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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, Dirschl, 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 forWeb-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
Subjects:Computer Science > Artificial intelligence
Computer Science > Software engineering
Computer Science > World Wide Web
Computer Science > Information storage and retrieval systems
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:European Union’s Horizon 2020 research and innovation programme under grant agreement No 644055, Science Foundation Ireland and co-funded by the European Regional Development Fund through the ADAPT Centre for Digital Content Technology [grant number 13/RC/2106
ID Code:22887
Deposited On:20 Feb 2019 11:38 by Rob Brennan . Last Modified 18 Dec 2020 04:30
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