The HiPAC project: combining active databases and timing constraints
ACM SIGMOD Record - Special Issue on Real-Time Database Systems
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Ode as an Active Database: Constraints and Triggers
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Composite Events for Active Databases: Semantics, Contexts and Detection
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CPM '97 Proceedings of the 8th Annual Symposium on Combinatorial Pattern Matching
Cayuga: a high-performance event processing engine
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
What is "next" in event processing?
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Cyber Physical Systems: Design Challenges
ISORC '08 Proceedings of the 2008 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Mining frequent k-partite episodes from event sequences
JSAI-isAI'09 Proceedings of the 2009 international conference on New frontiers in artificial intelligence
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a fast episode pattern matching engine EVIS that detects all occurrences in massively parallel data streams for an episode pattern, which represents a collection of event types in a given partial order. There should be important applications to be addressed with this technology, such as monitoring stock price movements, and tracking vehicles or merchandise by using GPS or RFID sensors. EVIS employs a variant of non-deterministic finite automata whose states are extended to maintain their activated times and activating streams. This extension allows EVIS's episode pattern to have 1) interval constraints that enforce time-bound conditions on every pair of consequent event types in the pattern, and 2) stream constraints by which two interested series of events are associated with each other and found in arbitrary pairs of streams. The experimental results show that EVIS performs much faster than a popular CEP engine for both artificial and real world datasets, as well as that EVIS effectively works for over 100,000 streams.