Ten lectures on wavelets
Compressing still and moving images with wavelets
Multimedia Systems - Special issue on video compression
Essential wavelets for statistical applications and data analysis
Essential wavelets for statistical applications and data analysis
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Approximate ad-hoc query engine for simulation data
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
JPEG 2000: Image Compression Fundamentals, Standards and Practice
JPEG 2000: Image Compression Fundamentals, Standards and Practice
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
Simulation data as data streams
ACM SIGMOD Record
A hybrid approach for multiresolution modeling of large-scale scientific data
Proceedings of the 2005 ACM symposium on Applied computing
Fast and effective lossy compression algorithms for scientific datasets
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Short communication: A framework for automating the configuration of OpenCL
Environmental Modelling & Software
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To provide scientists and engineers with the ability to explore and analyze tera-scale size data-sets we are using a twofold approach. First, we model the data with the objective of creating a compressed yet manageable representation. Second, with that compressed representation, we provide the ability to query the resulting approximation in order to obtain approximate yet sufficient answers; a process called ad-hoc querying. This paper is concerned with a wavelet modeling technique that seeks to capture the important physical characteristics of the target scientific data. Our approach is driven by the compression, which is necessary for viable throughput, along with the end user requirements from the discovery process. Our work contrasts existing research which applies wavelets to range querying, change detection, and clustering problems by working directly with the wavelet decomposition of the data. The difference in this procedure is due primarily to the nature of the data and the requirements of the scientists and engineers. Our approach directly uses the wavelet coefficients of the data to compress as well as query. We describe how the wavelet decomposition is used to facilitate data compression and how queries are posed on the resulting compressed model. Results of this process will be shown for several problems of interest.