Scalable continuous range monitoring of moving objects in symbolic indoor space
Proceedings of the 18th ACM conference on Information and knowledge management
Efficient fuzzy top-k query processing over uncertain objects
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Uncertain distance-based range queries over uncertain moving objects
Journal of Computer Science and Technology
Evaluating continuous probabilistic queries over imprecise sensor data
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Spatial query processing based on uncertain location information
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
MUD: Mapping-based query processing for high-dimensional uncertain data
Information Sciences: an International Journal
Probabilistic range monitoring of streaming uncertain positions in geosocial networks
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Probabilistic filters: A stream protocol for continuous probabilistic queries
Information Systems
Optimal k-constraint coverage queries on spatial objects
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Processing probabilistic range queries over gaussian-based uncertain data
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a probabilistic range query by extending the traditional notion of a spatial range query and present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.