Studies on E Governance in India using Data Mining Perspective

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JOURNAL OF COMPUTING, VOLUME 2, ISSUE 10, OCTOBER 2010, ISSN 2151-9617

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Studies on E Governance in India using

Data Mining Perspective

Ms. Sonali Agarwal, and Prof. G.N. Pandey

Abstract — The fast expansion, exploitation and propagation of the innovative and promising Information and Communication Technologies (ICTs) indicate

new opportunities for growth and development. Data Mining is a well established approach of discovering knowledge from databases for the purpose o

Knowledge Management. There is large number of data and information generated and collected by the different levels of governments. In case of gov

ernment, proper decision making is important to better utilization of all resources. Data Mining could help administrators to extract valuable knowledge and

practices out of this voluminous data, which can be used to obtained knowledge and practices for strategically reducing costs and increasing organization

expansion opportunities and also detect fraud, waste and abuse. The present investigation taken Education Data related with primary education in order to

analyze status of primary education in Allahabad and in Uttar Pradesh, India. Clustering and Classification methods are used to find out similarity or dissi-

milarity among various districts of Uttar Pradesh. This will create groups of districts as clusters so that these districts may further treated together undeone policy. Classification method is based on reported Gross Enrollment Ratio (GER). In this method some unusual classification of district highlighted

that the Data Mining could also establish the impact of migration from one district to another when all the students are given unique identification through

social security number.

Index Terms—Information and Communication Technologies, Knowledge Management, Data Mining, Clustering, Classification

—————————— ——————————

1 INTRODUCTION

ata Mining is a process of Knowledge Discovery in-

cludes methods used to recognize, generate, representand distribute knowledge for better utilisation of any

system. There is large number of data and information gen-erated and collected by the different levels of governments.In case of government, proper decision making is importantto better utilization of all resources. Data Mining could helpadministrators to extract valuable knowledge and practicesout of this voluminous data, which can be used to obtainedknowledge and practices for strategically reducing costs andincreasing organization expansion opportunities and alsodetect fraud, waste and exploitation.

The research work is aimed to represent the potential of

data mining in the context of smart techniques of E Gover-nance. Data Mining provides efficient techniques for gov-ernment agencies to analyze data quickly and with lessereconomic efforts [1]. The data extraction process generatesinteresting hidden patterns. The discovered hidden patternsenable the government systems in making better decisionsand having a more advanced plan in serving the citizens [8].Here we are representing an E Governance Model based onData Mining and Data Warehousing to facilitate

(i) Efficient methods for capturing, storing and handling government data collected from various resources over a period of time.

(ii)

Efficient Knowledge Management for improved internal processes, government policies and programson the basis of historical data stored in its databases

The present work proposes an E Governance modeframework based on Data Mining and Data Warehousingtechniques which may be efficiently used by the governmenat all its administrative levels Nation-al/State/District/Block).The proposed Model serves alpossible aspects of E Governance with the help of four basicbuilding blocks:

Administrative Block Technical Know How Block Service Block Stakeholder Block

2 RELETED WORK

There is an extensive range of Data Warehousing and DataMining applications in government’s regulatory, developmental and social welfare organization. The followings aresome examples reported in different literatures.

The project Total Information Awareness (TIA) waslaunched by the US government after the terrorist attack of9/11. The objective of Total Information Awareness (TIA

was to search large data and determine associations and pat-

———————————————— Ms. Sonali Agarwal is with the Indian Institute of Information Technolo-

gy, Allahabad, U.P., India Prof. G.N. Pandey is with the Indian Institute of Information Technology,

Allahabad, U.P., India

D






terns related with terrorist activities. The project conducteddiscovery of associations among transactions such as workpermits, credit card, airline tickets, passports, visas, rentalcars, gun purchases, driver’s license and events such as ar-rest or doubtful activities [17][15].

CAPPS is known as Computer Assisted Passenger Pre-

Screening System. It is a prescreening system initiated by theDepartment of Homeland Security US. It is implemented tocheck all airline passengers against a database of commer-cially available information. After checking it provides a riskcolor or status to each passenger. CAPPS collect informationprovided by the passenger for example Paasenger’s name,permanent address, contact number etc. These records arethen given to commercial data providers for assessment ofthe validity of the passenger and passenger’s correlationwith other events. The commercial data provider wouldassign a numerical score back to the owning system indicat-ing a particular risk level. The passengers having “green”score is considered as normal and safe passenger. The pas-sengers having “yellow” score then they would have to facesecond level screening test. The passengers having “red”score is considered as high risk passenger and high risk pas-sengers may not be allowed for traveling and they must befurther enquired about their identity and purpose of travel-ling [9].

In May 2004, a report on federal data mining activities in-dicates that US government agencies have very well adoptedthe data mining practices in e governance. Currently thereare 199 data mining projects ongoing in various stages. Stu-dies indicates that the government is also running some un-disclosed data mining projects for example national securityaagency's eavesdropping project and state level security

project matrix [10].

There are several research work published in the field ofmodel-building phase of the Data Mining process. A paperbased on Data Mining application for income tax departmentdiscusses how to build a Data Mining algorithm centeredapplication for the regulation of different government activi-ties [13]. Main concern of this paper includes architecture ofData Mining based application, working methodology andthe integration of knowledge of domain experts.

A Data Mining tool iHealth was developed by a healthorganization CSIRO. It provides a web based interface for

Data Mining and Data analyses tool for large health relateddatabases. The tool provides various clustering and classifi-cation methods to identify patients having certain specificprofiles. The patients’ profile could be visualized by usingvarious visualization techniques [6].

A paper presented a Data Mining based approach tostudy about student performance and dropout rate [11]. Themethod used Clustering and Decision Rule Data Miningtechniques to identify collection of clusters, which have beenhelpful to understand the nature of data. A Data Miningbased approach is discovered to classify the selected custom-ers into clusters using Recency, Frequency, Monetary value(RFM) model to identify high-profit, gold customers. Associ-

ation rules may establish the similarity, difference betweecustomer’s behaviors [6].

3 BASIC BUILDING BLOCK OF PROPOSED E

GOVERNANCE MODEL

The proposed E Governance model covers all important apect of E Governance in a single model. There are four BasBuilding Blocks of proposed E Governance Model. The lowest block is the Administration Block, which regulates thoverall function of any country through efficient goverment.

Fig. 1: Basic Building block of the Proposed E Governance Model

The overall regulation of government bodies may be ca

ried out by using appropriate Technical know how. ThTechnical know how block includes computerization of mnual processes, commonly agreed technological standarDatabase related applications and easy access of informatioThe third block is Service Block, which includes all availaboperations of the E Governance. It provides an interface btween user and government system. The upper block Stakeholder Block, which has various categories of useworking with the system. The user categories may be a Citzen, Business organization or any Government organizatio[13].

3.1 Module 1: Administration

Administration is a way of management of any working sytem supervised by an administrator. In any democratic sytem the administration may be governed by a structurebody name as government. The term Governance is basicalthe responsibility of a Government which includes each anevery processes performed by the government body. Thmain activity of the government is to controll the working different departments for exmaple Finance, Health, Eduction, Agriculture, Employment etc. All these activities anow maintained efficiently by using ICT. The transformatioof the working from conventional methods to modern mthods of Information Technology (IT) is now known as Government. The use of ICT in government activities hav

given a new idea of governance knows as E Governance.






3.1.1 Salient features of the proposed modelThe purpose of E Governance is to establishing good gover-nance and have seamless coordination between governmentauthorities, public and business parties. The utilization ofICT may join all three different sectors and support devel-opment and management. Therefore, following are the sa-

lient features of the proposed model.TABLE 1

SALIENT FEATURES OF THE PROPOSED MODEL

1. To provide proper information and awareness to thecitizen about the political practices and choices available.

2. To provide online services and active participation fordifferent citizen services.

3. To utilize ICT in government functions, that providesquick and well-organized communication with thepeople, business and other agencies.

4. To provide better decision-making through greater de-centralization of governance [4].

The proposed model is based on ICT, which may reformorganizational structures in both centralized as well as de-centralized manner. These approaches of E Governmenthave their own set of advantages and disadvantages.

Centralized ModelCentralize government initiatives are favorable as portalsand services to reduce cost and integration issues. Centralizegovernment initiatives may share technical, financial andhuman resources. A Single portal access is very useful forany end user because all the information may be centrallyavailable here. There are following features of Centralized EGovernance model.

All government process based on ICTs are centra-lized in one organizational unit.

Generally limited Infrastructural and set up costsbut less effective.

Centralized E Governance models have a single in-terface for its different users and these models couldbe easily enforced.

Decentralized ModelDecentralized model is required at lower level so that various projects can be handled saparatelty from initiation to

executation [3]. There are following features of DecentralizedE Governance model.

All government functions could be distributedamong various divisions or organizations.

Generally has a high coordination cost.

3.1.2 State level Model of E GovernanceThe State level model is based on the combination of bothcentralized and decentralized approaches. In State levelState government becomes the main coordinator of theproject and lower government offices with their departmentsbecome the partners of that project. Figure 1.2 describes ho-rizontal and vertical interconnections of E Governance.

Fig.2 : Horizontal and Vertical interconnection for E Governance

Certain important decisions are jointly made andthen standardized across the various levels.

Responsibilities as well as capabilities are decentralized at different government departments/levelswith infrastructure and output sharing across theState as a system.

Generally, high E Governance set up costs but moreresponsive to stakeholder needs. Higher level committees are formed to manage various Governmenactivities. These committees have authority to con-trol the functioning of large area.

Intra-department or horizontal and vertical collaborationsare very essential for success of any E Governance project. Iis very necessary to perform governance functions, shareinformation and deliver services to all stakeholders. Thesecollaborations depend on issues like what are the differen






types of intra- department collaborations exist in E Gover-nance and why intra- department collaborations are impor-tant [4].

3.2 Module 2: Technical Know HowFor E Governance, there are many applications need to beautomated. Various departments seek computerization and

other technological transformation of their working strate-gies. Now it is necessary to conceptualize the whole ap-proach and develop a standard framework and protocols forthe regulation of all E Governance activities. The proposedModel uses Data Mining and Data Warehousing for improv-ing the service performance of the E Governance system.

3.2.1 Case Study: Data Mining in Department ofEducation

Education related organizations are major application areafor Data Mining since it collect large amount of data on stu-dents enrollment, courses taught, students academic recordhistory etc. The data collection trend is also increasing be-

cause of the availability and popularity of courses taught.Today many institutions also have websites where studentsmay study online. Educational Data Mining may help identi-fy student academic performance, discover student’s beha-vior regarding selection of subjects. These patterns andtrends may further improve the quality of education, achievebetter student admission and satisfaction, and enhance goodacademic practice and policies.

Data Mining algorithms are used to distinguish different setof data by using the test data. For example an algorithmidentifies characteristics that distinguish students who tookout a particular kind of study loan from those who did not.Finally, it predicts rules regarding issuance of study loan.The rule is based on the attributes of the previous good stu-dents who are successfully paid their loans. These rules arefurther used to recognize such students on the remaining ofthe database.

In the same way, various algorithms are implemented toconvert the database into clusters of students with severalsimilar attributes and this may certainly reveal interestingand unexpected patterns. The patterns of the clusters arefurther interpreted by the experts, in collaboration with insti-tutions personnel.

3.3 Module 3 : Service BlockIn the service block, services of E Governance as end results are

provides to the citizens for betterment of their lives. It also pro-vides an interface so that a common citizen may participatein decision making processes. The Service Block also helpfulto simplify complex government process in which too manyoffices and manpower required. The final center of attentionwill be on efficient and well-organized delivery of govern-ment services [14]. The commonly used services are informa-tion access, making payments, submitting complaints anddownloading forms for some purpose.

3.4 Module 4 Stakeholder Block

Stakeholder is an individual person, group of persons orcommunity having common area of interest and commonaffected by any system. Here E Governances has a wide ragof stakeholders. The main groups are identified in 3 parts.

Fig.3: Data Mining in different Government Department by usiDistributed Databases

3.4.1 CitizenCitizen is associated with the E Governance by using Goernment to Citizen (G2C) interface. Government to Citize(G2C) interface is an online interaction between governmeand private individuals.

3.4.2 BusinessBusiness is associated with the E Governance by using Government to Business (G2B) interface. Government to Busine(G2B) interface is important because various trades anbusiness related transactions are required by the governmefor the regulatory purpose.

3.4.3 GovernmentVarious governments departments are associated with onother by the means of E Governance by using Government Government (G2G) interface. It provides online interaction different levels of government. The objective of G2G is build new relationships between different departments government. These relationships help collaboration betweelevels of government, and reform state and local goverments to convey better services to the citizen.

4 Data Mining Tool

For the idea of testing the framework, it is necessary to prvide at least one data mining tool to work with. The presen






investigation adopted WEKA as Data Mining Tool [3].

It contains tools for a whole range of data mining tasks likeData pre-processing, Classification, Clustering, Associationand Visualization [4]. It is Open Source Software, has stablereleases, is well documented. It is experimental in nature andit offers the ability to be extended. It provides an excellent

graphical user interface. It takes database in ARFF or CSVformats [5].

Fig. 4 : Different Views of WEKA Tool

4.1 Data Mining by using Data VisualizationData mining by using Data Visualization is a method inwhich various trends in databases may be visualized by us-ing graphs and charts [18]. Following issues are analyzed byusing Data Visualization.

The analysis indicates that there is large difference innumber of primary schools and upper primary schools.There must be one Upper Primary School for two PrimarySchool. But it is not actually present.

This will also obvious that, for maintaining the ratio ofnumber of primary school to number of upper primaryschool as two, more number of upper primary school willhave to be opened. The data further indicates that the dropout after primary school is or than the expected range. It isapparent from the data mining that the growth in number ofprimary school has not been uniform. This main reason maybe may the duplication of records. So, in order to remove anypossibility in duplication of data, allotment of social securitynumber to each citizen or student is very important.

4.2 Data Mining by using Clustering Clustering is a Data Mining approach which creates clustersof data items within a data set. Clusters are closed occur-rence of data items under the consideration of certain para-meters [19]. These clusters further represent similar groups.In this study raw data of education for Uttar Pradesh, Indiahas been taken. The database has 70 instances, whichrepresents all 70 districts of Uttar Pradesh. In the proposedapproach various districts may be clustered according to

their similarity. These groups of districts as clusters may fur-

ther treated together under one policy.

Fig. 5: Comparison between number of Primary and Upper PrimarySchool

Fig.6: Clusters based on number of Enrollment in Govt. and Private

Schools

The Data Mining approach based on clustering clearlyindicates significant variations between clusters of districtsfrom another cluster. However the cluster approach could besharpen when data for each district- rural, urban; categorywise-general, OBC, SC,ST, handicapped-visually impairedhearing impaired, mentally retarded are classified on thebasis of social security number to have qualitative approachto entire planning and implementation of “Education for all”program.

Decision tree and IF THAN Rules are used for Classification [78]. In this study various Districts are classified according to their Literacy Rate, Growth Rate and available re-sources. The above classification is based on reported GrossEnrollment Ratio (GER). However, Mahoba, Ambedkar Nagar, Lalitpur, Pratapgarh, Barabanki is placed in very goodclass where Gross Enrollment Ratio (GER) is between 101 to118.99 and Lucknow, Varanasi, Meerut, Gaziabad, Allahabad, Gautam Buddha Nagar find a place in very poor category where Gross Enrollment Ratio (GER) is in between 45 to60.99. It appears that the above position is due to migrationof learners from one district to another district, where theyfind better educational facilities. The Data Mining could also

establish the impact of migration from one district to anothe






when all the students are given unique identificationthrough social security number. The Data in Data Warehousebased on social security number will eliminate any scope forduplication and obviously the Data Warehouse developedon the basis of social security number will be more reliableand dependable for strategic planning for improving thepercentage education in primary sector through “Education

for All” scheme.

Fig.7: Categorization of district according to Gross Enrollment Ratio by

using Decision Tree

5 ConclusionIndian scenario is converting now in the form of an efficient,accountable and transparent society. It is essential that allgovernment functions use ICTs to provide better interfacesor interactions for the public at state and central level. It in-dicates that appropriate software has to be developed whichincludes common practices related with government func-tions. Data Warehousing and Data Mining has been estab-lished to be an excellent option for speeding up reportingand integrating data from various department of any gov-ernment.

The use of Data Mining in government departmentpresents several potential advantages for better administra-tion, including timely access to evaluate data. Different de-partments may quickly identify troublesome trends in its

functions and evaluate why they are occurring.The variousdepartments may associate this information with trends intheir future policies.

The use of Knowledge Discovery in Databases allows anindividual department to use this information in makingappropriate decisions and enhance the working methodolo-gies. This, unquestionably, translates into increased efficien-cy, higher progress rates, and economical society.

Along with the development of the relatively new E Go-vernance Model based on Data Mining and Data Warehous-ing, it is also important to determine multiple rules and poli-

cies for future implementation and better administration

which is based on experiences as well as quicker data analysis methods.

The study shows that in top 20 competitive nations education, Sweden, Japan, USA, Norway and Canada are very good positions. All these countries are using Data Mining techniques for studying, monitoring and evaluating di

ferent ongoing projects for the development of future strtegic planning. Previously it was understood that the coutries having better education level were also having bettGDP factor. But, recent studies have found that increases educational achievements are not linked to the economgrowth. It is also found that the primary level of educationnot going to affect on economic growth of the country.

The importance of Data Warehouse and effective DaMining should be obvious especially when there is delapractically in all the developmental activities which generaly fail in achieving the target as per schedule. The DaWarehouse and Data Mining technique will have to brooted through dynamic process to ensure implementatioas per schedule. The Data Warehouse and Data Mining wialso ensure the efficacy of monitoring, control and evalution, as integrating tool to achieve the target. The frequencintensity, sensitivity of monitoring and control will have be in dynamic mode all the time to ensure completion of thtask as per targeted schedule.

In fact Data Mining with Data Warehousing should be aongoing process. It should be integrated with strategic futristic planning of the entire government. The analysthrough Data Mining would clearly establish the strong anweak areas of planning and implementation of the whogovernment process. However, it would take some time

develop appropriate Data Warehouse of the past data to cary out qualitative analysis on the basis of Data Mining techniques.

The entire process of Data Warehouse development fany application may be based on the basis of unique identication of critical species, i.e., the citizen of the nation with nduplication of the process. Similarly, since district is the ceter of implementation, all the development action, regulatofunction of various departments, as well as social welfaactivities should be quantitatively associated with the uniquidentification with each development activities so that all thdevelopmental activities are completed as per targeted da

for the utilization by their stakeholders.

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Sonali Agarwal is a lecturer in Indian Institute of Information Techology,Allahabad, India. She received her bachelor Degree in Electrical Engi-neering in 1997 at Bhilai Institute of Technology, India and her MastersDegree in Computer Science at the Motilal Nehru National Institute oftechnology, Allahabad, India in 2000. Her research interests includeData Mining, Data Warehousing, E Governance, Knowledge Manage-ment and Support Vector Machine.

G. N. Pandey is Adjunct professor in Indian Institute of InformationTechology, Allahabad, India. He received his bachelor degree in Chemi-cal Engineering in 1962 at Banaras Hindu University, Varanasi, India andhis Masters Degree at Indian Institute of Technology, Kharagpur, India in

1963. He received his Doctoral degree in 1966 at Banaras Hindu Uni-

versity, Varanasi, and Post Doctoral degree at University of MichiganUSA. He worked as a Reader/Lecturer in Chemical Engineering, Banaras Hindu University, Varanasi, India, Director, Institute of Engineering &Technology, Lucknow, India and Founder Vice-Chancellor, JRH University, Chitrakoot, India. His research interest includes ERP, E GovernanceData Mining and Envionmental Science and Engineering.G.N. Pandey isthe author of 12 books and more than 200 research papers.

Studies on E Governance in India using Data Mining Perspective

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