The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Updating and Querying Databases that Track Mobile Units
Distributed and Parallel Databases - Special issue on mobile data management and applications
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Continuous queries over data streams
ACM SIGMOD Record
A Relational Approach to Querying Data Streams
IEEE Transactions on Knowledge and Data Engineering
Databases for Tracking Mobile Units in Real Time
ICDT '99 Proceedings of the 7th International Conference on Database Theory
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Indexing the Current Positions of Moving Objects Using the Lazy Update R-tree
MDM '02 Proceedings of the Third International Conference on Mobile Data Management
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
PSoup: a system for streaming queries over streaming data
The VLDB Journal — The International Journal on Very Large Data Bases
Fast computation of database operations using graphics processors
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
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
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Real-Time Processing of Range-Monitoring Queries in Heterogeneous Mobile Databases
IEEE Transactions on Mobile Computing
GPUTeraSort: high performance graphics co-processor sorting for large database management
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
Incremental Evaluation of Sliding-Window Queries over Data Streams
IEEE Transactions on Knowledge and Data Engineering
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
SOLE: scalable on-line execution of continuous queries on spatio-temporal data streams
The VLDB Journal — The International Journal on Very Large Data Bases
Semantics and implementation of continuous sliding window queries over data streams
ACM Transactions on Database Systems (TODS)
Smart phone for mobile commerce
Computer Standards & Interfaces
A Fast Similarity Join Algorithm Using Graphics Processing Units
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Leveraging Computation Sharing and Parallel Processing in Location-Based Services
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
Supporting views in data stream management systems
ACM Transactions on Database Systems (TODS)
Location-dependent query processing: Where we are and where we are heading
ACM Computing Surveys (CSUR)
GEDS: GPU Execution of Continuous Queries on Spatio-Temporal Data Streams
EUC '10 Proceedings of the 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing
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Much research exists for the efficient processing of spatio-temporal data streams. However, all methods ultimately rely on an ill-equipped processor [22], namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents GEDS, a scalable, Graphics Processing Unit GPU-based framework for the evaluation of continuous queries over spatio-temporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs.