Haifeng Liu, Hans Arno Jacobsen

Publikation: Beitrag in Buch/Bericht/KonferenzbandKapitelBegutachtung

8 Zitate (Scopus)


This chapter provides an overview of Approximate Matching based Toronto Publish/Subscribe System (A-ToPSS)—a publish/subscribe system supporting imperfect information processing. This new publish/subscribe system is becoming increasingly popular for information dissemination applications. Publish/subscribe systems anonymously interconnect information providers with information consumers in a distributed environment. Information providers publish information in the form of publications, and information consumers subscribe their interests in the form of subscriptions. The publish/subscribe system performs the matching task and ensures the timely delivery of published events to all the interested subscribers. However, in many situations the exact knowledge to crisply specify subscriptions or publications is not available. In these cases, the uncertainty about the state of the world has to be cast into a crisp data model that defines absolute limits. Moreover, for a user of a publish/subscribe system, it may be much simpler to describe the state of the world with vague or uncertain terms. For these reasons, it is of great advantage to provide a publish/subscribe data model and an approximate matching scheme that allows the expression and processing of uncertainties for both subscriptions and publications. It is difficult to absolutely quantify subscriber satisfaction, therefore the software demonstration is expected to further illustrate the pros and cons of this approach.

TitelProceedings 2004 VLDB Conference
UntertitelThe 30th International Conference on Very Large Databases (VLDB)
Herausgeber (Verlag)Elsevier
ISBN (elektronisch)9780120884698
PublikationsstatusVeröffentlicht - 1 Jan. 2004
Extern publiziertJa


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