Extracting semantics in OLAP databases using emerging cubes
Data cubes capture general trends aggregated from multidimensional data from a categorical relation. When provided with two relations, interesting knowledge can be exhibited by comparing the two underlying data cubes. Trend reversals or particular phenomena irrelevant in one data cube may indeed clearly appear in the other data cube. In order to capture such trend reversals, we have proposed the concept of Emerging Cube. In this article, we emphasize on two new approaches for computing Emerging Cubes. Both are devised to be integrated within standard Olap systems, since they do not require any additional nor complex data structures. Our first approach is based on Sql. We propose three queries with different aims. The most efficient query uses a particular data structure merging the two input relations to achieve a single data cube computation. This query works fine even when voluminous data are processed. Our second approach is algorithmic and aims to improve efficiency and scalability while preserving integration capability. The E-Idea algorithm works a laBuc and takes the specific features of Emerging Cubes into account. E-Idea is automaton-based and adapts its behavior to the current execution context. Our proposals are validated by various experiments where we measure query response time. Comparative experiments show that E-Ideas response time is proportional to the size of the Emerging Cube. Experiments also demonstrate that extracting Emerging Cubes can be computed in practice, in a time compatible with user expectations.
data cube olap mining data warehouse trend analysis