Paper Info
Title
Efficient Top-k Subscription Matching for Location-Aware Publish/Subscribe
Abstract
The dissemination of messages to a vast number of mobile users has raised a lot of attention. This issue is inherent in emerging applications, such as location-based targeted advertising, selective information disseminating, and ride sharing. In this paper, we examine how to support location-based message dissemination in an effective and efficient manner. Our main idea is to develop a location-aware version of the Pub/Sub model, which was designed for message dissemination. While a lot of studies have successfully used this model to match the interest of subscriptions (e.g., the properties of potential customers) and events (e.g., information of casual users), the issues of incorporating the location information of subscribers and publishers have not been well addressed. We propose to model subscriptions and events by boolean expressions and location data. This allows complex information to be specified. However, since the number of publishers and subscribers can be enormous, the time cost for matching subscriptions and events can be prohibitive. To address this problem, we have developed the R-I-tree. This data structure is an integration of the R-tree and the dynamic interval-tree. Together with our novel pruning strategy on R-I-tree, our solution can effectively and efficiently return the top-k subscriptions with respect to an event. We have performed extensive evaluations to verify our approach.
Year
DOI
Venue
2015
10.1007/978-3-319-22363-6_18
ADVANCES IN SPATIAL AND TEMPORAL DATABASES (SSTD 2015)
Field
DocType
Volume
Publication,Data structure,Data mining,Computer science,Tree (data structure),Targeted advertising,Priority queue,Dissemination,Conjunctive normal form,Boolean expression
Conference
9239
ISSN
Citations 
PageRank 
0302-9743
6
0.41
References 
Authors
16
4
Name
Order
Citations
PageRank
Jiafeng Hu116210.87
Reynold Cheng23069154.13
Dingming Wu360.41
Beihong Jin440549.23