Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
History-less Checking of Dynamic Integrity Constraints
Proceedings of the Eighth International Conference on Data Engineering
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
XPath queries on streaming data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Frequent Episode Rules for Internet Anomaly Detection
NCA '04 Proceedings of the Network Computing and Applications, Third IEEE International Symposium
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
A Tree-Based Approach for Event Prediction Using Episode Rules over Event Streams
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Episode rule-based prognosis applied to complex vacuum pumping systems using vibratory data
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Data-driven prognosis applied to complex vacuum pumping systems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
On-line rule matching for event prediction
The VLDB Journal — The International Journal on Very Large Data Bases
Frequent episode mining within the latest time windows over event streams
Applied Intelligence
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Predicting future events has great importance in many applications. Generally, rules with predicate events and consequent events are mined out, and then current events are matched with the predicate ones to predict the occurrence of consequent events. Many previous works focus on the rule mining problem; however, little emphasis has been attached to the problem of predicate events matching. As events often arrive in a stream, how to design an efficient and effective event predictor becomes challenging. In this paper, we give a clear definition of this problem and propose our own method. We develop an event filter and incrementally maintain parts of the matching results. By running a series of experiments, we show that our method is efficient and effective in the stream environment.