Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Filtering algorithms and implementation for very fast publish/subscribe systems
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
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)
Introduction to Algorithms
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Query Merging: Improving Query Subscription Processing in a Multicast Environment
IEEE Transactions on Knowledge and Data Engineering
Clustering Algorithms for Content-Based Publication-Subscription Systems
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
The many faces of publish/subscribe
ACM Computing Surveys (CSUR)
Forwarding in a content-based network
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Epidemic Algorithms for Reliable Content-Based Publish-Subscribe: An Evaluation
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Subscription Summarization: A New Paradigm for Efficient Publish/Subscribe Systems
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Meghdoot: content-based publish/subscribe over P2P networks
Proceedings of the 5th ACM/IFIP/USENIX international conference on Middleware
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Semi-Probabilistic Content-Based Publish-Subscribe
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Chained forests for fast subsumption matching
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
The arbitrary Boolean publish/subscribe model: making the case
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
Approximate Covering Detection among Content-Based Subscriptions Using Space Filling Curves
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Bloom filter based routing for content-based publish/subscribe
Proceedings of the second international conference on Distributed event-based systems
Subscription subsumption evaluation for content-based publish/subscribe systems
Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware
Efficient probabilistic subsumption checking for content-based publish/subscribe systems
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
Thrifty privacy: efficient support for privacy-preserving publish/subscribe
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Approximate covering detection among content-based subscriptions using space filling curves
Journal of Parallel and Distributed Computing
DYNATOPS: a dynamic topic-based publish/subscribe architecture
Proceedings of the 7th ACM international conference on Distributed event-based systems
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One of the main challenges faced by content-based publish/subscribe systems is handling large amount of dynamic subscriptions and publications in a multidimensional content space. To reduce subscription forwarding load and speed up content matching, subscription covering, subsumption and merging techniques have been proposed. In this paper we propose MICS, Multidimensional Indexing for Content Space that provides an efficient representation and processing model for large number of subscriptions and publications. MICS creates a one dimensional representation for publications and subscriptions using Hilbert space filling curve. Based on this representation, we propose novel content matching and subscription management (covering, subsumption and merging) algorithms. Our experimental evaluation indicates that the proposed approach significantly speeds up subscription management operations compared to the naive linear approach.