Matching events in a content-based subscription system
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Design and evaluation of a wide-area event notification service
ACM Transactions on Computer Systems (TOCS)
Efficient filtering in publish-subscribe systems using binary decision diagrams
ICSE '01 Proceedings of the 23rd International Conference on Software Engineering
Forwarding in a content-based network
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
An Efficient Multicast Protocol for Content-Based Publish-Subscribe Systems
ICDCS '99 Proceedings of the 19th IEEE International Conference on Distributed Computing Systems
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Finding optimal satisficing strategies for and-or trees
Artificial Intelligence
Adaptive case management in the social enterprise
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
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Content-based Publish/Subscribe (CPS) systems can efficiently deliver messages to large numbers of subscribers with diverse interests and consequently, have often been considered an appropriate technology for large-scale, event-based applications. In fact, a significant amount of existing research addresses the issue of providing scalable CPS services [3, 8, 7, 11]. In these approaches, scalability and high performance matching is often achieved by taking advantage of similarities between subscriptions. However, even though such optimization techniques are widely used, no model has been developed yet to capture them. Such an abstraction would allow CPS matching algorithms to be studied, analyzed, and optimized at a more fundamental and formal level. In this work-in-progress paper, we present the initial results of our work towards modelling and analyzing matching optimizations frequently used by CPS systems. Using our proposed model, we find that probabilistically optimal CPS matching is possible in certain types of subscription sets and that there is also a non-obvious upper bound on the expected cost of some subscription sets. We also provide experimental results that support the model proposed and studied in this paper.