doi:10.1016/j.datak.2007.06.021
Copyright © 2007 Elsevier B.V. All rights reserved.
Continuous range monitoring of mobile objects in road networks
aDepartment of Computer Science, University of Nis AleksandraMedvedeva 14, 18000 Nis, Serbia
bDepartment of Informatics, Aristotle University 54124, Thessaloniki, Greece
Available online 12 September 2007.
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Abstract
In contrast to regular queries that are evaluated only once, a continuous query remains active over a period of time and has to be continuously evaluated to provide up to date answers. We propose a method for continuous range query processing for different types of queries, characterized by mobility of objects and/or queries which all follow paths in an underlying spatial network. The method assumes an available 2D indexing scheme for indexing spatial network data. An appropriately extended R*-tree, that primarily is used as an indexing scheme for network segments, provides matching of queries and objects according to their locations on the network or their network routes. The method introduces an additional pre-refinement step which generates main-memory data structures to support efficient, incremental reevaluation of continuous range queries in periodically performed refinement steps.
Keywords: Mobile objects; Location based services; Continuous range queries; Query processing
Fig. 1. Changing the status of a mobile object in a continuous query answer.
Fig. 2. Structure of SR*-tree, as well as MOT and CQT auxiliary structures.
Fig. 3. Insertion of mobile object in SR*-tree.
Fig. 4. Insertion of static/mobile query in SR*-tree according to range in Euclidean space.
Fig. 5. Insertion of static/mobile query in SR*-tree according to range in a network.
Fig. 6. Pre-refinement step for static query-mobile objects.
Fig. 7. Refinement step for the set of static continuous queries over mobile objects.
Fig. 8. Location/speed/network segment update of a mobile object.
Fig. 9. The average number of mobile objects obtained in the filter step and pre-refinement step for 10,000 MO/1000 static continuous queries.
Fig. 10. The CPU time required for the filter step for 1000 static/mobile continuous queries.
Fig. 11. The CPU time for the pre-refinement step per query for 10,000 MO/1000 (a) static CQ (b) mobile CQ.
Fig. 12. The CPU time needed for refinement of 1000 mobile/static queries over 10,000 mobile objects.
Fig. 13. The average number of mobile objects obtained in the refinement step for 10,000 MO/1000 static continuous queries.
Fig. 14. The average CPU time per mobile object needed for data structures update upon location/speed/segment update.
Fig. 15. The average CPU time for evaluation of continuous query with and without the pre-refinement step for different MO agilities and the evaluation period of 10 seconds.
Fig. 16. The relation between incremental and full continuous query answer for different evaluation periods.
Table 1.
Notation for analyzing the cost of pre-refinement step in query processing methodology
