Abordagem ontológica na gestão de dados : estudo de caso CGU

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2018.Este trabalho estuda a utilização da ontologia como abordagem para a gestão e integração de dados, ela é considerada como meio de dominar a complexidade envolvida em ambientes com grande diversidade de fontes de dados. A aplicabilidade variada da ontologia direcionou as investigações deste trabalho no sentido de considerá-la como base de solução apresentada para o estudo de caso considerado: arquitetura da informação para a gestão de dados CGU. A CGU é o Ministério do poder executivo federal com responsabilidade primária pelas ações de controle interno, corretivas e ouvidoria. Além disso, o Órgão é responsável por medidas para promover a transparência e prevenir a corrupção. O desempenho das atividades CGU muitas vezes se faz por meio de consolidação de informações e do cruzamento e mineração de dados. Tais atividades necessitam de uma visão holística e integrada dos seus ativos de dados, porém, a estrutura de gestão de dados existente é elementar e não tem se mostrado efetiva para atender às necessidades CGU. Em razão disso, o Órgão criou, em 2014, um grupo de trabalho para estudar a implementação do processo PO2–Arquitetura da Informação do Cobit 4.1. O grupo apresentou uma proposta de arquitetura da informação, não implementada até o momento, estruturada na forma de um modelo Entidade-Relacionamento (ER) que contemplava metadados das bases de dados do Órgão. No entanto, havia algumas lacunas nesse modelo, as quais motivaram a primeira etapa deste trabalho: uma revisão sistemática explorando o uso de ontologia na gestão de dados. Os resultados obtidos sugestionaram a proposta de adoção de uma abordagem ontológica de gestão de dados e alicerçaram a proposta de modelo para arquitetura da informação subjacente, que se mostrou capaz de trazer melhorias ao modelo originalmente proposto. A principal contribuição deste trabalho para a academia e para outros Órgãos de governo é expor alguns benefícios de se adotar a abordagem ontológica na gestão de dados, com demonstração de aplicabilidade por meio de um protótipo para os domínios de dados da CGU.This work studies the ontology as an approach to the data management and integration, it is considered as a mean to overcome the complexity involved in environments with variety of data sources. The varied applicability of the ontology directed the investigations of this work, which considered it as a base for the solution of the case study: CGU data management information architecture. The Ministry CGU is the arm of the federal executive branch with primary responsibility for internal control, corrective and ombudsman actions. In addition, the agency executes measures to promote transparency and prevent corruption. The performance of its various activities is often done through the consolidation of information, data cross-referencing and mining. Such activities require a holistic and integrated data assets view, nevertheless the existing data management structure is elementary and has not shown to be effective in meeting CGU requirements. As a result, in 2014 the Agency set up a working group to study the Cobit 4.1 PO2 - Information Architecture process implementation. The group presented an information architecture proposal, not implemented until now, structured in the form of a metadata Entity-Relationship (ER) model. However, there were some shortcomings in this model, which motivated the first stage of this work: a systematic review exploring the use of ontology in data management. The obtained results suggested the ontology based data management approach and supported the underlying information architecture proposed, which proved be able to bring about CGU model improvements. The main contribution of this work to the academy and other government offices is to present some ontology based data management benefits, with applicability demonstration by means of CGU data domains prototype

Developing ontology-based decision-making framework for Middle Eastern region HEIs

Decision making is one of the most challenging processes that higher education institutions continuously experience worldwide. Most educational decisions rely mainly on evaluating the academic profile of staff members, which usually includes the academic and research activities of the teacher. The massive amount of scattered educational data, if represented in traditional forms, causes the problem of ambiguity and inaccuracy of decisions. Educational institutions have recently been attempting to apply emerging technologies in the data engineering field to solve as many challenges as possible. In addition, online libraries continuously produce an enormous amount of open scholarly data, including publications, citations, and other research activity records, which could effectively improve the quality of academic decisions when linked with the local data of universities. This thesis presents the academic profiles and course records semantically, and employs them with a scientific knowledge graph as linked data to enrich the internal data and support the decision-making process within universities. The proposed approach is applied to assign courses to the most qualified academic staff as a proof-of-concept experiment. Traditionally, this process is performed manually by heads of departments and is considered time-consuming, especially when the data are in textual format. This research aims to address this challenge. To this end, courses and academic profiles are represented semantically in RDF format, in order to improve the quality of the institutional data. To ensure the efficiency of this process, a survey is conducted to identify the key factors that influence decision making during the distribution of courses among staff members, which was successfully distributed to the heads of departments who actively participated and provided their variable insights into this matter. The survey results indicated that the research areas of academic staff and whether they had taught the course before are the most important factors that are usually considered in this type of decision. Furthermore, this study proves the importance of generating links between local data and external repositories with updated research records to improve the course–teacher assignment process. Linked data technology is applied to combine all the possible information affecting the course–teacher assignment decision from different resources, and the sufficiency of the linked data and the selection of external data are examined using data mining techniques. Two prediction models are developed to predict the most qualified academic teacher to teach each course, with the results being associated with 314 academic teachers and 119 courses from the Faculty of Computing and Information Technology at King Abdulaziz University. According to the obtained accuracy of the models, it is suggested that the performance is improved when the data are enriched with external scholarly open data using LD, with the accuracy increasing from 80.95% to 93.26% after applying LD techniques. Additionally, adding research records of the academic member improved the sensitivity of the models to 89.11% and 97.76%. These improvements demonstrate the importance of considering the research activities of academic members when distributing courses, especially when extracted from external repositories using LD