Data Warehousing And Data Mining 53b3m

WALCHAND INSTITUTE OF TECHNOLOGY

A PAPER PRESENTATION ON DATA WAREHOUSING AND DATA MINING AT

SUBMITTED BY: AMOL P. NITAVE

ABBAS HASHMI

B.E. (C.S.E) B.E. (C.S.E) [email protected]

GUIDED BY:

Prof. R. B. Kulkarni (CSE Dept. WIT, Solapur)

INDEX 1. ABSTRACT 2. DATA WAREHOUSING  Introduction  Need of Data Warehousing  Purpose of Data Warehousing

 Characteristics  Life cycle  Architecture  Tools and technologies  Applications 3. DATA MINING  Introduction  Types of Data Mining  Major elements of Data Mining  Data Mining: A KDD process  Steps in KDD process  Methods of Data Mining 4. PROJECT ON DATA MINING: Website Data Mining  Aim of project  Implementation  Working  Advantages 5. CONCLUSION 6. REFERENCE

DATA WAREHOUSING AND DATA MINING ABSTRACT: Fast, accurate and scalable data analysis techniques are needed to extract useful information from huge pile of data. Data warehouse is a single, integrated source of decision information formed by collecting data from multiple sources, internal to the organization as well as external, and transforming and summarizing this information to enable improved decision making. Data warehouse is designed for easy access by s to large amounts of information, and data access is typically ed by specialized analytical tools and applications. Typical applications include decision systems and execution information system. Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. It is “An information extraction activity whose goal is to discover hidden facts contained in databases”. The process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions. The project entitled “Website Data Mining” is an application of data mining which is built for the website developers for their effective creation of websites in internet. Data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. It produces output values for an assigned set of input values. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis.

DATA WAREHOUSING Everyday increasingly, organizations are analyzing current and historical data to identify useful patterns and business strategies. A large amount of the right information is the key to survival in today’s competitive environment. And this kind of information can be made available only if there’s totally integrated enterprise data warehouse.

 What is data warehousing? A data warehouse is a subject-oriented, integrated, non-volatile & time-variant collection of data in of management’s decisions.

 Need for Data Warehousing: • IT or business staff spending a lot of time developing special reports for decision-makers. • Lots of PC-based or small server systems obtaining extracts of data incapable of presenting a holistic view of the entire gamut of information. • Same data present on different systems, in different department and s may be unaware of this fact. • Difficulty in getting meaningful information in a timely manner. • Multiple systems giving different answer to the business questions. • Less analysis by decision makers and policy planners due to non-availability of sophisticated tools and easily decipherable, timely and comprehensive information

 Purpose of Data Warehousing: Better business intelligence for end s. • Reduction in time to access and analyze information. • Consolidation of disparate information sources.

• Replacement of older, less-responsive decision systems • Faster time to market for products and services

 Data Warehouse Characteristics: 1. Subject-orientedàWH is organized around the major subjects of the enterprise rather

than the major application areas. This is reflected in the need to store decision- data rather than application-oriented data. 2. Integratedàbecause the source data come together from different enterprise-wide

applications systems. The source data is often inconsistent using..The integrated data source must be made consistent to present a unified view of the data to the s. 3. Time-variantàthe source data in the WH is only accurate and valid at some point in

time or over some time interval. The time-variance of the data warehouse is also shown in the extended time that the data is held, the implicit or explicit association of time with all data, and the fact that the data represents a series of snapshots. 4. Non-volatileàdata is not update in real time but is refresh from OS on a regular basis.

New data is always added as a supplement to DB, rather than replacement. the DB continually absorbs this new data, incrementally integrating it with previous data 

DATA WAREHOUSE LIFE CYCLE: Data warehousing is a concept. It is not a product that can be purchased off the shelf. It

is a set of hardware and software components integrated together which can be used to analyze the massive amount of data stored in an efficient manner. It is a process through which one can build a successful data warehouse. Following are the five steps towards building a successful data warehouse.

1) JUSTIFICATION 2) REQUIREMENT ANALYSIS 3) DESIGN 4) DEVELOPMENT & IMPLEMENTATION

5) DEPLOYMENT



DATA WAREHOUSE ARCHITECTURE :

Operational data source1

Meta-data

Operational data source 2

Lightly Manage

Reporting, query,application development, and EIS(executive information system) tools

High Query summarized data

summarized

Load Manager

data

Operational Detailed data

data source n

DBMS

OLAP(online analytical processing) tools

Operational data store (ods)

Operational data store (ODS)

Data mining Archive/backup

End-access tools

data

Typical architecture Warehouse Manager of a data warehouse

Main Components: •

Operational data sourcesàfor the DW is supplied from mainframe operational data held in first generation hierarchical and network databases, departmental data held in proprietary file systems, private data held on workstaions and private serves and external

systems such as the Internet, commercially available DB, or DB assoicated with and organization’s suppliers or customers •

Operational datastore(ODS)àis a repository of current and integrated operational data used for analysis. It is often structured and supplied with data in the same way as the data warehouse, but may in fact simply act as a staging area for data to be moved into the warehouse

•

Load manageràalso called the frontend component, it performance all the operations associated with the extraction and loading of data into the warehouse. These operations include simple transformations of the data to prepare the data for entry into the warehouse

•

Warehouse manageràperforms all the operations associated with the management of the data in the warehouse. The operations performed by this component include analysis of data to ensure consistency, transformation and merging of source data, creation of indexes and views, generation of denormalizations and aggregations, and archiving and backing-up data

•

Query manageràalso called backend component, it performs all the operations associated with the management of queries. The operations performed by this component include directing queries to the appropriate tables and scheduling the execution of queries

•

End- access toolsàcan be categorized into five main groups: data reporting and query tools, application development tools, executive information system (EIS) tools, online analytical processing (OLAP) tools, and data mining tools.

 Tools and Technologies: •

The critical steps in the construction of a data warehouse: a. Extraction

•

b. Cleansing

c. Transformation

After the critical steps, loading the results into target system can be carried out either by separate products, or by a single, categories:

•

Code generators

•

Database data replication tools

•

Dynamic transformation engine

 Applications: •

Online Transaction Processing:

OLTP systems are the major kinds of enterprise applications: Examples: Order entry systems, Inventory control systems, Reservation systems, Point-of-sale systems, Tracking systems, etc.

•

Executive information system (EIS) : Present information at the highest level of summarization using corporate business measures. They are designed for extreme ease-of-use and, in many cases, only a mouse is required. Graphics are usually generously incorporated to provide at-a-glance indications of performance

•

Decision Systems (DSS) : They ideally present information in graphical and tabular form, providing the with the ability to drill down on selected information. Note the increased detail and data manipulation options presented.

DATA MINING What is data mining? Data Mining refers to the process of analyzing the data from different perspectives and summarizing it into useful information. Data mining software is one of the numbers of tools used for analyzing data from many different dimensions or angles, categorize it, and summarize the relationship identified.

Definition: Data mining is the process of finding correlation or patterns among fields

in large

relational databases. “The process of extracting valid, previously unknown, comprehensible, and actionable information from large databases and using it to make crucial business decision”

Different Types of Data Mining: Business, Scientific and Internet Data Mining Five major elements of Data Mining: 1. Extract, transform, & load transaction data on to the data warehouse system. 2. Store and manage data in multidimensional database system. 3. Provide access to business analysts and IT Professionals. 4. Analyze the data by application software. 5. Present the data in useful format such as graph or table.

DATA MINING: A KDD Process:

Steps of KDD Process: 1. Learning the application domain 2. Relevant prior knowledge and goals of application 3. Creating a target data set: data selection 4. Data cleaning and preprocessing 5. Data reduction and transformation 6. Find useful features, dimensionality or variable reduction, and invariant representation.

7. Choosing functions of data mining 8. Summarization, classification, regression, association, clustering. 9. Choosing the mining algorithm(s) 10. Data mining: search for patterns of interest 11. Pattern evaluation and knowledge presentation 12. Visualization, transformation, removing redundant patterns, etc. 13. Use of discovered knowledge.

 Methods of Data Mining: 1. Classification



2.Regression

3.Clustering 4.Associative rules 5.Visualization

PROJECT ON DATA MINING: “Website Data Mining” We have created an application which works as a data mining for a website developer.

The project has been implemented successfully on a local server and has given an excellent . •

Aim of the project: To give a simple graph to a on the whole information of websites

•

Implementation: The data warehouse that is being used for the project is information gathered by a survey. The data has been collected to a database. This database is used in the project. The database contains the information on many websites. This is a huge database. The database is formed going to the questionnaires that were subtitled by the s of that websites. The application we created is a web based one. The application creates particular graph like, pie chart, line chart or bar graph. These graphs are generated as per the parameters selected by the website builders. The parameters that are selected would look as the figure below:

These constraints entered by the are considered to generate charts. The abstraction of the data from the database is done in effective manner. The will just know, for example, a website builder wants to know where the social networking sites are used maximum as per the database will look as below:

•

Working:

Java Servlet Pages (JSP) is used for the program the application. The database is stored in the Microsoft Access DB. For implementation purpose a local server of Tomcat 6.0 Server is used. For generating the charts in JSP, we made use of the JFreeChart package. The page navigation is considered for taking the inputs. The traversing is as follows: Index.jsp à ganechhart.jsp In index.jsp, the parameters are taken from the . These parameters are posted to the genechart.jsp file in the server. The SQL queries are fixed to generate the appropriate records. These records are used to build the charts. Example of the code for SQL in JSP is as follows: String url="jdbc:odbc:Driver={Microsoft Access Driver(*.mdb)}; DBQ=/FinalDB.mdb;DriverID=22;READONLY=true"; Class.forName("sun.jdbc.odbc.JdbcOdbcDriver"); con=DriverManager.getConnection(url,"",""); st=con.createStatement(); rs = st.executeQuery( sSql ); while( rs.next() ) { out.println( "" ); for( int i=1; i<=n; i++ ) // Achtung: erste Spalte mit 1 statt 0 out.println( "" + rs.getString( i ) + "" ); } These records after getting formed, an algorithm is used to get the statistics of the data. This algorithm will give the whole implementation of websites that can be used to generate the chart. The charts are generated with the following code: while( rs3.next() ) { data.setValue(rs3.getString( 1 ), cvi[i++]); } final ChartRenderingInfo info = new ChartRenderingInfo(new StandardEntityCollection()); final File file1 = new File("../piechart3.png"); ChartUtilities.saveChartAsPNG(file1, chart, 600, 400, info); The chart when generated will be saved as ‘.png’ image file. This is then displayed as an output to the . • Advantages:  The website builder can retrieve the appropriate factors that he wants to know before creating a site.  A big survey results can be generated within records and a simple understandable chart is prepared that can be used by the surveyors.

 CONCLUSION Data Warehousing provides the means to change the raw data into information for making effective business decisions-the emphasis on information, not data. The Data warehouse is the hub for decision data. Data mining is a useful tool with multiple algorithms that can be tuned for specific tasks. It can benefit business, medicine, and science. It needs more efficient algorithms to speed up data mining process.

 REFERENCE 

Multidimensional Data analysis and Data Mining

- Arinjay Choudhary, Dr. P.S. Deshande  Data Mining and Data Warehousing and OLAP -A. Berson, S.J. Smith  www.datawarehousingonline.com AND www.Wikipedia.com