Handling Veracity of Nominal Data in Big Data: A Multipolar Approach

Abstract

With this paper we aim to contribute to the proper handling of veracity, which is generally recognized as one of the main problems related to ‘Big’ data. Veracity refers to the extent to which the used data adequately reflect real world information and hence can be trusted. More specifically we describe a novel computational intelligence technique for handling veracity aspects of nominal data, which are often encountered when users have to select one or more items from a list. First, we discuss the use of fuzzy sets for modelling nominal data and specifying search criteria on nominal data. Second, we introduce the novel concept of a multipolar satisfaction degree as a tool to handle criteria evaluation. Third, we discuss aggregation of multipolar satisfaction degrees. Finally, we demonstrate the proposed technique and discuss its benefits using a film genre example.