Open Access
OEOP: A Novel Algorithm for Periodic Pattern Mining
Jieh-Shan Yeh,Szu-Chen Lin,Shueh-Cheng Hu +2 more
- 01 Jan 2012
1
TL;DR: This study proposed an efficient 2-D linked structure and the OEOP (One Event One Pattern) algorithm to discover all kinds of valid segments in each single event sequence, and combines these valid segments into 1-patterns with multiple events, and multiple patterns with multiple event periodic patterns.
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Abstract: Research on periodic pattern mining has gained a great attention in the past decade. Periodic pattern mining discovers valid periodic patterns in a time-related dataset. This study proposed an efficient 2-D linked structure and the OEOP (One Event One Pattern) algorithm to discover all kinds of valid segments in each single event sequence. Then, this study combines these valid segments found by OEOP into 1-patterns with multiple events, and multiple patterns with multiple events periodic patterns. The experimental results show that the proposed algorithm has good performance and scalability.
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Citations
Mining Periodic Workload Patterns in Database Audit Trails
TL;DR: This work shows how to detect the oscillations of database workloads caused by the periodical invocations of user applications by presenting an algorithm for discovering periodic patterns in the histories of processing of complex and elementary database operations.
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Mining asynchronous periodic patterns in time series data
Jiong Yang,Wei Wang,Philip S. Yu +2 more
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TL;DR: A more flexible model of asynchronous periodic pattern that may be present only within a subsequence and whose occurrences may be shifted due to disturbance is proposed and shown to provide linear time complexity with respect to the length of the sequence.
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SMCA: a general model for mining asynchronous periodic patterns in temporal databases
Kuo-Yu Huang,Chia-Hui Chang +1 more
TL;DR: A more general model of asynchronous periodic patterns from a sequence of symbol sets where a time slot can contain multiple events is proposed and good performance and scalability with large frequent patterns are demonstrated.