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A model-driven data-analysis architecture enabling reuse and insight in open data

Hoogervorst, R.W.P. (2018) A model-driven data-analysis architecture enabling reuse and insight in open data.

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Abstract:The last years have shown an increase in open data. Organisations can use open data to enhance data analysis, but traditional data solutions are not suitable for data sources not controlled by the organisation. Hence, each external source needs a specific solution to solve accessing its data, interpreting it and provide possibilities verification. Lack of proper standards and tooling prohibits generalization of these solutions. This work designs metamodels to represent different open data sets, allowing this generalization. In addition, a function metamodel is designed to define operations and generate executable code using these generalizations. These functions allow parallel transformation of metadata and data, keeping them synchronized. Using these definitions, a prototype application framework is created. Validation is performed by considering a real-life case study and using the framework to execute the complete data analysis. The framework and its structure proved to be suitable. The transformation structure allows for traceability of the data, as well as automatic documentation of its context. The framework structure and lessons from the prototype show possible improvements for the different metamodels. These provide more expressiveness for defining models, while maintaining the interoperability between different datasets.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/75367
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