Kalinka Kaloyanova

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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)
Big data is a broad term with numerous dimensions, most notably: big data characteristics, techniques, software systems, application domains, computing platforms, and big data milieu (industry, government, and academia). In this paper we briefly introduce fundamental big data characteristics and then present seven case studies of big data techniques,(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)
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)
In the software industry there are new major technology trends - i.e. Service Oriented Architecture (SOA), Business Process Management (BPM) and Enterprise Architecture - that had already gained enough popularity and adoption to have a stance on their own in the community of practice, but that have not been successfully incorporated into the standard set of(More)
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