Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Approximating multi-dimensional aggregate range queries over real attributes
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using wavelet decomposition to support progressive and approximate range-sum queries over data cubes
Proceedings of the ninth international conference on Information and knowledge management
Optimal and approximate computation of summary statistics for range aggregates
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
How to evaluate multiple range-sum queries progressively
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Wavelet synopses with error guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Approximate Answers to Aggregate Queries on a Data Cube
SSDBM '99 Proceedings of the 11th International Conference on Scientific and Statistical Database Management
pCube: Update-Efficient Online Aggregation with Progressive Feedback and Error Bounds
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient computation of multiple group by queries
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
ProDA: a suite of web-services for progressive data analysis
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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Plots are among the most important and widely used tools for scientific data analysis and visualization. With a plot (a.k.a. range group-by query) data are divided into a number of groups, and at each group, they are summarized over one or more attributes for a given arbitrary range. Wavelets, on the other hand, allow efficient computation of (individual) exact and approximate aggregations. With the current practice, to generate a plot over a wavelet-transformed dataset, one aggregate query is executed per each plot point; hence, for large plots (containing numerous points) a large number of aggregate queries are submitted to the database. On the contrary, we redefine a plot as a range group-by query and propose a wavelet-based technique that exploits I/O sharing across plot points to evaluate the plot efficiently and progressively. The intuition behind our approach comes from the fact that we can decompose a plot query into two sets of 1) aggregate queries, and 2) reconstruction queries. Subsequently, we exploit and extend our earlier related studies to effectively compute both quires in the wavelet domain. We also show that our technique is not only efficient as an exact algorithm but also very effective as an approximation method where either the query time or the storage space is limited.