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
Journal of Intelligent Information Systems
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Balancing accuracy and privacy of OLAP aggregations on data cubes
DOLAP '10 Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
Journal of Computer and System Sciences
Supporting efficient distributed skyline computation using skyline views
Information Sciences: an International Journal
Towards a theory for privacy preserving distributed OLAP
Proceedings of the 2012 Joint EDBT/ICDT Workshops
Parallel rare term vector replacement: Fast and effective dimensionality reduction for text
Journal of Parallel and Distributed Computing
Efficiently compressing OLAP data cubes via R-tree based recursive partitions
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
FGIT'12 Proceedings of the 4th international conference on Future Generation Information Technology
Efficient Top-k Keyword Search Over Multidimensional Databases
International Journal of Data Warehousing and Mining
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An innovative technique supporting accuracy control in compressed multidimensional data cubes is presented in this paper. The proposed technique can be efficiently used in QoA-based OLAP tools, where OLAP users/applications and DW servers are allowed to mediate on the accuracy of (approximate) answers, similarly to what happens in QoS-based systems for the quality of services. The compressed data structure KLSA, which implements the technique, is also extensively presented and discussed. We complement our analytical contributions with an experimental evaluation on several kinds of synthetic multidimensional data cubes, demonstrating the superiority of our approach in comparison with other similar techniques.