How to evaluate multiple range-sum queries progressively
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
SHIFT-SPLIT: I/O efficient maintenance of wavelet-transformed multidimensional data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Plot Query Processing with Wavelets
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Online Scientific Applications (OSA) require statistical analysis of large multidimensional datasets. Towards this end, we have designed and developed a data storage and retrieval system, called ProDA, which deploys wavelet transform and provides fast approximate answers with progressively increasing accuracy in support of the OSA queries. ProDA employs a standard web-service infrastructure to enable remote users to interact with their data. These web-services enable wavelet transformation of large multidimensional datasets as well as inserting, updating, and exact, approximate and progressive querying of these datasets in the wavelet domain. We demonstrate the features of ProDA on a massive atmospheric dataset provided to us by NASA/JPL.