Paper
20 March 2013 A weight based genetic algorithm for selecting views
Seyed Hamid Talebian, Sameem Abdul Kareem
Author Affiliations +
Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87683O (2013) https://doi.org/10.1117/12.2011072
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore
Abstract
Data warehouse is a technology designed for supporting decision making. Data warehouse is made by extracting large amount of data from different operational systems; transforming it to a consistent form and loading it to the central repository. The type of queries in data warehouse environment differs from those in operational systems. In contrast to operational systems, the analytical queries that are issued in data warehouses involve summarization of large volume of data and therefore in normal circumstance take a long time to be answered. On the other hand, the result of these queries must be answered in a short time to enable managers to make decisions as short time as possible. As a result, an essential need in this environment is in improving the performances of queries. One of the most popular methods to do this task is utilizing pre-computed result of queries. In this method, whenever a new query is submitted by the user instead of calculating the query on the fly through a large underlying database, the pre-computed result or views are used to answer the queries. Although, the ideal option would be pre-computing and saving all possible views, but, in practice due to disk space constraint and overhead due to view updates it is not considered as a feasible choice. Therefore, we need to select a subset of possible views to save on disk. The problem of selecting the right subset of views is considered as an important challenge in data warehousing. In this paper we suggest a Weighted Based Genetic Algorithm (WBGA) for solving the view selection problem with two objectives.
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Seyed Hamid Talebian and Sameem Abdul Kareem "A weight based genetic algorithm for selecting views", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87683O (20 March 2013); https://doi.org/10.1117/12.2011072
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Cited by 2 scholarly publications.
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KEYWORDS
Genetic algorithms

Data modeling

Stars

Databases

Optimization (mathematics)

Binary data

Algorithm development

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