The Experts below are selected from a list of 66 Experts worldwide ranked by ideXlab platform
Ephias Ruhode - One of the best experts on this subject based on the ideXlab platform.
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Data Governance: A Challenge for Merged and Collaborating Institutions in Developing Countries
2017Co-Authors: Thandi Charmaine Mlangeni, Ephias RuhodeAbstract:Organisations now invest in ICT solutions to drive business activities and to provide the agility sought within changing environments. Owing to many reasons including inadequate financial resources, organisations in developing countries are characterised by mergers of two or more institutions. It means therefore that disparate systems with different data management schemes are merged or made to collaborate making access to quality data almost impossible. In turn, a level of inefficiency finds its way with potential to generate inaccurate, missing, misinterpreted and poorly defined information. This research is motivated by the need to investigate data governance challenges in institutions within developing countries that are characterised by complex dynamics rooted in merged and collaborating environments. The study has been empirically scoped to explore data governance challenges in a large university of technology in the Western Cape Region of South Africa as a developing country. The challenges with regards to ICT and data governance are equally applicable in higher education institutions as they do in business organisations. Higher education institutions have a growing ICT infrastructure used in everyday activities and online functionality, making them prone to data problems. Challenges related to data management in universities are a lot more pronounced in universities which were established through the merging of independent institutions and also those that exchange data through collaborations. Thematic analysis has been employed within the theoretical lens of two models, contingency model (Wende and Otto 2007) and the data governance decision domain model (Khatri and Brown 2010). Analysis of data through the two models led to the development of a data governance framework applicable to the case under study and deemed to apply to any organisation in the same context. Challenges related to data principles, data access, data quality, data Integration, Metadata, data lifecycle, and design parameters emerged as the main findings from the study. Since the institution under study was established through a merger of independent technikons, the findings were deemed to be applicable to many other institutions where mergers and collaborations characterise their environment.
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ICT4D - Data Governance: A Challenge for Merged and Collaborating Institutions in Developing Countries
Information and Communication Technologies for Development, 2017Co-Authors: Thandi Charmaine Mlangeni, Ephias RuhodeAbstract:Organisations now invest in ICT solutions to drive business activities and to provide the agility sought within changing environments. Owing to many reasons including inadequate financial resources, organisations in developing countries are characterised by mergers of two or more institutions. It means therefore that disparate systems with different data management schemes are merged or made to collaborate making access to quality data almost impossible. In turn, a level of inefficiency finds its way with potential to generate inaccurate, missing, misinterpreted and poorly defined information. This research is motivated by the need to investigate data governance challenges in institutions within developing countries that are characterised by complex dynamics rooted in merged and collaborating environments. The study has been empirically scoped to explore data governance challenges in a large university of technology in the Western Cape Region of South Africa as a developing country. The challenges with regards to ICT and data governance are equally applicable in higher education institutions as they do in business organisations. Higher education institutions have a growing ICT infrastructure used in everyday activities and online functionality, making them prone to data problems. Challenges related to data management in universities are a lot more pronounced in universities which were established through the merging of independent institutions and also those that exchange data through collaborations. Thematic analysis has been employed within the theoretical lens of two models, contingency model (Wende and Otto 2007) and the data governance decision domain model (Khatri and Brown 2010). Analysis of data through the two models led to the development of a data governance framework applicable to the case under study and deemed to apply to any organisation in the same context. Challenges related to data principles, data access, data quality, data Integration, Metadata, data lifecycle, and design parameters emerged as the main findings from the study. Since the institution under study was established through a merger of independent technikons, the findings were deemed to be applicable to many other institutions where mergers and collaborations characterise their environment.
Thandi Charmaine Mlangeni - One of the best experts on this subject based on the ideXlab platform.
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Data Governance: A Challenge for Merged and Collaborating Institutions in Developing Countries
2017Co-Authors: Thandi Charmaine Mlangeni, Ephias RuhodeAbstract:Organisations now invest in ICT solutions to drive business activities and to provide the agility sought within changing environments. Owing to many reasons including inadequate financial resources, organisations in developing countries are characterised by mergers of two or more institutions. It means therefore that disparate systems with different data management schemes are merged or made to collaborate making access to quality data almost impossible. In turn, a level of inefficiency finds its way with potential to generate inaccurate, missing, misinterpreted and poorly defined information. This research is motivated by the need to investigate data governance challenges in institutions within developing countries that are characterised by complex dynamics rooted in merged and collaborating environments. The study has been empirically scoped to explore data governance challenges in a large university of technology in the Western Cape Region of South Africa as a developing country. The challenges with regards to ICT and data governance are equally applicable in higher education institutions as they do in business organisations. Higher education institutions have a growing ICT infrastructure used in everyday activities and online functionality, making them prone to data problems. Challenges related to data management in universities are a lot more pronounced in universities which were established through the merging of independent institutions and also those that exchange data through collaborations. Thematic analysis has been employed within the theoretical lens of two models, contingency model (Wende and Otto 2007) and the data governance decision domain model (Khatri and Brown 2010). Analysis of data through the two models led to the development of a data governance framework applicable to the case under study and deemed to apply to any organisation in the same context. Challenges related to data principles, data access, data quality, data Integration, Metadata, data lifecycle, and design parameters emerged as the main findings from the study. Since the institution under study was established through a merger of independent technikons, the findings were deemed to be applicable to many other institutions where mergers and collaborations characterise their environment.
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ICT4D - Data Governance: A Challenge for Merged and Collaborating Institutions in Developing Countries
Information and Communication Technologies for Development, 2017Co-Authors: Thandi Charmaine Mlangeni, Ephias RuhodeAbstract:Organisations now invest in ICT solutions to drive business activities and to provide the agility sought within changing environments. Owing to many reasons including inadequate financial resources, organisations in developing countries are characterised by mergers of two or more institutions. It means therefore that disparate systems with different data management schemes are merged or made to collaborate making access to quality data almost impossible. In turn, a level of inefficiency finds its way with potential to generate inaccurate, missing, misinterpreted and poorly defined information. This research is motivated by the need to investigate data governance challenges in institutions within developing countries that are characterised by complex dynamics rooted in merged and collaborating environments. The study has been empirically scoped to explore data governance challenges in a large university of technology in the Western Cape Region of South Africa as a developing country. The challenges with regards to ICT and data governance are equally applicable in higher education institutions as they do in business organisations. Higher education institutions have a growing ICT infrastructure used in everyday activities and online functionality, making them prone to data problems. Challenges related to data management in universities are a lot more pronounced in universities which were established through the merging of independent institutions and also those that exchange data through collaborations. Thematic analysis has been employed within the theoretical lens of two models, contingency model (Wende and Otto 2007) and the data governance decision domain model (Khatri and Brown 2010). Analysis of data through the two models led to the development of a data governance framework applicable to the case under study and deemed to apply to any organisation in the same context. Challenges related to data principles, data access, data quality, data Integration, Metadata, data lifecycle, and design parameters emerged as the main findings from the study. Since the institution under study was established through a merger of independent technikons, the findings were deemed to be applicable to many other institutions where mergers and collaborations characterise their environment.
Renée J. Miller - One of the best experts on this subject based on the ideXlab platform.
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The iBench Integration Metadata generator
Proceedings of the VLDB Endowment, 2015Co-Authors: Patricia C. Arocena, Boris Glavic, Radu Ciucanu, Renée J. MillerAbstract:Given the maturity of the data Integration field it is surprising that rigorous empirical evaluations of research ideas are so scarce. We identify a major roadblock for empirical work - the lack of comprehensive Metadata generators that can be used to create benchmarks for different Integration tasks. This makes it difficult to compare Integration solutions, understand their generality, and understand their performance. We present iBench, the first Metadata generator that can be used to evaluate a wide-range of Integration tasks (data exchange, mapping creation, mapping composition, schema evolution, among many others). iBench permits control over the size and characteristics of the Metadata it generates (schemas, constraints, and mappings). Our evaluation demonstrates that iBench can efficiently generate very large, complex, yet realistic scenarios with different characteristics. We also present an evaluation of three mapping creation systems using iBench and show that the intricate control that iBench provides over Metadata scenarios can reveal new and important empirical insights. iBench is an open-source, extensible tool that we are providing to the community. We believe it will raise the bar for empirical evaluation and comparison of data Integration systems.
Patricia C. Arocena - One of the best experts on this subject based on the ideXlab platform.
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The iBench Integration Metadata generator
Proceedings of the VLDB Endowment, 2015Co-Authors: Patricia C. Arocena, Boris Glavic, Radu Ciucanu, Renée J. MillerAbstract:Given the maturity of the data Integration field it is surprising that rigorous empirical evaluations of research ideas are so scarce. We identify a major roadblock for empirical work - the lack of comprehensive Metadata generators that can be used to create benchmarks for different Integration tasks. This makes it difficult to compare Integration solutions, understand their generality, and understand their performance. We present iBench, the first Metadata generator that can be used to evaluate a wide-range of Integration tasks (data exchange, mapping creation, mapping composition, schema evolution, among many others). iBench permits control over the size and characteristics of the Metadata it generates (schemas, constraints, and mappings). Our evaluation demonstrates that iBench can efficiently generate very large, complex, yet realistic scenarios with different characteristics. We also present an evaluation of three mapping creation systems using iBench and show that the intricate control that iBench provides over Metadata scenarios can reveal new and important empirical insights. iBench is an open-source, extensible tool that we are providing to the community. We believe it will raise the bar for empirical evaluation and comparison of data Integration systems.
Ian Williamson - One of the best experts on this subject based on the ideXlab platform.
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Enabling spatial data sharing through multi-source spatial data Integration
2009Co-Authors: Hossein Mohammadi, Abbas Rajabifard, Ian WilliamsonAbstract:The dynamic environment of SDIs and the involvement of diverse spatial data providers present uncertainty for involving organizations. This pushes organizations to focus on cooperative data sharing relationships to deliver their objectives. Spatial data sharing provides transactions in which individuals, governments and businesses obtain access to spatial data and services from other stakeholders. However, spatial data sharing goes beyond simple data exchange and requires the provision of usable datasets. It is specifically important at multi-national level and Global SDI (GSDI). One of the most significant and demanding characteristics of usable datasets is the readiness of spatial datasets for Integration with other datasets. However it is often difficult or even impossible for users to sensibly integrate datasets from different sources. This is because of the diversity of data standards, specifications and arrangements which have been utilized by organizations. Data providers adopt spatial data standards and specifications and establish data sharing arrangement based on their requirements which may differ form other organizations. Therefore, multi-source spatial datasets are associated with technical and non-technical inconsistency and heterogeneity. In order to facilitate the Integration of multi-source spatial datasets, the investigation of the data Integration process, potential barriers and challenges of spatial data Integration and possible enablers and solutions is necessary. This paper aims to provide an investigation on the spatial data Integration as a compelling reason for spatial data sharing. The investigation approach is based on a number of case studies. The case study investigation has also highlighted and identified a number of technical and non-technical barriers and issues of multi-source spatial data Integration. The paper also capitalizes on the analysis of the case study investigation to identify the possible tools, solutions and enablers which can be utilized to facilitate the Integration of multi-source datasets. In this regard, the paper presents a spatial data Integration toolbox. The toolbox consists of a number of components including spatial data validation and Integration tool, associated guidelines; and data Integration Metadata and data specification documents. The design and development of a spatial data validation and Integration tool and associated guidelines have also been presented in the paper.