Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Composite Event Specification in Active Databases: Model & Implementation
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Aurora: a data stream management system
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Temporal management of RFID data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Continuously matching episode rules for predicting future events over event streams
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Towards expressive publish/subscribe systems
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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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Event prediction over event streams is an important problem with broad applications. For this problem, rules with predicate events and consequent events are given, and then current events are matched with the predicate events to predict future events. Over the event stream, some matches of predicate events may trigger duplicate predictions, and an effective scheme is proposed to avoid such redundancies. Based on the scheme, we propose a novel approach CBS-Tree to efficiently match the predicate events over event streams. The CBS-Tree approach maintains the recently arrived events as a tree structure, and an efficient algorithm is proposed for the matching of predicate events on the tree structure, which avoids exhaustive scans of the arrived events. By running a series of experiments, we show that our approach is more efficient than the previous work for most cases.