A Methodology for Resolving Heterogeneity and Interdependence in Data Analytics

Abstract

The big data analytics achieves wide application in a number of areas due to its capability in uncovering hidden patterns, correlations and insights through integrating multiple data sources. However, the interdependence and heterogeneity features of these data sources pose a big challenge in managing these data sources to support “last mile” analytics in decision making and value co-creation which are usually with multiple perspectives and at multiple granularities. In this paper, we propose a unified knowledge representation framework, namely, Cyber-Entity (Cyber-E) modeling, to capture and formalize selected behaviors of real entities in both the social and physical worlds to the cyber analytic space. Its special features include not only the stateful, intra- properties of a Cyber-E, but also the inter-relationship and dependence among them. A grouping mechanism, called Cyber-G, is also introduced to support flexible granularity adjustment in the knowledge management. It supports rapid on-demand self-service analytics. An illustrating example of applying this approach in academic research community is given, followed by a case study of two top conferences in service computing area– ICSOC and ICWS– to illustrate the effectiveness and potentials of our approach.