A unifying look at data structures
Communications of the ACM
Block-Oriented Compression Techniques for Large Statistical Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Multi-Dimensional ROLAP Indexing
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
An Algebraic Compression Framework for Query Results
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Lowest common ancestors in trees and directed acyclic graphs
Journal of Algorithms
| Hi-index | 0.00 |
It is desirable to employ compression techniques in Relational OLAP systems to reduce disk space requirements and increase disk I/O throughput. Tuple Differential Coding (TDC) techniques have been introduced to compress views on a tuple level by storing only the differences between consecutive ordered tuples. These techniques work well for highly regular data in which the differences between tuples are fairly constant but are less effective on real data containing either skew or outliers. In this paper we introduce Adaptive Tuple Differential Coding (ATDC), which employs optimization techniques to analyze blocks of tuples to detect large tuple differences, with the purpose of isolating them to minimize their negative effect on the compression of neighbouring tuples. Our experiments show that this new algorithm provides an increase in compression ratio of 15-30% over TDC on typical real datasets.