A Framework for Generating Network-Based Moving Objects
Geoinformatica
SINA: scalable incremental processing of continuous queries in spatio-temporal databases
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
A generic framework for monitoring continuous spatial queries over moving objects
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient proximity detection among mobile targets with dead reckoning
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
Adaptive location constraint processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Processing proximity relations in road networks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Efficient proximity detection among mobile users via self-tuning policies
Proceedings of the VLDB Endowment
RoadTrack: scaling location updates for mobile clients on road networks with query awareness
Proceedings of the VLDB Endowment
Continuous Monitoring of Distance-Based Range Queries
IEEE Transactions on Knowledge and Data Engineering
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Many location-based applications are enabled by handling numerous moving queries over mobile objects. Efficient processing of such queries mainly relies on effective probing, i.e., polling the objects to obtain their current locations (required for processing the queries). With effective probing, one can monitor the current location of the objects with sufficient accuracy for the existing queries, by striking a balance between communication cost of probing and accuracy of the knowledge about current location of the objects. In this paper, we focus on location-based applications that reduce to processing a large set of proximity monitoring queries simultaneously, where each query continuously monitors if a pair of objects are within a certain predefined distance. Accordingly, we propose an effective object probing solution for efficient processing of proximity monitoring queries. In particular, with our proposed solution for the first time we formulate optimal probing as a batch processing problem and propose a method to prioritize probing the objects such that the total number of probes required to answer all queries is minimized. Our extensive experiments demonstrate the efficiency of our proposed solution for a wide range of applications involving up to hundreds of millions of queries.