Paper Info
Title
A knowledge-rich similarity measure for improving IT incident resolution process
Abstract
The aim of incident management is to restore a given IT service disruption, simply called incident, to normal state as quickly as possible. In incident management, it is essential to resolve a new incident efficiently and accurately. However, typically, incident resolution process is largely manual, thus, it is time-consuming and error-prone. This paper proposes a new knowledge-rich similarity measure for improving this process. The role of this measure is to retrieve the most similar past incident cases for a new incident without human intervention. The solution information contained the retrieved incident cases can be utilized to resolve the new incident. The main feature of our similarity measure is to incorporate additional useful meta knowledge, outside of incident description that is the only exploited information in typical similarity measures used in CBR, to improve effectiveness. Moreover, this measure exploits as much semantic knowledge as possible about features contained in previous incident cases. Through an experimental evaluation, we show the effectiveness, technical coherence and feasibility of this measure using a real dataset.
Year
DOI
Venue
2010
10.1145/1774088.1774466
SAC
Keywords
Field
DocType
new knowledge-rich similarity measure,incident description,previous incident case,incident resolution process,new incident,similarity measure,it incident resolution process,measure exploit,similar past incident case,incident management,incident case,it incident management
Incident management (ITSM),Semantic memory,Data mining,Similarity measure,Information retrieval,Computer science,Normal state,Exploit,Coherence (physics),IT service management
Conference
Citations 
PageRank 
References 
8
0.55
14
Authors
4
Name
Order
Citations
PageRank
Yongbin Kang15510.61
Arkady Zaslavsky2113381.03
Shonali Priyadarsini Krishnaswamy31439104.01
Claudio Bartolini494486.00