文章基本信息

标题：Different Types of Layouts in SQL to Prepare DatasetsandReports
本地全文：下载
作者：G. Kamala Kumari ; N. Tulasi Raju ; S. Uma Maheswara Rao 等
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：3
页码：889-894
语种：English
出版社：Ayushmaan Technologies
摘要：Preparing Reports and Dataset are difficult task in data mining. Our proposed horizontal aggregations provide several unique features and advantages. First, they represent a template to generate SQL code from a data mining tool. Such SQL code automates writing SQL queries, optimizing them and testing them for correctness. This SQL code reduces manual work in the data preparation phase in a data mining project. Second, since SQL code is automatically generated it is likely to be more efficient than SQL code written by an end user. For instance, a person who does not know SQL well or someone who is not familiar with the database schema (e.g. a data mining practitioner). Therefore, data sets can be created in less time. Third, the data set can be created entirely inside theDBMS. In modern database environments it is common to export denormalized data sets to be further cleaned and transformed outside a DBMS in external tools (e.g. statistical packages). Unfortunately, exporting large tables outside a DBMS is slow, creates inconsistent copies of the same data and compromises database security. Therefore, we provide a more efficient, better integrated and more secure solution compared to external data mining tools. Horizontal aggregations just require a small syntax extension to aggregate functions called in a SELECT statement. Alternatively, horizontal aggregations can be used to generate SQL code from a data mining tool to build data sets for data mining analysis.We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, whereas the SPJ method does not.
关键词：Aggregation;Data Preparation;Pivoting