Handbook on Parallel and Distributed Processing
Handbook on Parallel and Distributed Processing
A Framework for Generating Network-Based Moving Objects
Geoinformatica
Spatio-Temporal Data Services in a Shared-Nothing Environment
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
A framework for spatio-temporal query processing over wireless sensor networks
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
Quality-aware dstributed data delivery for continuous query services
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
MobiEyes: A Distributed Location Monitoring Service Using Moving Location Queries
IEEE Transactions on Mobile Computing
Efficient Maintenance of Continuous Queries for Trajectories
Geoinformatica
SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams
The VLDB Journal — The International Journal on Very Large Data Bases
The Art of Multiprocessor Programming
The Art of Multiprocessor Programming
Deriving spatio-temporal query results in sensor networks
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Processing (multiple) spatio-temporal range queries in multicore settings
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
P2EST: parallelization philosophies for evaluating spatio-temporal queries
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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We address the problem of efficiently parallelizing the processing of spatio-temporal range queries in multicore settings. Although the data set can be partitioned and assigned to individual cores for processing a collection of range queries, one cannot achieve an "ideal" assignment for all the cores' load. Hence, the cores should collaborate in a dynamic manner: ones that have completed their (sub)tasks should take part of the load from the cores that are still processing some of the data. We provide algorithms and synchronization data structures that achieve such collaborative behavior and we investigate their impact in different initial load-partitioning strategies. Our experiments demonstrate that about 40% speed-up can be gained when compared to static load-partitioning and that the proposed approach scales well.