A Trust-Powered Technique to Facilitate Scientific Tool Discovery and Recommendation

Jia Zhang, Chris Lee, Petr Votava, Tsengdar J. Lee, Shuai Wang, Venkatesh Sriram, Neeraj Saini, Pujita Rao and Ramakrishna Nemani
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Jia Zhang: Carnegie Mellon University, Silicon Valley, CA, USA
Chris Lee: Carnegie Mellon University, Silicon Valley, CA, USA
Petr Votava: NASA Ames Research Center, Silicon Valley, USA & Science Mission Directorate, NASA Headquarters, Washington, D.C., USA
Tsengdar J. Lee: Science Mission Directorate, NASA Headquarters, Washington, D.C., USA
Shuai Wang: Carnegie Mellon University, Silicon Valley, CA, USA
Venkatesh Sriram: Carnegie Mellon University, Silicon Valley, CA, USA
Neeraj Saini: Carnegie Mellon University, Silicon Valley, CA, USA
Pujita Rao: Carnegie Mellon University, Silicon Valley, CA, USA
Ramakrishna Nemani: NASA Ames Research Center, Silicon Valley, CA, USA

International Journal of Web Services Research (IJWSR), 2015, vol. 12, issue 3, 25-47

Abstract: While the open science community engenders many similar scientific tools as services, how to differentiate them and help scientists select and reuse existing software services developed by peers remains a challenge. Most of the existing service discovery approaches focus on finding candidate services based on functional and non-functional requirements as well as historical usage analysis. Complementary to the existing methods, this paper proposes to leverage human trust to facilitate software service selection and recommendation. A trust model is presented that leverages the implicit human factor to help quantify the trustworthiness of candidate services. A hierarchical Knowledge-Social-Trust (KST) network model is established to extract hidden knowledge from various publication repositories (e.g., DBLP) and social networks (e.g., Twitter and DBLP). As a proof of concept, a prototyping service has been developed to help scientists evaluate and visualize trust of services. The performance factor is studied and experience is reported.

Date: 2015
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