NDLI: Reliable detection of episodes in event sequences

Content Provider	Springer Nature Link
Author	Gwadera, Robert Atallah, Mikhail J. Szpankowski, Wojciech
Copyright Year	2004
Abstract	Suppose one wants to detect bad or suspicious subsequences in event sequences. Whether an observed pattern of activity (in the form of a particular subsequence) is significant and should be a cause for alarm depends on how likely it is to occur fortuitously. A long-enough sequence of observed events will almost certainly contain any subsequence, and setting thresholds for alarm is an important issue in a monitoring system that seeks to avoid false alarms. Suppose a long sequence, T, of observed events contains a suspicious subsequence pattern, S, within it, where the suspicious subsequence S consists of m events and spans a window of size w within T. We address the fundamental problem: Is a certain number of occurrences of a particular subsequence unlikely to be generated by randomness itself (i.e. indicative of suspicious activity)? If the probability of an occurrence generated by randomness is high and an automated monitoring system flags it as suspicious anyway, then such a system will suffer from generating too many false alarms. This paper quantifies the probability of such an S occurring in T within a window of size w, the number of distinct windows containing S as a subsequence, the expected number of such occurrences, its variance, and establishes its limiting distribution that allows setting up an alarm threshold so that the probability of false alarms is very small. We report on experiments confirming the theory and showing that we can detect bad subsequences with low false alarm rate.
Starting Page	415
Ending Page	437
Page Count	23
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	7
Issue Number	4
e-ISSN	02193116
Language	English
Publisher	Springer London
Publisher Date	2005-05-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data mining Episode pattern matching Hidden pattern matching Overrepresented and Underrepresented patterns Probabilistic analysis Information Systems and Communication Service IT in Business
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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