DESIGN AND IMPLEMENTATION OF A COMPUTERISED LIBRARY STOCK MATCHING SYSTEM                                                           TABLE OF  CONTENTTitle Page    CertificationDeclarationDedicationAcknowledgementAbstractTable of ContentList of FiguresList of TableChapter 1: Introduction1.0.0    General Introduction1.1.0    Aim Of Research1.2.0    Objective Of Study 1.3.0   Scope Of Research1.4.0   Limitation Of Research1.5.0  Justification Of Research1.6.0    Definition Of TermsChapter 2: Literature Review2.1.0    Introduction2.2.0    Brief History Of Data Matching2.3.0    Brief History Of University Of Calabar Library 2.3.1    Physical Facilities2.3.2    Library Holdings2.4.0 Existing Data Matching 2.4.1 Health Section2.4.2 National Security2.4.3 Crime And Fraud Detection2.4.4  Business Mailing  List2.4.5 Social Science AndGeneology2.5.0 Problems Of Data Matching2.6.1 Solutions Of Problems Of Data Matching Data Matching Deduplication2.6.1.2 Merge-Consolidate Duplicates2.6.1.3 Data Matching-File Comparison2.6.1.4 Data Matching- Entry Checks2.7.0  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 Policy2.8.1.2  Running A Matching Project2.9.0 Importance Of Matching2.10.0 Data Matching Improvement Cycle Data  Transfer2.10.1.2 Data Format Data Pre Processing2.10.3 Solution Design (Including Algorithm) 2.10.4  Knowledge Sharing And Data Matching Improvements 2.10.5 Improved Data QualityChapter 3: System Design Methodology 3.1.0    Introduction3.2.0    System Requirements Specification3.3.0    System Design3.3.1.0    Logical Design3.3.1.1    Input Design3.3.1.2    Output Design3.3.1.3    Menu Design3.3.1.4    Use Case Diagram3.3.1.5    Activity Diagram3.3.1.6  Physical Design3.3.2.1    Program Specification3.3.2.2    System Controls    Layout of Files Design    Database StructureChapter 4: System Implementation4.1.0    Introduction4.2.0    Features and Choice of Implementation Language4.3.0    System Testing Strategies4.3.1    Unit Test4.3.2    Integration Test 4.4.0    Target Computer System Requirements4.5.0    Software Maintenance Issues4.5.1    Corrective Maintenance4.5.2    Preventive Maintenance4.5.3    Adaptive MaintenanceChapter 5: Recommendation and Conclusion5.1.0    Introduction5.2.0       Recommendation5.3.0       ConclusionReferencesAppendix A:  Sample Output Appendix B:     Source Code Listing            CHAPTER ONE1.0    INTRODUCTIONData 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).1.1   AIM OF RESEARCHThis 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. 1.2   OBJECTIVE OF RESEARCH1.    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).1.3   SCOPE OF RESEARCHThe 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).1.4   LIMITATIONS OF RESEARCHThere 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.    PrivacyLACK OF UNIQUE IDENTIFIER    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).COMPUTATION COMPLEXITY    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.LACK OF TRAINING DATA CONTAINING THE TRUE MATCH STATUS    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).PRIVACY AND CONFIDENTIALITY    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).1.5.0    JUSTIFICATION OF THE RESEARCH    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.1.6.0    TERMS ASSOCIATED WITH DATA MATCHING1.    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.     





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