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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Improved low-degree testing and its applications
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Exploiting an event-based infrastructure to develop complex distributed systems
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Optimization of queries with user-defined predicates
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Pipelining in multi-query optimization
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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ACM Transactions on Computer Systems (TOCS)
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Filtering of XML Documents for Selective Dissemination of Information
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
WebFilter: A High-throughput XML-based Publish and Subscribe System
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The VLDB Journal — The International Journal on Very Large Data Bases
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FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
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ICDE '02 Proceedings of the 18th International Conference on Data Engineering
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WWW '05 Proceedings of the 14th international conference on World Wide Web
S-ToPSS: semantic Toronto publish/subscribe system
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Content-based publish/subscribe systems allow matching the content of events with predicates in the subscriptions. However, most existing systems only allow a limited set of operators, such as comparison on primitive data types (string, integer, etc). In this paper, we consider a publish/subscribe system that supports more flexible events/subscriptions with the use of advanced, yet potentially expensive, matching operators. Examples of such operators are pattern recognizers on multimedia data and spatial operators on location data. We study a critical problem in these publish/subscribe systems, namely how to optimize the matching process for a large number of subscriptions. This is achieved by exploiting the overlap in the subscriptions and sharing the operator evaluation results whenever possible. We formulate the optimal subscription evaluation problem and show that it is NP-Hard. We propose an efficient d-approximation algorithm, where d is the maximum number of operators in one subscription, as well as a heuristic algorithm that can further improve the system performance in practice. Our experiment results show that the proposed algorithms can reduce the matching cost by up to 80%, as compared to a naive strategy that evaluates the subscriptions independently.