Modelling Uncertainties in Publish/Subscribe

Haifeng Liu and Hans-Arno Jacobsen.

In International Conference on Data Engineering (ICDE), pages 510-522, Boston, MA, June 2004.
Acceptance rate: 14 %. Number of submissions: 441.

Abstract

In the publish/subscribe paradigm, information providers disseminate publications to all consumers who have expressed interest by registering subscriptions. This paradigm has found wide-spread applications, ranging from selective information dissemination to network management. However, all existing publish/subscribe systems cannot capture uncertainty inherent to the information in either subscriptions or publications. In many situations, exact knowledge of either specific subscriptions or publications is not available. Moreover, especially in selective information dissemination applications, it is often more appropriate for a user to formulate her search requests or information offers in less precise terms, rather than defining a sharp limit. To address these problems, this paper proposes a new publish/subscribe model based on possibility theory and fuzzy set theory to process uncertainties for both subscriptions and publications. Furthermore, an approximate publish/subscribe matching problem is defined and algorithms for solving it are developed and evaluated.

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Tags: content-based publish/subscribe, event processing, probabilistic data management, pub/sub applications, publish/subscribe, topss


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