Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case

Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

Trust on the Web: Some Web Science Research Challenges

Web Science is the interdisciplinary study of the World Wide Web as a first-order object in order to understand its relationship with the wider societies in which it is embedded, and in order to facilitate its future engineering as a beneficial object. In this paper, research issues and challenges relating to the vital topic of trust are reviewed, showing how the Web Science agenda requires trust to be addressed, and how addressing the challenges requires a range of disciplinary skills applied in an integrated manner

QUAL : A Provenance-Aware Quality Model

The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Closing the loop: assisting archival appraisal and information retrieval in one sweep

In this article, we examine the similarities between the concept of appraisal, a process that takes place within the archives, and the concept of relevance judgement, a process fundamental to the evaluation of information retrieval systems. More specifically, we revisit selection criteria proposed as result of archival research, and work within the digital curation communities, and, compare them to relevance criteria as discussed within information retrieval's literature based discovery. We illustrate how closely these criteria relate to each other and discuss how understanding the relationships between the these disciplines could form a basis for proposing automated selection for archival processes and initiating multi-objective learning with respect to information retrieval

The onus on us? Stage one in developing an i-Trust model for our users.

This article describes a Joint Information Systems Committee (JISC)-funded project, conducted by a cross-disciplinary team, examining trust in information resources in the web environment employing a literature review and online Delphi study with follow-up community consultation. The project aimed to try to explain how users assess or assert trust in their use of resources in the web environment; to examine how perceptions of trust influence the behavior of information users; and to consider whether ways of asserting trust in information resources could assist the development of information literacy. A trust model was developed from the analysis of the literature and discussed in the consultation. Elements comprising the i-Trust model include external factors, internal factors and user's cognitive state. This article gives a brief overview of the JISC funded project which has now produced the i-Trust model (Pickard et. al. 2010) and focuses on issues of particular relevance for information providers and practitioners

A Framework for the Analysis and User-Driven Evaluation of Trust on the Semantic Web

This project will examine the area of trust on the Semantic Web and develop a framework for publishing and verifying trusted Linked Data. Linked Data describes a method of publishing structured data, automatically readable by computers, which can linked to other heterogeneous data with the purpose of becoming more useful. Trust plays a significant role in the adoption of new technologies and even more so in a sphere with such vast amounts of publicly-created data. Trust is paramount to the effective sharing and communication of tacit knowledge (Hislop, 2013). Up to now, the area of trust in Linked Data has not been adequately addressed, despite the Semantic Web stack having included a trust layer from the very beginning (Artz and Gil, 2007). Some of the most accurate data on the Semantic Web lies practically unused, while some of the most used linked data has high numbers of errors (Zaveri et al., 2013). Many of the datasets and links that exist on the Semantic Web are out of date and/or invalid and this undermines the credibility and validity, and ultimately, the trustworthiness of both the dataset and the data provider (Rajabi et al., 2012). This research will examine a number of datasets to determine the quality metrics that a dataset is required to meet to be considered ‘trusted’. The key findings will be assessed and utilized in the creation of a learning tool and a framework for creating trusted Linked Data