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    Use of linked data model to publish land acquisition data (a case study of Lusalira – Kasambya – Nkonge – Sembabule Road Project)

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    Master's dissertation (3.189Mb)
    Date
    2022-09
    Author
    Mugabo, Dennis Raymond
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    Abstract
    The Uganda National Roads Authority through the Department of Land Acquisition under the Directorate of Roads and Bridges Development is in charge of gathering, administering, and preserving information on the national road projects. Structured data that is linked to other data to increase its value through querying is known as linked data. Relational databases and graph databases differ significantly in that the relationships between the data are maintained in graph databases as discrete bits of information. Likewise, although in a different way, relational databases imply that the emphasis on relationships between data is important. The relational database focus is on the columns of data tables rather than the data pieces. Land Acquisition data currently exists in a scattered format where by the valuation data, land survey data, social-economic data and data about payments for a particular project can be stored in separate locations. The main goal of this study is to explore the use of the Linked data model to store, manage and query this data. Focus group discussions were used to determine the current data maturity level of the LA data as well as determine the competence questions that one would be interested in from the modeled data. The structured data was transformed into RDF turtles using transformation rules that were guided by the physical model of the Class and Subclasses of the data developed in Protégé software. It was determined that all the LA data was currently stored at the One star, Two star and Three star levels of the 5 Star Open Data Model of Linked data. This data was transformed into the Four star level where basically each entry in the data is denoted with Uniform Resource Identifiers (URLs) which provide a unique identifier for each instance of the data. Queries were then performed on the data using SPARQL querying language to validate the data model if it can answer the competence questions. The model was successfully validated therefore creating a Linked data model for the LA data of the pilot project. This indicated that if all the LA data across all the various projects under UNRA is modeled this way, it can easily be stored, managed and stored centrally and once this Linked data is published on the web, it can be accessible from any location however access constraints have to be put in place to cater for data security.
    URI
    http://hdl.handle.net/10570/11797
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