A CASE STUDY ON DATA MINING AND DATA WAR (1)

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GADL Journal of Inventions in Computer Science and Communication Technology (JICSCT) ISSN(O): 2455-5738

Volume 3

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Issue 4,-July-Aug 2017

Richa et al., JICSCT www.gadlonline.com

1 A CASE STUDY ON DATA MINING AND DATA WAREHOUSE

Richa Mewara 1, Sarvesh Singh2

Dept. of Computer Science & Engineering, JVWU Jaipur

ABSTRACT

Th

is paper shows information and new application in data mining and data warehouse as well as the advantage of data mining and data warehouse the purpose of data mining and data warehouse to make decision making system in any field for business and organization. This project for how the business will organize in marketing field and what are the advantage or disadvantage for data mining and data warehouse it represent the new techniques in the top of the market for accessing and increase the value for the database. Thus the paper shows that what is data mining and data warehouse and how is use in market and what all are the advantage for market value. It is familiar that the strategic level of decision usually does not use business information on a daily basis but instead, derivative data from specific time. It is necessary in decision-making process to consider the large amounts of database so that the quality of decision-making is satisfied. Data Warehouse and Data Mining concept are any as a good base for business decision-making.

Keywords:

Data Mining, Data Warehouse, Database, Information.

INTRODUCTION TO DATA

WAREHOUSE

A data warehouse is a database which use for kept the data and large amount of information from

organization’s operational database. That user can

easily access and use the data from data warehouse. A business organization’s wants to access or analyze the previous data or feedback on any database such as product, a supplier, or any other data then the executive will have no database available to analyze because the previous data has been updated due to transactions.

A data warehouse provides us generalized and consolidated data in multidimensional views. A data warehouse also provides us online Analytical Processing tools. This analysis results in database generalization and data mining. In this no frequent updating done in a data warehouse. It is use their data to take strategic decisions. It helps in the integration of diversity of applications systems.

APPLICATION OF DATA

WAREHOUSE

BANKING INDUSTRY

Bank Intelligence is a method of storing and presenting and accessing of business data so that anyone in the bank can quickly and easily access data and asks questions of accurate and timely data. In a bank networks, hundreds of millions of customer database records are generated daily. Data Warehouse provides a consolidated database view of the past transactions and is not useful without a proper strategies or view to convert that into information.

FINANCE INDUSTRY

A financial data warehouse using the latest technologies can increase the quality of our data and help us to gain insights into customer behaviour. Clients often improve the effectiveness of their marketing campaigns and loyalty programs as a result of these new insights.Our financial analytics services are business-driven and our experts spend time establishing our individual requirements and task. Our consulting services help financial businesses to:

 Make decisions more effectively and easy to access.

 Bring new products onto the market.

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2 GOVERMENT AND EDUCATION

The objective of the Data Warehouse is to enable easy access to accurate and integrated government database for better and faster decision making and more efficient decisions purposes. The Government Data Warehouse will be a single data with a subject-centric approach, in order to provide a view of Service data, optimised for reporting and analysis. Educational institutions have developed into large businesses in and of themselves. This change has resulted in more business-like management of these institutions as well. To gain an appreciation of how the data warehousing process can benefit institutions of higher education, we can take the many example of the benefits manufacturing and sales organizations have enjoyed from this process and apply them to the educational setting. This application will help to provide more clear understanding of the value data warehousing can provides to higher education institutions and many others .

INTRODUCTION OF DATA MINING

Data mining the extraction of hidden predictive information from large databases Is a powerful new technology with great potential to help companies focus on the most important information and database in their data warehouses. A data mining tools predict future trends and behaviours and database, allowing businesses to make knowledge-driven decisions.

The automated prospective analyses offered by data mining move the analyses of past and present events provided by retrospective tools typical of decision support systems. Data mining tool can answer business questions that traditionally were too time consuming to resolve. Databases for hidden patterns, finding predictive information those experts may miss because it lies outside their expectations. Data mining process is the result of a long process of research and accessing the database, product development and decision making system.

This evolution begin when business database was first stored on computers, continued with improvements in database access, and more recently, generated .

APPLICATION OF DATA MINING

RETAIL INDUSTRY

Retail industry collects large amount of database and information on sales and customer shopping history. The quantity and quality of data and information collected continues to rapidly, especially due to the increasing ease, availability and popular the business conducted on web, or retail industry provides a rich source for data mining. Retail database mining can help identify customer behaviour, discover customer shopping trends, improve the quality of customer service and patterns, achieve better customer retention and satisfaction and feedback, enhance goods consumption ratios design more effective goods transportation and distribution policies and reduce the cost of business.

BIOLOGICAL DATA ANALYSIS

Data mining techniques which designed to handle such challenging data analysis problems, And with real life applications how biologists and clinical scientists can employ data mining to enable them to make meaningful strategy from a wide array of heterogeneous data from molecular biology and clinical domains.

 Data produced by different labs need to be integrated and for accessing.

 Data mining can be used to perform data cleaning and

integration, object reconciliation to merge heterogeneous databases.

 Build phylogenetic trees.  Similarity search.  Difference search.

 3D structure of proteins heavily affects their

Functionalities.

 Prediction of protein structures.  Discovery of regularities.

CRIME INVESTIGATION

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distinguishes between information related threats and non-information related security threats. Information related threats are essentially attacks on computers and networks. They are threats that damage electronic information and data. Non-information related terrorist threats include terrorist attacks, bombing, shooting and, vandalism, kidnapping, setting property on fire. The questions asked by all stakeholders are can the security agencies and their strategies fight the non-information related security threats. Do these agencies have appropriate Information Technology Infrastructure in place for the purpose of information gathering, sharing, dissemination, and

decision making

.

The circumstances of humans social life, makes it vital to encounter a phenomenon known as crime. So we always need to the knowledge of crime analysis as an efficient combating tool. Crime analysis basically includes leveraging a systematic approach for identifying, discovering and sometimes predicting crime incidents. The input of a crime analysis system is consisted of data and information assigned to crime variables and the output includes the answer to investigative and analytical questions, knowledge extraction and finally visualization of the results.

COMPARISON BETWEEN DATA

MINING AND DATA WAREHOUSE

# Data Mining Data Warehouse 1. Data Mining is a

set of techniques used to search,

retrieve and

analyze data from a data warehouse.

Data Warehouse is about the storing of analytical data in a structure suitable for data mining. from multiple sources into one common

Data Warehouse is the central repository for the data of several patterns in a given data set these

Data Warehouse is a tool to save time and improve efficiency by brining data from different location from different areas of the organization together. from the data that we even don’t know exist in the database.

Data Warehouse is an environment where stores all the relevant information of

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4 CONCUSION

This paper shows the Introduction of data mining and data warehouse. And their application Based on the some real life problems we can conclude that Data ware house offers a flexible solution to the user to access the data, who can use many application and task for data mining as well as data ware house in many fields of education and in more many field. A data warehouse project can be very difficult. Analysis of the success of the data warehouse project is done considering the financial benefits against the investment. Since most of the educational institutes are nonprofits organizations and services oriented data, the evaluation of the usefulness of the data warehouse can be done on

the basis of its ability to meet user’s requirements in many other fields.

REFERENCES

1.http://www.differencebetween.info/difference-between-data-mining-and-data-warehousing.

2.https://www.quora.com/What-is-the-difference-between-data-mining-and-data-warehouse.

3.http://www.careerride.com/Data-warehousing-data-mining.aspx.

4.http://www.differencebetween.net/technology/sof tware-technology/difference-between-data-mining-and-data-warehousing.

5.http://www.anderson.ucla.edu/faculty/jason.frand /teacher/technologies/palace/datamining.htm.

6.http://searchsqlserver.techtarget.com/definition/d ata-mining.

7.https://www.tutorialspoint.com/data_mining/dm_ applications_trends.htm.

8.http://bigdata-madesimple.com/14-useful-applications-of-data-mining.

9.https://www.matillion.com/insights/5-real-life- applications-of-data-mining-and-business-intelligence.

10.http://tdan.com/data-warehouse-applications-by-industry.

11.https://www.slideshare.net/wikramanayake/appl ication-of-data-warehousing-data-mining-to- exploitation-for-supporting-the-planning-of-higher-education-system-in-sri-lanka.

12.http://whatisdbms.com/12-applications-of-data-warehouse.

13.http://www.1keydata.com/datawarehousing/data -warehouse-definition.html.

14.http://searchsqlserver.techtarget.com/definition/ data-warehouse.

15.https://www.tutorialspoint.com/dwh/dwh_data_ warehousing.htm.

16.https://docs.oracle.com/cd/B10500_01/server.92 0/a96520/concept.htm.

17.http://ieeexplore.ieee.org/document/1500153.

18.https://www.techopedia.com/definition/1184/dat a-warehouse-dw.

19.https://www.slideshare.net/2cdude/data-warehousing-3292359.

20.https://thesai.org/Downloads/Volume7No4/Pape

r_55-Application_of_Data_Warehouse_in_Real_Life_St ate.pdf.

21.http://www.dei.unipd.it/~capri/SI/MATERIALE /DWDM0405.pdf.

22.http://cdn.ttgtmedia.com/searchDataManagemen t/downloads/Data_Warehouse_Design.pdf.