Building the data warehouse (2nd ed.)
Building the data warehouse (2nd ed.)
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Quasi-cubes: exploiting approximations in multidimensional databases
ACM SIGMOD Record
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Compressed data cubes for OLAP aggregate query approximation on continuous dimensions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Computing appropriate representations for multidimensional data
Data & Knowledge Engineering - Special issue: Advances in OLAP
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Condensed Cube: An Efficient Approach to Reducing Data Cube Size
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Range CUBE: Efficient Cube Computation by Exploiting Data Correlation
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
PrefixCube: prefix-sharing condensed data cube
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Evaluation of a MCA-based approach to organize data cubes
Proceedings of the 14th ACM international conference on Information and knowledge management
Efficient multidimensional data representations based on multiple correspondence analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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On Line Analytical Processing (OLAP) is a technology basically created to provide users with tools in order to explore and navigate into data cubes. Unfortunately, in huge and sparse data, exploration becomes a tedious task and the simple user's intuition or experience does not lead to efficient results. In this paper, we propose to exploit the results of the Multiple Correspondence Analysis (MCA) in order to enhance data cube representations and make them more suitable for visualization and thus, easier to analyze. Our approach addresses the issues of organizing data in an interesting way and detects relevant facts. Our purpose is to help the interpretation of multidimensional data by efficient and simple visual effects. To validate our approach, we compute its efficiency by measuring the quality of resulting multidimensional data representations. In order to do so, we propose an homogeneity criterion to measure the visual relevance of data representations. This criterion is based on the concept of geometric neighborhood and similarity between cells. Experimental results on real data have shown the interest of using our approach on sparse data cubes.