Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis(2012)

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ABSTRACT

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, 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 denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. 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.

Existing System:

      An existing to preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group.

Disadvantage:

1)    Existing SQL aggregations have limitations to prepare data sets.

2)    To return one column per aggregated group.

Proposed System:

   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.

Advantage:

1)    The SQL code reduces manual work in the data preparation phase in a data mining project.

2)    The SQL code is automatically generated it is likely to be more efficient than SQL code written by an end user.

3)    The data sets can be created in less time.

4)    The data set can be created entirely inside the DBMS.

Module Description:

1.    Admin Module

2.    User Module

3.    View Module

4.    Download Module

Module 1 : Admin Module

     Admin will upload new connection form based on regulations in various states. Admin will be able upload various details regarding user bills like a new connection to a new user, amount paid or payable by user. In case of payment various details regarding payment will be entered and separate username and password will be provided to users in large.

Module 2 : User Module

    User will be able to view his bill details on any date may be after a month or after months or years and also he can to view the our bill details in a various ways for instance, The year wise bills, Month wise bills, totally paid to bill in EB. This will reduce the cost of transaction. If user thinks that his password is insecure, he has option to change it. He also can view the registration details and allowed to change or edit and save it.

Module 3 : View Module

Admin has three ways to view the user bill details, the 3 ways are

i)  SPJ

ii) PIVOT

iii)CASE

i) SPJ : While using SPJ the viewing and processing time of user bills is reduced.

ii) PIVOT : This is used to draw the user details in a customized table. This table will elaborate us on the various bill details regarding the user on monthly basis.

iii) CASE : using CASE query we can customize the present table and column based on the conditions. This will help us to reduce enormous amount of space used by various user bill details. It can be viewed in two difference ways namely Horizontal and Vertical.

In case of vertical the number of rows will be reduced to such an extent it is needed and column will remain the same on other hand the Horizontal will reduce rows as same as vertical and will also increase the columnar format

Module 4 : Download Module

     User will be able to download the various details regarding bills. If he/she is a new user, he/she can download the new connection form, subscription details etc. then he/she can download his /her previous bill details in hands so as to ensure it.

Hardware Requirements:

•         System                 : Pentium IV 2.4 GHz.

•         Hard Disk            : 80 GB.

•         Floppy Drive       : 1.44 Mb.

•         Monitor                : 15’ VGA Colour.

•         Mouse                  : Optical Mouse

•         RAM                    : 512 MB.

Software Requirements:

•         Operating system : Windows XP.

•         Coding Language : ASP.Net with C#

•         Data Base             : SQL Server 2005     

Click here to download Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis(2012) source code