Modeling Uncertainties in Publish/Subscribe Systems

  • Authors:
  • Haifeng Liu;Hans-Arno Jacobsen

  • Affiliations:
  • -;-

  • Venue:
  • ICDE '04 Proceedings of the 20th International Conference on Data Engineering
  • Year:
  • 2004

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Abstract

In the publish/subscribe paradigm, informationproviders disseminate publications to all consumers whohave expressed interest by registering subscriptions. Thisparadigm has found wide-spread applications, rangingfrom selective information dissemination to network management.However, all existing publish/subscribe systemscannot capture uncertainty inherent to the information ineither subscriptions or publications. In many situations,exact knowledge of either specific subscriptions or publicationsis not available. Moreover, especially in selectiveinformation dissemination applications, it is often moreappropriate for a user to formulate her search requestsor information offers in less precise terms, rather thandefining a sharp limit. To address these problems, thispaper proposes a new publish/subscribe model based onpossibility theory and fuzzy set theory to process uncertaintiesfor both subscriptions and publications. Furthermore,an approximate publish/subscribe matching problem isdefined and algorithms for solving it are developed andevaluated.