Proper management of multidimensional aggregates is a fundamental prerequisite for efficient OLAP. The experimental OLAP server CUBESTAR, which concepts are described in this paper, was designed exactly for that purpose. All logical query processing is based solely on a specific algebra for multidimensional data. However, a relational database system is used for the physical storage of the data. Therefore, in popular terms CUBESTAR can be classified as a ROLAP system. In comparison to commercially available systems, CUBESTAR is superior in two aspects: First, the implemented multidimensional data model allows more adequate modeling of hierarchical dimensions, because properties which apply only to certain dimensional elements can be modeled context-sensitively. This fact is reflected by an extended star schema on the relational side. Second, CUBESTAR supports multidimensional query optimization by caching multidimensional aggregates. Since summary tables are not created in advance but as needed, hot spots can be adequately represented. The dynamic and partition-oriented caching method allows cost reductions of up to 60% with space requirements of less than 10% of the size of the fact table.
Abstract:A data warehouse system is a necessity for fundamental decisions in every enterprise. The integration of data from several internal or external sources and the functionality of modern decision support systems like OLAP tools not only provide broad access to data but also raise security problems. Security concerns are more or less the same as those of other database systems but enriched especially with access and inference control in the multidimensional model. This paper presents an integrated approach for inference and access control not on the physical but on the conceptual level. The question is not only the restriction of relations, but rather the identification and evaluation of the inference problem of hierarchies and dimensions. The possibility to restrict or perturbate data in general, is not an adequate solution. We present some specific problems of a market research company and a solution with an indicator to discover possible attacks and so be able to restrict the access by mechanisms like aggregation, restriction or perturbation.
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