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Towards a knowledge driven framework for bridging the gap between software and data engineering

Solanki, Monika orcid logoORCID: 0000-0002-2345-449X, Božić, Bojan orcid logoORCID: 0000-0002-4420-1029, Freudenberg, Markus, Kontokostas, Dimitris orcid logoORCID: 0000-0002-2300-4421, Dirsch, Christian and Brennan, Rob orcid logoORCID: 0000-0001-8236-362X (2018) Towards a knowledge driven framework for bridging the gap between software and data engineering. Journal Of Systems And Software, 149 . pp. 476-484. ISSN 0164-1212

Abstract
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 webscale, 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.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Ontologies; Data engineering; Software engineering; Alignment; Integration
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:Elsevier
Official URL:https://doi.org/10.1016/j.jss.2018.12.017
Copyright Information:© 2018 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland, European Unions Horizon 2020 research and innovation programme under grant agreement No 644055, the ALIGNED project
ID Code:24654
Deposited On:19 Jun 2020 10:59 by Vidatum Academic . Last Modified 18 Dec 2020 04:30
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