A Model for Publish/Subscribe System Supporting Uncertainties

Haifeng Liu.

University of Toronto, 2003.

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 neither subscriptions nor 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 convenient for a user to formulate her search requests or information offers in less precise terms, rather than defining a sharp limit.

To address this problem, this thesis proposes a new publish/subscribe model based on possibility theory and fuzzy set theory to process uncertain information 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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