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Reliable Detection of Episodes in Event Sequences
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KWY is a Computer Scientist, Algorithm Analysis and Design Expert, Bioinformatics Expert, Information Theory, Multimedia Compression Consultant, Computer Systems and Computer Communication Networks Consultant with world-class expertise in analysis and design of algorithms, bioinformatics, information theory, multimedia compression, random structures, performance evaluation, analytic combinatorics, discrete mathematics, pattern matching, stability problems in distributed systems, modeling of computer systems and computer communication networks, queueing theory, and operations research
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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 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 fortuitous (i.e., indicative of suspicious activity)? If the probability of fortuitous occurrences 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 to set 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.
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Read other articles by this KKAI Associate:
A Framework for Self Manipulating Video Streams
2D-Pattern Matching Image and Video Compression: Theory, Algorithms, and Experiments
Detecting Conserved Interaction Patterns in Biological Networks
Error Resilient LZ'77 Data Compression: Algorithms, Analysis, and Experiments
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Kevin Kennedy & Associates, Inc.
Rapid Response Engineering® Solutions
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