Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Selectivity estimation in spatial databases
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
A survey of logical models for OLAP databases
ACM SIGMOD Record
Data mining: concepts and techniques
Data mining: concepts and techniques
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TreeScape System: Reuse of Pre-Computed Aggregates over Irregular OLAP Hierarchies
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Binary-Tree Histograms with Tree Indices
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Overcoming Limitations of Approximate Query Answering in OLAP
IDEAS '05 Proceedings of the 9th International Database Engineering & Application Symposium
A quad-tree based multiresolution approach for two-dimensional summary data
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
Extending visual OLAP for handling irregular dimensional hierarchies
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
This paper introduces a novel data cube compression technique for data cubes whose main idea consists in exploiting the knowledge kept in OLAP hierarchies to drive the compression process. This approach leads to the so-called knowledge-oriented data cube compression paradigm, which is a noticeable alternative to the traditional algorithmic-oriented paradigm that focuses the attention on the issue of compressing the data cube like the latter would be a simple multidimensional array without additional knowledge. This amenity allows us to achieve several benefits, among which a more meaningful exploration of the compressed data cube enriched by semantics-aware metaphors. Our analytical contribution is finally completed by a comprehensive experimental evaluation of our proposed technique on both benchmark and real-life data cubes, also in comparison with well-established histogram-based data cube compression techniques.