Mining Serial Episode Rules with Time Lags over Multiple Data Streams

Authors:
Tung-Ying Lee;En Tzu Wang;Arbee L. Chen
Affiliations:
Department of Computer Science, National Tsing Hua University, Hsinchu, R.O.C.;Department of Computer Science, National Tsing Hua University, Hsinchu, R.O.C.;Department of Computer Science, National Chengchi University, Taipei, R.O.C.
Venue:
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Year:
2008

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Abstract

The problem of discovering episode rulesfrom static databases has been studied for years due to its wide applications in prediction. In this paper, we make the first attempt to study a special episode rule, named serial episode rule with a time lagin an environment of multiple data streams. This rule can be widely used in different applications, such as traffic monitoring over multiple car passing streams in highways. Mining serial episode rules over the data stream environment is a challenge due to the high data arrival rates and the infinite length of the data streams. In this paper, we propose two methods considering different criteria on space utilization and precision to solve the problem by using a prefix tree to summarize the data streams and then traversing the prefix tree to generate the rules. A series of experiments on real data is performed to evaluate the two methods.