TABLE OF  CONTENT

Title Page   






Table of Content

List of Figures

List of Table

Chapter 1: Introduction

1.0.0    General Introduction

1.1.0    Aim Of Research

1.2.0    Objective Of Study

1.3.0   Scope Of Research

1.4.0   Limitation Of Research

1.5.0  Justification Of Research

1.6.0    Definition Of Terms

Chapter 2: Literature Review

2.1.0    Introduction

2.2.0    Brief History Of Data Matching

2.3.0    Brief History Of University Of Calabar Library

2.3.1    Physical Facilities

2.3.2    Library Holdings

2.4.0 Existing Data Matching

2.4.1 Health Section

2.4.2 National Security

2.4.3 Crime And Fraud Detection

2.4.4  Business Mailing  List

2.4.5 Social Science AndGeneology

2.5.0 Problems Of Data Matching

2.6.1 Solutions Of Problems Of Data Matching Data Matching Deduplication Merge-Consolidate Duplicates Data Matching-File Comparison Data Matching- Entry Checks

2.7.0  Feasibility Of Research

2.7.1  Technology Feasibility Analysis

2.8.0 Data Matching Architecture

2.8.1 How To Perform Data Matching Building A Matching Policy  Running A Matching Project

2.9.0 Importance Of Matching

2.10.0 Data Matching Improvement Cycle Data  Transfer Data Format Data Pre Processing

2.10.3 Solution Design (Including Algorithm)

2.10.4  Knowledge Sharing And Data Matching Improvements

2.10.5 Improved Data Quality

Chapter 3: System Design Methodology

3.1.0    Introduction

3.2.0    System Requirements Specification

3.3.0    System Design    Logical Design    Input Design    Output Design    Menu Design    Use Case Diagram    Activity Diagram  Physical Design    Program Specification    System Controls    Layout of Files Design    Database Structure

Chapter 4: System Implementation

4.1.0    Introduction

4.2.0    Features and Choice of Implementation Language

4.3.0    System Testing Strategies

4.3.1    Unit Test

4.3.2    Integration Test

4.4.0    Target Computer System Requirements

4.5.0    Software Maintenance Issues

4.5.1    Corrective Maintenance

4.5.2    Preventive Maintenance

4.5.3    Adaptive Maintenance

Chapter 5: Recommendation and Conclusion

5.1.0    Introduction

5.2.0       Recommendation

5.3.0       Conclusion


Appendix A:  Sample Output

Appendix B:     Source Code Listing

            CHAPTER ONE


Data matching process enables an analyst to reduce data duplication and improve data source. Matching analyses the degree of duplication in all records of a single data sources, returning weighted probabilities of a match between each set of records compared. You can then decide which records are matched and take the appropriate action in the source data. De Andes (1993).

Matching a data comes with several benefits which includes the following: it enables elimination of differences between data values that should be equal determining the correct values and reducing the errors that data differences can cause. For example names and addresses are often the identifying data for overtime. Performing matching to identify and correct these errors can make use and maintenance easier.                                                  (Winkler 1993).

Data matching also enables the names of books in the library that are equivalent but were entered in a different style of format will be rendered uniform. It is also necessary to know that data matching and merging records that correspond to the same entities from several databases. Most times the entities under consideration are commonly people, such as patients, customers, tax payers or travellers but for now this research will be considering data matching in the a library scene.

This research involves the full or partial integration of two or more data sets on the basis of information held in common. It enables data obtained from separate sources to be used more effectively thereby enhancing the value of the original sources. Data matching can also reduce the potential burden on data provided by reducing the need for further data collection. However, where data matching involves the integration of records for the same units. The product of the research will raise important issues about confidentiality and security. Copas J.R; & F.J Hilton (1990).


This project aims at creating and developing a computerized matching record for the university library. In developing a data match for the school library attempts will be made to achieve absolute confidence in the accuracy, completeness, robustness and consistency overtime of these identifiers, because any error in such an identifier will result in wrongly matched records.


1.    Common entity identifier will be used in the database to be matched and in order to achieve these attributes that contain partially identified information, such as name of publisher, location of publication and dates of publication will be used. The name and brief details of the writer could also be used Winkler (1986, 1987).

2.    Rather than develop a special survey to collect data for policy decisions, data from available books sources will be matched which have potential advantages because it contains greater amount of data and their data might be more accurate due to improvement over some period of years Swain et al (1992).


The research sets out how all those involved in the production of data matching for uncial library will meet their commitment to protect the confidentiality of data within their care whilst also, and where appropraite, maximizing the value of those data through data matching.Coper, W.S & M.E Maron (1987).


There are several limitations that would be encountered during these research work and    thereafter. Some of these challenges are:

1.    Lack of unique entity identifier and data quality.

2.    Computation complexity.

3.    Lack of training data containing the true match status.

4.    Privacy


    Generally, the databases to be matched/de-duplicated does not contain unique entry identifiers or keys.Even when entity identifier are available in the databases to be matched, one must be absolutely confident in the accuracy, completeness, robustness and consistency over time of these identifiers, because any error in such as identifiers will result in wronly matched record.

    Finally, if no entity identifiers are available in the databases to be matched then the matching needs to rely upon the attribute that are common across the databases. Decurre.Y(1998).


    When matching the databases pontentially each record from one database needs to be compared with all the records in the other database in order to determine if a pair of records correspond to the same entity or not. The computation complexity of data matching therefore grows quadratically as the databases to be matched gets large.


    In many data matching applications, the true status of two records that are matched across the two databases is not known, that is to say that there is no ground truth or gold in the standard data available that specifies if two records correspond to the same entity or not. Without extra information one  cannot be sure that the outcomes of a data matching project are correct. Deming, W.E & G.J Glesser(1959).


    As previously mentioned, with data matching commonly relying on personal information such as names, addresses, dates, privacy and confidentiality need to be carefully considered. The analysis of matched data has the potential to uncover aspects of individuals or group of entities that are not obvious when a single database is analysed seperately. (Harberman,S.J ()1975).


    One of the important reasons why the research is necessary and reasonable is it enables users to eliminate differences between data values that should be the same, determining the  correct values and reducing the errors that data differences can cause. Another reason while these topic is justified is that it ensures that values that are equivalent, but were entered in a different format or style, are rendered uniform . Hill,T.(1991).

    Futhermore, there will be avoidance of duplicate records in a database where different identifiers are used for the same entity(Fellegi 1999). Finally, data matching identifies exact and approximate matches, enabling the user or administrator to remove duplicate data as it is being defined.


1.    key : the combination of data fields which are the basis of comparison in a data matching application.

2.    Matched Results:  the set of matched records produced by a data matching application.

3.    Matched Records:  Two or more records brought together as a match.

4.    Name Inconsistencies:  When the same individual is recorded with varied identity datail by different agencies.

5.    Name Tokens :  A component of the full or raw name such as family name, first given name or title.

6.    Name Type:  Describes the nature of a name used currently or previously by an individual such as legal, maiden name or an alais.

7.    Non matched records : Records for which data matching application failed to find a matching record in one or more other data files  N/B:  This is not to say that a record for the individual does not exists elsewhere, only that the application failed to find one.

8.    Profile groups: In the interpretation of identity data matching results, the allocation of matched records to particular groups depending on the ways in which matching records was obtained. Used to better allocate resource to subsequent processing of results.

9.    Unicode standard :  A character code 1-4 bytes that defines every character. In most of the speaking languages in the world

10.    Data matching :  The bringing together of data from different sources and comparing it.

11.    Data topology:  The order relationship of specific items of data to other items of data.

12.    Address elements : The individual component elements/fields of an address string e.g street number, street name, street type, town/suburb.

13.    Algorithm : A set of logic rules determined during the design phase of a data matching application. The ‘blueprint’ used to turn logic rules into computer instructions that detail what step to perform in what order.

14.    Application:  The final combination of software and hardware which performs the data matching.

15.    Control group : In data matching context, a set of records of a known type (e.g previous identfied fraudulent identities, decreased individuals) which are used to better interprete data matching results.

16.    Cross Agency :  The matching of data from one agency with those of one or more other agencies.

17.    Data matching database: A structured collection of records or data that is stored in a computer system.

18.    Data cleansing: The proactive identification and correction of data quality issues which affect an agency’s ability to effectively use its data.

19.    Data integrity : The quality of correctness, completeness and complain with the intention of the creators of the data i.e ‘fit for purpose’

20.    Enrollment :  The process of an individual to enroll aith an agency. Involves the initial collection of identifying details.     



RESEARCHWAP.COM is an online repository for free project topics and research materials, articles and custom writing of research works. We’re an online resource centre that provides a vast database for students to access numerous research project topics and materials. guides and assist Postgraduate, Undergraduate and Final Year Students with well researched and quality project topics, topic ideas, research guides and project materials. We’re reliable and trustworthy, and we really understand what is called “time factor”, that is why we’ve simplified the process so that students can get their research projects ready on time. Our platform provides more educational services, such as hiring a writer, research analysis, and software for computer science research and we also seriously adhere to a timely delivery.


Please feel free to carefully review some written and captured responses from our satisfied clients.

  • "Exceptionally outstanding. Highly recommend for all who wish to have effective and excellent project defence. Easily Accessable, Affordable, Effective and effective."

    Debby Henry George, Massachusetts Institute of Technology (MIT), Cambridge, USA.
  • "I saw this website on facebook page and I did not even bother since I was in a hurry to complete my project. But I am totally amazed that when I visited the website and saw the topic I was looking for and I decided to give a try and now I have received it within an hour after ordering the material. Am grateful guys!"

    Hilary Yusuf, United States International University Africa, Nairobi, Kenya.
  • " is a website I recommend to all student and researchers within and outside the country. The web owners are doing great job and I appreciate them for that. Once again, thank you very much "" and God bless you and your business! ."

    Debby Henry George, Massachusetts Institute of Technology (MIT), Cambridge, USA.
  • "I love what you guys are doing, your material guided me well through my research. Thank you for helping me achieve academic success."

    Sampson, University of Nigeria, Nsukka.
  • " is God-sent! I got good grades in my seminar and project with the help of your service, thank you soooooo much."

    Cynthia, Akwa Ibom State University .
  • "Great User Experience, Nice flows and Superb functionalities.The app is indeed a great tech innovation for greasing the wheels of final year, research and other pedagogical related project works. A trial would definitely convince you."

    Lamilare Valentine, Kwame Nkrumah University, Kumasi, Ghana.
  • "Sorry, it was in my spam folder all along, I should have looked it up properly first. Please keep up the good work, your team is quite commited. Am grateful...I will certainly refer my friends too."

    Elizabeth, Obafemi Awolowo University
  • "Am happy the defense went well, thanks to your articles. I may not be able to express how grateful I am for all your assistance, but on my honour, I owe you guys a good number of referrals. Thank you once again."

    Ali Olanrewaju, Lagos State University.
  • "My Dear Researchwap, initially I never believed one can actually do honest business transactions with Nigerians online until i stumbled into your website. You have broken a new legacy of record as far as am concerned. Keep up the good work!"

    Willie Ekereobong, University of Port Harcourt.
  • "WOW, SO IT'S TRUE??!! I can't believe I got this quality work for just 3k...I thought it was scam ooo. I wouldn't mind if it goes for over 5k, its worth it. Thank you!"

    Theressa, Igbinedion University.
  • "I did not see my project topic on your website so I decided to call your customer care number, the attention I got was epic! I got help from the beginning to the end of my project in just 3 days, they even taught me how to defend my project and I got a 'B' at the end. Thank you so much, infact, I owe my graduating well today to you guys...."

    Joseph, Abia state Polytechnic.
  • "My friend told me about ResearchWap website, I doubted her until I saw her receive her full project in less than 15 miniutes, I tried mine too and got it same, right now, am telling everyone in my school about, no one has to suffer any more writing their project. Thank you for making life easy for me and my fellow students... Keep up the good work"

    Christiana, Landmark University .
  • "I wish I knew you guys when I wrote my first degree project, it took so much time and effort then. Now, with just a click of a button, I got my complete project in less than 15 minutes. You guys are too amazing!."

    Musa, Federal University of Technology Minna
  • "I was scared at first when I saw your website but I decided to risk my last 3k and surprisingly I got my complete project in my email box instantly. This is so nice!!!."

    Ali Obafemi, Ibrahim Badamasi Babangida University, Niger State.
  • To contribute to our success story, send us a feedback or please kindly call 2348037664978.
    Then your comment and contact will be published here also with your consent.

    Thank you for choosing