The Open UniversitySkip to content
 

Propagation of Policies in Rich Data Flows

Daga, Enrico; d'Aquin, Mathieu; Gangemi, Aldo and Motta, Enrico (2015). Propagation of Policies in Rich Data Flows. In: Proceedings of the 8th International Conference on Knowledge Capture (K-CAP 2015), pp. 1–8.

Full text available as:
[img]
Preview
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (328kB) | Preview
URL: http://dx.doi.org/10.1145/2815833.2815839
DOI (Digital Object Identifier) Link: https://doi.org/10.1145/2815833.2815839
Google Scholar: Look up in Google Scholar

Abstract

Governing the life cycle of data on the web is a challenging issue for organisations and users. Data is distributed under certain policies that determine what actions are allowed and in which circumstances. Assessing what policies propagate to the output of a process is one crucial problem. Having a description of policies and data flow steps implies a huge number of propagation rules to be specified and computed (number of policies times number of actions). In this paper we provide a method to obtain an abstraction that allows to reduce the number of rules significantly. We use the Datanode ontology, a hierarchical organisation of the possible relations between data objects, to compact the knowledge base to a set of more abstract rules. After giving a definition of Policy Propagation Rule, we show (1) a methodology to abstract policy propagation rules based on an ontology, (2) how effective this methodology is when using the Datanode ontology, (3) how this ontology can evolve in order to better represent the behaviour of policy propagation rules.

Item Type: Conference or Workshop Item
ISBN: 1-4503-3849-6, 978-1-4503-3849-3
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 45432
Depositing User: Kay Dave
Date Deposited: 04 Mar 2016 14:31
Last Modified: 13 Sep 2017 08:22
URI: http://oro.open.ac.uk/id/eprint/45432
Share this page:

Altmetrics

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU