Computer
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MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
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Mobile Computing
Foundations of Databases: The Logical Level
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Modeling and Querying Moving Objects
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ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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NGIT '99 Proceedings of the 4th International Workshop on Next Generation Information Technologies and Systems
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SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
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ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
FIFO indexes for decomposable problems
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Interpolating and using most likely trajectories in moving-objects databases
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Leveraging computation sharing and parallel processing in location-dependent query processing
The Journal of Supercomputing
Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework
International Journal of Grid and High Performance Computing
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Proceedings of the 25th International Conference on Scientific and Statistical Database Management
GEDS: GPU execution of spatio-temporal queries over spatio-temporal data streams
Journal of Embedded Computing
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In this paper we consider databases representing information about moving objects (e.g. vehicles), particularly their location. We address the problems of updating and querying such databases. Specifically, the update problem is to determine when the location of a moving object in the database (namely its database location) should be updated. We answer this question by proposing an information cost model that captures uncertainty, deviation, and communication. Then we analyze dead-reckoning policies, namely policies that update the database location whenever the distance between the actual location and the database location exceeds a given threshold, x. Dead-reckoning is the prevalent approach in military applications, and our cost model enables us to determine the threshold x. Then we consider the problem of processing range queries in the database, and we propose a probabilistic algorithm to solve the problem.