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Towards a knowledge driven framework for bridging the gap between software and data engineering
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DOI：10.1016/j.jss.2018.12.017.png)
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Abstract
En 中文
In this paper we present a collection of ontologies specifically designed to model the information exchange needs of combined software and data engineering. Effective, collaborative integration of software and big data engineering for Web-scale systems, is now a crucial technical and economic challenge. This requires new combined data and software engineering processes and tools. Our proposed models have been deployed to enable: tool-chain integration, such as the exchange of data quality reports; cross-domain communication, such as interlinked data and software unit testing; mediation of the system design process through the capture of design intents and as a source of context for model-driven software engineering processes. These ontologies are deployed in web-scale, data-intensive, system development environments in both the commercial and academic domains. We exemplify the usage of the suite on case-studies emerging from two complex collaborative software and data engineering scenarios: one from the legal sector and the other from the Social sciences and Humanities domain. (C) 2018 Published by Elsevier Inc.
Keywords:
Ontologies
Data engineering
Software engineering
Alignment
Integration
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