Updating derived relations: detecting irrelevant and autonomously computable updates
ACM Transactions on Database Systems (TODS)
TREAT: a new and efficient match algorithm for AI production systems
TREAT: a new and efficient match algorithm for AI production systems
Continuous queries over append-only databases
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Information Monitoring on the Web: A Scalable Solution
World Wide Web
Starburst Mid-Flight: As the Dust Clears
IEEE Transactions on Knowledge and Data Engineering
A Rule Engine for Query Transformation in Starburst and IBM DB2 C/S DBMS
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
An Algorithmic Basis for Integrating Production Systems and Large Databases
Proceedings of the Sixth International Conference on Data Engineering
Measuring the Complexity of Join Enumeration in Query Optimization
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Alert: An Architecture for Transforming a Passive DBMS into an Active DBMS
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Design and Evaluation of Alternative Selection Placement Strategies in Optimizing Continuous Queries
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Predicate indexing for incremental multi-query optimization
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Incremental aggregation on multiple continuous queries
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Applying incremental graph transformation to existing models in relational databases
ICGT'12 Proceedings of the 6th international conference on Graph Transformations
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Efficient processing of complex streaming data presents multiple challenges, especially when combined with intelligent detection of hidden anomalies in real time. We label such systems Stream Anomaly Monitoring Systems (SAMS), and describe the CMU/Dynamix ARGUS system as a new kind of SAMS to detect rare but high value patterns combining streaming and historical data. Such patterns may correspond to hidden precursors of terrorist activity, or early indicators of the onset of a dangerous disease, such as a SARS outbreak. Our method starts from an extension of the RETE algorithm for matching streaming data against multiple complex persistent queries, and proceeds beyond to transitivity inferences, conditional intermediate result materialization, and other such techniques to obtain both accuracy and efficiency, as demonstrated by the evaluation results outperforming classical techniques such as a modern DMBS.