By Topic

Scalable Processing of Continuous K-Nearest Neighbor Queries with Uncertainty in Spatio-Temporal Databases

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lien-Fa Lin ; Dept. of Inf. Commun., Kao-Yuan Univ., Kaohsiung, Taiwan ; Yuan-Ko Huang

Continuous K-nearest neighbor (CKNN) query is an important type of spatio-temporal queries. Given a time interval [ts, te] and a moving query object q, a CKNN query is to find the K-nearest neighbors (KNNs) of q at each time instant within [ts, te]. In this paper, we focus on the issue of scalable processing of CKNN queries over moving objects with uncertain velocity. Due to the large amount of CKNN queries needed to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the CKNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. Several experiments are performed to demonstrate the effectiveness and the efficiency of the CI-tree.

Published in:

Research Challenges in Computer Science, 2009. ICRCCS '09. International Conference on

Date of Conference:

28-29 Dec. 2009