Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
Approximate range---sum query answering on data cubes with probabilistic guarantees
Journal of Intelligent Information Systems
A Robust Sampling-Based Framework for Privacy Preserving OLAP
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Experimenting the Query Performance of a Grid-Based Sensor Network Data Warehouse
Globe '08 Proceedings of the 1st international conference on Data Management in Grid and Peer-to-Peer Systems
Multiple-Objective Compression of Data Cubes in Cooperative OLAP Environments
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
H-IQTS: a semantics-aware histogram for compressing categorical OLAP data
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
LCS-Hist: taming massive high-dimensional data cube compression
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Journal of Intelligent Information Systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Event-based lossy compression for effective and efficient OLAP over data streams
Data & Knowledge Engineering
Balancing accuracy and privacy of OLAP aggregations on data cubes
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Non-linear data stream compression: foundations and theoretical results
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
| Hi-index | 0.00 |
Two important limitations of approximate query answering in OLAP are recognized and investigated. These limitations are: (i) scalability of the techniques, i.e. their reliabiliy on highly-dimensional data cubes, and (ii) need for guarantees on the degree of approximation of the answers. In this paper, we focus on the first limitation, and propose adopting the well-known Karhunen-Loeve Transform (KLT) to obtain dimensionality reduction of data cubes, thus devising a transformation methodology that is independent by the number of dimensions of the data cubes. To tailor the KLT for the specific OLAP context, effective optimizations are also proposed, by taking into account the query-consciousness feature. Finally, some encouraging preliminary experimental results are presented.