contributors xxv
1 Introduction
1(10)
Tao Li
1.1 Data-Driven System Management
1(4)
1.2 Overview of the Book
5(1)
1.3 Content of the Book
6(3)
1.4 Conclusion
9(2)
I Event Generation and System Monitoring
11(58)
2 Event Generation: From Logs to Events
13(36)
Liang Tang
Tao Li
2.1
Chapter Overview
14(3)
2.2 Log Parser
17(2)
2.3 Log Message Classification
19(2)
2.4 Log Message Clustering
21(3)
2.5 Tree-Structure-Based Clustering
24(7)
2.6 Message-Signature-Based Event Generation
31(15)
2.7 Summary
46(1)
2.8 Glossary
46(3)
3 Optimizing System Monitoring Configurations
49(20)
Liang Tang
Tao Li
3.1
Chapter Overview
49(1)
3.2 Automatic Monitoring
50(4)
3.3 Eliminating False Positives
54(3)
3.4 Eliminating False Negatives
57(3)
3.5 Evaluation
60(6)
3.6 Summary
66(1)
3.7 Glossary
66(3)
II Pattern Discovery and Summarization
69(124)
4 Event Pattern Mining
71(52)
Chunqiu Zeng
Tao Li
4.1 Introduction
72(2)
4.2 Sequential Pattern
74(6)
4.3 Fully Dependent Pattern
80(2)
4.4 Partially Periodic Dependent Pattern
82(6)
4.5 Mutually Dependent Pattern
88(3)
4.6 T-Pattern
91(5)
4.7 Frequent Episode
96(2)
4.8 Event Burst
98(3)
4.9 Rare Event
101(1)
4.10 Correlated Pattern between Time Series and Event
102(2)
4.11 A Case Study
104(16)
4.12 Conclusion
120(1)
4.13 Glossary
120(3)
5 Mining Time Lags
123(28)
Chunqiu Zeng
Liang Tang
Tao Li
5.1 Introduction
123(2)
5.2 Non-Parametric Method
125(7)
5.3 Parametric Method
132(8)
5.4 Empirical Studies
140(9)
5.5 Summary
149(1)
5.6 Glossary
149(2)
6 Log Event Summarization
151(42)
Yexi Jiang
Tao Li
6.1 Introduction
152(5)
6.2 Summarizing with Frequency Change
157(8)
6.3 Summarizing with Temporal Dynamics
165(14)
6.4 Facilitating the Summarization Tasks
179(12)
6.5 Summary
191(1)
6.6 Glossary
192(1)
III Applications
193(90)
7 Data-Driven Applications in System Management
195(62)
Wubai Zhou
Chunqiu Zeng
Liang Tang
Tao Li
7.1 System Diagnosis
196(5)
7.2 Searching Similar Sequential Textual Event Segments
201(22)
7.3 Hierarchical Multi-Label Ticket Classification
223(13)
7.4 Ticket Resolution Recommendation
236(17)
7.5 Summary
253(2)
7.6 Glossary
255(2)
8 Social Media Event Summarization Using Twitter Streams
257(26)
Chao Shen
Tao Li
8.1 Introduction
258(2)
8.2 Problem Formulation
260(2)
8.3 Tweet Context Analysis
262(3)
8.4 Sub-Event Detection Methods
265(5)
8.5 Multi-Tweet Summarization
270(1)
8.6 Experiments
271(10)
8.7 Conclusion and Future Work
281(1)
8.8 Glossary
281(2)
Bibliography 283(22)
Index 305
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.