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• W2149503419 abstract "This thesis reports on the research done into improving query performance for holistic aggregate queries for real-time data exploration. There are three types of aggregation functions: distributive, algebraic and holistic. The performance of database queries with the first two types of aggregation functions can be (greatly) improved by employing the distributed nature of such functions. However holistic aggregate functions, such as the median aggregation function, cannot be computed distributively. Therefore a different method is needed to improve query performance of database queries with a holistic function. Existing research in this field is primarily focused on finding approximate solutions to improve query performance however the research described in this thesis focuses on finding an exact solution. As part of the research several new algorithms have been developed which are able to significantly improve the query performance of holistic aggregation queries and completely change the time complexity of such queries. In addition several optimizations have been developed which improve query performance even further. Each of the newly developed algorithms and optimizations have been systematically evaluated on two criteria: query performance and storage cost. Finally a selection framework has been designed that aids in the selection of the best algorithm and optimization for different circumstances." @default.
• W2149503419 created "2016-06-24" @default.
• W2149503419 creator A5024243106 @default.
• W2149503419 date "2014-07-01" @default.
• W2149503419 modified "2023-09-27" @default.
• W2149503419 title "Improving Query Performance of Holistic Aggregate Queries for Real-Time Data Exploration" @default.
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• W2149503419 hasPublicationYear "2014" @default.
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