Self-organising management of user-generated data and knowledge
File(s)
Author(s)
Macbeth, S
Pitt, JV
Type
Journal Article
Abstract
The proliferation of sensor networks, mobile and pervasive computing has provided the technological push for a new class of participatory-sensing applications, based on sensing and aggregating user-generated content, and transforming it into knowledge. However, given the power and value of both the raw data and the derived knowledge, to ensure that the generators are commensurate beneficiaries, we advocate an open approach to the data and intellectual property rights by treating user-generated content, as well as derived information and knowledge, as a common-pool resource. In this paper, we undertake an extensive review of experimental, commercial and social participatory sensory applications, from which we identify that a decentralised, community-oriented governance model is required to support this approach. Furthermore, we show that Ostrom’s institutional analysis and development framework, in conjunction with a framework for self-organising electronic institutions, can be used to give both an architecture and algorithmic base for the requisite governance model, in terms of operational and collective-choice rules specified in computational logic. This provides, we believe, the foundations for engineering knowledge commons for the next generation of participatory-sensing applications, in which the data generators are also the primary beneficiaries.
Date Issued
2014-11-19
Date Acceptance
2014-11-19
Citation
Knowledge Engineering Review, 2014, 30 (3), pp.237-264
ISSN
1469-8005
Publisher
Cambridge University Press
Start Page
237
End Page
264
Journal / Book Title
Knowledge Engineering Review
Volume
30
Issue
3
Copyright Statement
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/3.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
PROPERTY
TRAGEDY
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Publication Status
Published