Title :
Approximate matching in publish/ subscribe
Author :
Liu, Haifeng ; Jacobsen, Hans-Arno
Author_Institution :
Dept. of Comput. Sci., Toronto Univ., Ont., Canada
Abstract :
The publish/subscribe paradigm has found wide-spread applications, including selective information dissemination, location-based services, enterprises application integration, and network management. However, all existing publish/subscribe system models cannot capture any kind of uncertainty naturally inherent to many real world scenarios about formulated. To address this shortcoming, this paper proposes a new publish/subscribe system model to process uncertainties in both subscriptions and publications. The system model is evaluated in an implementation of a publish/subscribe system supporting uncertainties in publications and subscriptions through an approximate matching semantic.
Keywords :
inference mechanisms; information dissemination; publishing; uncertainty handling; approximate matching; approximate matching semantic; enterprises application integration; location-based services; network management; publications; publish paradigm; selective information dissemination; subscribe paradigm; subscriptions; uncertainties; Application software; Computer network management; Computer science; Data models; Engineering management; Intelligent networks; Jacobian matrices; Prototypes; Subscriptions; Uncertainty;
Conference_Titel :
Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on
Print_ISBN :
0-7803-7866-0
DOI :
10.1109/CIRA.2003.1222087