Two-dimensional signal and image processing
Two-dimensional signal and image processing
OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Optimal multi-scale patterns in time series streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Efficient Process of Top-k Range-Sum Queries over Multiple Streams with Minimized Global Error
IEEE Transactions on Knowledge and Data Engineering
Boolean representation based data-adaptive correlation analysis over time series streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Adaptive correlation analysis in stream time series with sliding windows
Computers & Mathematics with Applications
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For time-relevant multi-dimensional data sets (MDS), users usually pose a huge amount of data due to the large dimensionality, and approximating query processing has emerged as a viable solution. Specifically, the cube streams handle MDSs in a continuous manner. Traditional cube approximation focuses on generating single snapshots rather than continuous ones. To address this issue, the application of generating snapshots for cube streams, called SCS, is investigated in this paper. Such an application collects data events for cube streams on-line and generates snapshots with limited resources in order to keep the approximated information in synopsis memory for further analysis. As compared to OLAP applications, the SCS ones are subject to much more resource constraints for both processing time and memory and cannot be dealt with by existing methods due to the limited resources. In this paper, the DAWA algorithm, standing for a hybrid algorithm of Dct for Data and discrete WAvelet transform, is proposed to approximate the cube streams. The DAWA algorithm combines the advantage of high compression rate from DWT and that of low memory cost from DCT. Consequently, DAWA costs much smaller working buffer and outperforms both DWT-based and DCT-based methods in execution efficiency. Also, it is shown that DAWA provides answers of good quality for SCS applications with a small working buffer and short execution time. The optimality of algorithm DAWA is theoretically proved and also empirically demonstrated by our experiments.