Cart (Loading....) | Create Account
Close category search window
 

Supporting movement pattern queries in user-specified scales

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

4 Author(s)
Yunyao Qu ; NOAA Satellite Active Archive Comput. Sci. Corp., Suitland, MD, USA ; Changzhou Wang ; Like Gao ; Wang, X.S.

An important investigation of moving objects involves searching for objects with specific movement patterns, such as "going up," "going towards southwest," or a combination of these. Movement patterns can be in various scales, and larger-scale patterns usually span over longer time periods with greater disturbances ignored. Movement pattern queries ask for moving objects which show a given movement pattern in a specific scale. This paper studies database techniques to support fast evaluation of movement pattern queries in user-specified scales. The database is assumed to contain position information of moving objects sampled at a certain time interval. A movement pattern is defined as a regular expression of movement letters where each letter describes a set of movement directions. For each series of positions, movement directions of all scales are precomputed and results are mapped into points on a plane. Points on this plane usually cluster well and can be readily bounded by trapezoids. These bounding trapezoids are then stored in a relational database and the query language SQL can be used to help evaluate movement pattern queries. This paper also reports some experiments conducted on a real data set as well as a synthesized data set. Results show that both the precomputation algorithm and the bounding strategy are efficient and scalable.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:15 ,  Issue: 1 )

Date of Publication:

Jan.-Feb. 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.