Research Article | OPEN ACCESS
Analysis of Data Mining Dataset Using Fuzzy Based Unnested Select SQL Queries
1D. Veni and 2K.R. Chandran
1Anna University,
2Information Technology Department, PSG College of Technology, Coimbatore, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology 2015 3:267-273
Received: December ‎10, ‎2014 | Accepted: February ‎5, ‎2015 | Published: September 25, 2015
Abstract
The aim of this study is to improve the existing traditional databases. Some new techniques have been involved to handle the imprecise or uncertain information from the dataset. Dataset is prepared and used to analyze the data mining project which consume more time and need many complex queries. Nested query is predominant method to handle complex queries. Execution of a nested query may cause the heavy performance penalty. The main objective of this study is to reduce the heavy performance penalty of nested queries by using the unnested queries. The unnested queries produce the equivalent output as nested queries with minimum penalty and execution time. Success of unnested queries are examined using join algorithms. It is more efficient than the nested-loop algorithms which are used to evaluate the nested queries. In this study, unnested queries are used to analysis the data-mining project in dataset, we get the result from combining fuzzy set theory. In experimental results, we have shown that the performance of evaluating the unnesting techniques with extended merge-join and horizontal aggregations techniques CASE, SPJ and PIVOT in dataset. Thus, unnested queries improve the performance of execution and linear scalability.
Keywords:
Fuzzy queries, nested queries, PIVOT, set exclusive operator,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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