Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/10205
Title: Performance tuning stratgies for data warehouse: a division of Rajasthan state
Researcher: Goar, Vishal Kumar
Guide(s): Sarangdevot, S S
Tanwar, Manish
Keywords: Computer Science
Performance tuning stratgies
data warehouse
Upload Date: 30-Jul-2013
University: Suresh Gyan Vihar University
Completed Date: n.d.
Abstract: There has been an extensive demand in the use of databases for decision support in these days. This was mainly due to the fact that information, one of the most precious assets of an organization, can assist in decision making and this way considerably improves the value of an organization. This phenomenon is a result of the increased availability of new technologies to support capable storage and retrieval of large volumes of data, namely data warehousing. Thus, we could define data warehouse as a repository of data that has been extracted and integrated from heterogeneous and autonomous distributed sources [Kimball96]. Data warehouses usually contain a huge amount of data that must be analyzed and, provide that analysis, helping in the organizational decision making process. The success of this kind of support depends greatly on database systems and correlated analysis tools. Data warehouses differ significantly from the traditional database applications. Data warehouses provide a different context in which huge amounts of data must be processed efficiently and queries are often complex, but still require interactive response times. In data warehouse environments the data is used for decision support and large sets of data are read and analyzed. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: the data warehouse must be fast . The main focus of our research is finding adequate solutions to improve query response time of typical data ware house queries and improve scalability using an environment that takes advantage of characteristics specific to the data ware house context. Our propose model provides very good performance and scalability even on huge data warehouses.
Pagination: 273p.
URI: http://hdl.handle.net/10603/10205
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File18.61 kBAdobe PDFView/Open
02_acknowledgement.pdf83.29 kBAdobe PDFView/Open
03_thesis approval.pdf82.73 kBAdobe PDFView/Open
04_contents.pdf153.01 kBAdobe PDFView/Open
05_list of figures.pdf97.62 kBAdobe PDFView/Open
06_list of abbreviations.pdf100.61 kBAdobe PDFView/Open
07_abstract.pdf102.47 kBAdobe PDFView/Open
08_nehruji.pdf128.48 kBAdobe PDFView/Open
09_chapter 1.pdf405.87 kBAdobe PDFView/Open
10_chapter 2.pdf519.8 kBAdobe PDFView/Open
11_chapter 3.pdf374.97 kBAdobe PDFView/Open
12_chapter 4.pdf869.48 kBAdobe PDFView/Open
13_chapter 5.pdf138.84 kBAdobe PDFView/Open
14_chapter 6.pdf44.68 kBAdobe PDFView/Open
15_bibliography.pdf318.1 kBAdobe PDFView/Open
16_publications.pdf112.34 kBAdobe PDFView/Open


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