Title :
Pointwise-Dense Region Queries in Spatio-temporal Databases
Author :
Jinfeng Ni ; Ravishankar, C.V.
Author_Institution :
Dept. of Comput. Sci. & Eng., California Univ., Riverside, CA, USA
Abstract :
Applications such as traffic management and resource scheduling for location-based services commonly need to identify regions with high concentrations of moving objects. Such queries are called dense region queries in spatio-temporal databases, and desire regions in which the density of moving objects exceeds a given threshold. Current methods for addressing this important class of queries suffer from several drawbacks. For example, they may fail to find all dense regions, provide ambiguous answers, impose restrictions on size, or lack a notion of local density. We address these issues in this paper, starting with a new definition of dense regions. We show that we are able to answer dense region queries completely and uniquely using this definition. Dense regions in our approach may have arbitrary shape and size, as well as local density guarantees. We present two methods, the first, an exact method, and the second, an approximate method. We demonstrate through extensive experiments that our exact method is efficient and is superior to current approaches. Our approximate method runs orders of magnitude faster than our exact method, at the cost of a tolerable loss of accuracy.
Keywords :
mobile computing; temporal databases; visual databases; location-based services; pointwise-dense region queries; spatio-temporal databases; Air traffic control; Application software; Computer science; Data engineering; Databases; Engineering management; Processor scheduling; Resource management; Space technology; Spatiotemporal phenomena;
Conference_Titel :
Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on
Conference_Location :
Istanbul
Print_ISBN :
1-4244-0802-4
DOI :
10.1109/ICDE.2007.368965