Recognizing credible experts in inaccurate databases

Abstract

While the problem of incomplete data in databases has been extensively studied, a relatively unexplored form of uncertainty in databases, called inaccurate data, demands due attention. Inaccurate data results when data are contributed by various information agents with associated credibility. Though the data itself is total or complete, the reliability of the data now depends on the agents' credibility. Several issues of this form of data reliability has been reported recently where the credibility of agents were assumed to be known, static and uniform throughout the database. In this paper we address the issue of credibility maintenance of information agents and take the view that the agent credibility is dynamic and is a function of the database knowledge, the agent's performance relative to other agents, and the agent's expertise. We present a method to identify agents' field of expertise (called the contexts) and use agents' context dependent credibility to calculate the reliability of the contextual data.

This research was supported in part by grants from the Natural Sciences and Engineering Research Council of Canada and the Fonds Pour Formation De Chercheurs Et L'Aide à La Recherche of Quebec.

This author's research was additionally supported in part by grants from the Canadian Commonwealth Scholarship and Fellowship Plan and the University of Dhaka, Bangladesh. The author is on leave from the University of Dhaka, Bangladesh.