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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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ASPLOS IX Proceedings of the ninth international conference on Architectural support for programming languages and operating systems
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Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Mining based decision support multi-agent system for personalized e-healthcare service
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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Sensors are active everywhere. Enormous volumes of sensed events are sent over the data streams, while most of applications want to focus on events that would be curious. We propose a technique for mining periodicities and predicting its outliers from the stream. The key to our technique is a simple periodic pattern {\Delta x}t, derived from delta-time mining, or SUP(t, t+{\Delta x}t). We provide efficient algorithms for finding the highest support {\Delta x}t on a small and resource-limited sensor device. Our experiments will compare memory efficiency and accuracy, on a variety of event patterns, monogenesis, polygenesis, and semi-random.