Paper Info
Title
Dynamic histograms for future spatiotemporal range predicates
Abstract
Recently, in moving object databases that mainly manage the spatiotemporal attributes, approximate query processing for the future location-based queries has deserved enormous attention. Histograms are generally used for selectivity estimation and approximate query answering in database enviromnents. Because histograms' static properties may, however make them inappropriate for application areas that treat dynamic properties such as moving object databases, it is necessary to develop several mechanisms that can be well applied to dynamic query processing.In this paper we present a new method to efficiently process the approximate answers for future location-based query predicates on demand by using spatiotemporal histograms. Based on the concepts of entropy and marginal distribution, we build spatiotemporal histograms for the movement parameters, which result in the avoidance of reconstructing histograms. Using spatiotemporal histograms, the approximate future query processing can be achieved efficiently. In addition, we clarify and evaluate our proposed method with several experiments.
Year
DOI
Venue
2005
10.1016/j.ins.2004.07.007
Inf. Sci.
Keywords
Field
DocType
spatiotemporal attribute,approximate query answering,object databases,dynamic histogram,future location-based query predicate,future location-based query,future spatiotemporal range predicate,approximate answer,approximate future query processing,approximate query processing,spatiotemporal histogram,dynamic query processing,histogram
Query optimization,Histogram,Data mining,Query language,Query expansion,Computer science,Sargable,Web query classification,Predicate (grammar),Marginal distribution
Journal
Volume
Issue
ISSN
172
1-2
0020-0255
Citations 
PageRank 
References 
4
0.42
13
Authors
3
Name
Order
Citations
PageRank
Hyun Kyoo Park1141.47
Jin Hyun Son221718.21
Myoung Ho Kim31040273.40