Cited By
View all- Hesabi ZTari ZGoscinski AFahad AKhalil IQueiroz C(2015)Data Summarization Techniques for Big Data—A SurveyHandbook on Data Centers10.1007/978-1-4939-2092-1_38(1109-1152)Online publication date: 17-Mar-2015
Towards realistic sampling: generating dependencies in a relational database
Article No.: 12, Pages 1 - 6
Abstract
Managing large amounts of information is one of the most expensive, time-consuming and non-trivial activities and it usually requires expert knowledge. In a wide range of application areas, such as data mining, histogram construction, approximate query evaluation, and software validation, handling exponentially growing databases has become a difficult challenge, and a subset of the data is generally preferred. As a solution to the current challenges in managing large amounts of data, database sampling from the operational data available has proved to be a powerful technique. However, none of the existing sampling approaches consider the dependencies between the data in a relational database. In this paper, we propose a novel approach towards constructing a realistic testing environment, by analyzing the distribution of data in the original database along these dependencies before sampling, so that the sample database is representative to the original database.
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Index Terms
- Towards realistic sampling: generating dependencies in a relational database
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Published In
January 2013
772 pages
ISBN:9781450319584
DOI:10.1145/2448556
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Published: 17 January 2013
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ICUIMC '13
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ICUIMC '13: The 7th International Conference on Ubiquitous Information Management and Communication
January 17 - 19, 2013
Kota Kinabalu, Malaysia
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Cited By
View all- Hesabi ZTari ZGoscinski AFahad AKhalil IQueiroz C(2015)Data Summarization Techniques for Big Data—A SurveyHandbook on Data Centers10.1007/978-1-4939-2092-1_38(1109-1152)Online publication date: 17-Mar-2015
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