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Published Articles >> Table of Contents >> Abstract
17th International Conference on Data Engineering (ICDE'01)
p. 0205
Mining Partially Periodic Event Patterns with Unknown Periods
Sheng Ma, IBM T.J. Watson Research Center
Joseph L. Hellerstein, IBM T.J. Watson Research Center
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2001.914829
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Abstract: Periodic behavior is common in real-world applications. However; in many cases, periodicities are partial in that they are present only intermittently. Herein, we study such intermittent patterns, which we refer to as p-pattems. Our formulation of p-patterns takes into account imprecise time information ( e.g., due to unsynchronized clocks in distributed environments), noisy data (e.g., due to extraneous events), and shifts in phase and/or periods. We structure mining for p-patterns as two sub-tasks: (1) finding the periods of p-patterns and (2) mining temporal associations. For (2), a level-wise algorithm is used. For (1), we develop a novel approach based on a chi-squared test, and study its performance in the presence of noise. Further; we develop two algorithms for mining p-patterns based on the order in which the aforementioned sub-tasks are performed: the period-first algorithm and the association-first algorithm. Our resuits show that the association-first algorithm has a higher tolerance to noise; the period-first algorithm is more computationally efficient and provides flexibility as to the specification of support levels. In addition, we apply the period-first algorithm to mining data collected from two production computer networks, a process that led to several actionable insights.
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Citation:
Sheng Ma, Joseph L. Hellerstein,
"Mining Partially Periodic Event Patterns with Unknown Periods,"
icde,
p. 0205,
17th International Conference on Data Engineering (ICDE'01),
2001
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