Townend, P, Webster, D, Venters, CC et al. (11 more authors) (2013) Personalised provenance reasoning models and risk assessment in business systems: a case study. In: Proceedings - 2013 IEEE 7th International Symposium on Service-Oriented System Engineering, SOSE 2013. 2013 IEEE 7th International Symposium on Service-Oriented System Engineering, SOSE 2013, 25-28 Mar 2013, Redwood City, USA. IEEE , 329 - 334. ISBN 978-1-4673-5659-6
Abstract
As modern information systems become increasingly business- And safety-critical, it is extremely important to improve both the trust that a user places in a system and their understanding of the risks associated with making a decision. This paper presents the STRAPP framework, a generic framework that supports both of these goals through the use of personalised provenance reasoning engines and state-of-art risk assessment techniques. We present the high-level architecture of the framework, and describe the process of systematically modelling system provenance with the W3C PROV provenance data model. We discuss the business drivers behind the concept of personalizing provenance information, and describe an approach to enabling this through a user-adaptive system style. We discuss using data provenance for risk management and treatment in order to evaluate risk levels, and discuss the use of CORAS to develop a risk reasoning engine representing core classes and relationships. Finally, we demonstrate the initial implementation of our personalised provenance system in the context of the Rolls-Royce Equipment Health Management, and discuss its operation, the lessons we have learnt through our research and implementation (both technical and in business), and our future plans for this project.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2013, IEEE. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Provenance; Risk; Trust; Web services |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Institute for Computational and Systems Science (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 02 Oct 2014 10:49 |
Last Modified: | 19 Dec 2022 13:28 |
Published Version: | http://dx.doi.org/10.1109/SOSE.2013.53 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SOSE.2013.53 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:80699 |