- 期刊
- OpenAccess
Analysis of Data Mining Dataset Using Fuzzy Based Unnested Select SQL Queries
並列摘要
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.