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Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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Continuous queries over data streams
ACM SIGMOD Record
Data management and transfer in high-performance computational grid environments
Parallel Computing - Parallel data-intensive algorithms and applications
Data streams: algorithms and applications
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GRID '02 Proceedings of the Third International Workshop on Grid Computing
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DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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ICPADS '05 Proceedings of the 11th International Conference on Parallel and Distributed Systems - Volume 01
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IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
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SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
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HPCS '06 Proceedings of the 20th International Symposium on High-Performance Computing in an Advanced Collaborative Environment
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CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
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IDEAS '07 Proceedings of the 11th International Database Engineering and Applications Symposium
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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This paper presents our experience in experimenting the query performance of a Grid-based sensor network data warehouse, which encompasses several metaphors of data compression/approximation and high performance and high reliability computing that are typical of Grid architectures. Our experimentation focuses on two main classes of aggregate range queries over sensor readings, namely (i) the window queries, which apply a SQL aggregation operator over a fixed window over the reading stream produced by the sensor network, and (ii) the continuous queries, which instead consider a "moving" window, and produce as output a stream of answers. Both classes of queries are extremely useful to extract summarized knowledge to be exploited by OLAP-like analysis tools over sensor network data. The experimental results, conducted on several synthetic data sets, clearly confirm the benefits deriving from embedding the data compression/approximation paradigm into Grid-based sensor network data warehouses.