Wavelets for computer graphics: theory and applications
Wavelets for computer graphics: theory and applications
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 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
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
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Wavelet-based relative prefix sum methods for range sum queries in data cubes
CASCON '02 Proceedings of the 2002 conference of the Centre for Advanced Studies on Collaborative research
Extended wavelets for multiple measures
Proceedings of the 2003 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
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
ProDA: a suite of web-services for progressive data analysis
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A study on workload-aware wavelet synopses for point and range-sum queries
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Exploiting duality in summarization with deterministic guarantees
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical bin buffering: Online local moments for dynamic external memory arrays
ACM Transactions on Algorithms (TALG)
Hierarchical synopses with optimal error guarantees
ACM Transactions on Database Systems (TODS)
Plot Query Processing with Wavelets
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Hierarchically compressed wavelet synopses
The VLDB Journal — The International Journal on Very Large Data Bases
Spatiotemporal summarization of traffic data streams
Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming
Parsimonious linear fingerprinting for time series
Proceedings of the VLDB Endowment
Target-based privacy preserving association rule mining
Proceedings of the 2011 ACM Symposium on Applied Computing
ThermoCast: a cyber-physical forecasting model for datacenters
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast approximate wavelet tracking on streams
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Constructing optimal wavelet synopses
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
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The Discrete Wavelet Transform is a proven tool for a wide range of database applications. However, despite broad acceptance, some of its properties have not been fully explored and thus not exploited, particularly for two common forms of multidimensional decomposition. We introduce two novel operations for wavelet transformed data, termed SHIFT and SPLIT, based on the properties of wavelet trees, which work directly in the wavelet domain. We demonstrate their significance and usefulness by analytically proving six important results in four common data maintenance scenarios, i.e., transformation of massive datasets, appending data, approximation of data streams and partial data reconstruction, leading to significant I/O cost reduction in all cases. Furthermore, we show how these operations can be further improved in combination with the optimal coefficient-to-disk-block allocation strategy. Our exhaustive set of empirical experiments with real-world datasets verifies our claims.