文章基本信息

标题：SQL Data Sets With PIVOT Based Indexing for Data Mining Analysis
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作者：K Jayaprakash ; Venkata Ramana Adari
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：2013
卷号：4
期号：3
页码：130-133
语种：English
出版社：Ayushmaan Technologies
摘要：Now a day’s many complex queries are required to prepare data sets for data mining analysis. They require more time and much effort is need for joining tables and aggregate columns. Existing SQL methods have limitations to prepare data sets because they return one column per aggregated group. A data mining project requires many SQL queries, joining tables and aggregating columns. Conventional RDBMS usually manage tables with vertical form. Aggregated columns in a horizontal tabular layout returns set of numbers, instead of one number per row. The system uses one parent table and different child tables, operations are then performed on the data loaded from multiple tables. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. The system use specific methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal de normalized layout which is the standard layout required by most data mining algorithms. The system proposes three fundamental methods to generate data sets for mining analysis.
关键词：Aggregation;Pivot based Indexing;SQL 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.