DMCA
Matching Events in a Content-based Subscription System (2003)
Cached
Download Links
| Citations: | 303 - 8 self |
BibTeX
@INPROCEEDINGS{Aguilera03matchingevents,
author = {Marcos K. Aguilera and Robert E. Strom and Daniel C. Sturman and Mark Astley and Tushar D. Chandra},
title = {Matching Events in a Content-based Subscription System},
booktitle = {},
year = {2003},
pages = {53--61},
publisher = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Content-based subscription systems are an emerging alternative to traditional publish-subscribe systems, because they permit more flexible subscriptions along multiple dimensions. In these systems, each subscription is a predicate which may test arbitrary attributes within an event. However, the matching problem for content-based systems --- determining for each event the subset of all subscriptions whose predicates match the event --- is still an open problem. We present an efficient, scalable solution to the matching problem. Our solution has an expected time complexity that is sub-linear in the number of subscriptions, and it has a space complexity that is linear. Specifically, we prove that for predicates reducible to conjunctions of elementary tests, the expected time to match a random event is no greater than O#N # where N is the number of subscriptions, and # is a closed-form expression that depends on the number and type of attributes (in some cases, # # 1=2). We present some optimizations to our algorithms that improve the search time. We also present the results of simulations that validate the theoretical bounds and that show acceptable performance levels for tens of thousands of subscriptions.
Keyphrases
content-based subscription system matching problem multiple dimension traditional publish-subscribe system closed-form expression show acceptable performance level open problem arbitrary attribute expected time flexible subscription scalable solution content-based system elementary test search time random event theoretical bound expected time complexity space complexity
