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Knowledge Components and Methods for Policy Propagation in Data Flows

Daga, Enrico (2018). Knowledge Components and Methods for Policy Propagation in Data Flows. PhD thesis The Open University.

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DOI (Digital Object Identifier) Link: https://doi.org/10.5281/zenodo.1308710
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Abstract

Data-oriented systems and applications are at the centre of current developments of the World Wide Web (WWW). On the Web of Data (WoD), information sources can be accessed and processed for many purposes. Users need to be aware of any licences or terms of use, which are associated with the data sources they want to use. Conversely, publishers need support in assigning the appropriate policies alongside the data they distribute.
In this work, we tackle the problem of policy propagation in data flows - an expression that refers to the way data is consumed, manipulated and produced within processes. We pose the question of what kind of components are required, and how they can be acquired, managed, and deployed, to support users on deciding what policies propagate to the output of a data-intensive system from the ones associated with its input. We observe three scenarios: applications of the Semantic Web, workflow reuse in Open Science, and the exploitation of urban data in City Data Hubs. Starting from the analysis of Semantic Web applications, we propose a data-centric approach to semantically describe processes as data flows: the Datanode ontology, which comprises a hierarchy of the possible relations between data objects. By means of Policy Propagation Rules, it is possible to link data flow steps and policies derivable from semantic descriptions of data licences. We show how these components can be designed, how they can be effectively managed, and how to reason efficiently with them. In a second phase, the developed components are verified using a Smart City Data Hub as a case study, where we developed an end-to-end solution for policy propagation. Finally, we evaluate our approach and report on a user study aimed at assessing both the quality and the value of the proposed solution.

Item Type: Thesis (PhD)
Copyright Holders: 2017 The Author
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 55762
Depositing User: Enrico Daga
Date Deposited: 20 Jul 2018 08:52
Last Modified: 23 May 2019 19:41
URI: http://oro.open.ac.uk/id/eprint/55762
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