Ina Naydenova

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Business intelligence applications involve complex queries on very large databases. Users typically view the data as multidimensional data cubes. Computing multidimensional aggregates in large data cubes is a performance bottleneck for many OLAP applications. Calculating the answer of an aggregation query can be too expensive in terms of time and storage(More)
Sparse data causes the data explosion problem in precomputation process and decreases the performance of OLAP. The regular sparsity map is an object that saves information about specific empty domains of the OLAP hyper-cubes and enables business analysts to define rules and place data constraints over the multidimensional cube. The preserved information can(More)
Data warehouses require and provide extensive support for data cleaning. They load and continuously refresh huge amounts of data from a variety of sources so the probability that some of the sources contain “dirty data” is high. In this paper we present our regular sparsity map editor which can be used for the purpose of detection of specific data errors in(More)
On Line Analytical Processing (OLAP) is an approach to provide the answer to analytical queries. Pre-aggregation is one of the most important techniques used to speed up the query response time. However, if many aggregates are precomputed, then the database can quickly multiply in size. In this paper we present a scheme of how to decrease the storage size(More)
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