Statistical Methods for Use in Analysis of Trust-Skyline Sets

摘要

Volume and veracity of Resource Description Framework (RDF) data in the web are two main issues in managing information. Due to the diversity of RDF data, several researchers enriched the basic RDF data model with trust information to rate the trustworthiness of the collected data. This paper is an extension of our previous work in which we extended Trust-Skyline queries over RDF data. We are interested in analyzing the trust-Skyline list. We particularly study the user-defined trust measure (a) problem, which consists in checking the impact of such measure on the resulting list. To this end, we first distinguish between the trust-Skyline points, we propose two main categories, points that enter to the final list after the Pareto-dominance check and points that have trust measures less than a. Then, we proposed statistical methods to investigate the trust measures dependence. Indeed we used the central tendency measures, and the measures of spread for such analysis. Experiments led on the algorithm's implementations showed promising results.