Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces

Analytic methodology as to generation of integrated hypotheses is necessary for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we describe generalized difference methods as a methodology for generating integrated hypotheses. Finally, we validate our proposal by applying them to three use cases involving data in concept and real spaces by using our data model as their description guided by generalized difference methods.