Modeling data driven interactions on property graph

Abstract

Data Driven Transformation is a process where an organization transforms its infrastructure, strategies, operational methods, technologies, or organizational culture to facilitate and encourage data driven decision-making behaviors. We defined a data driven network as group/groups of people in an organization network working on common projects where making data driven decisions is necessary in the project. One important problem in data driven transformation is how to understand data driven behaviors in the network. Ability to model and compute on a DDIG is crucial for understanding data driven network behaviors. To this end, we modeled a data driven network as a property graph, in which nodes represent users, projects, or data sources and edges represent interactions between them. We termed this Data Driven Interactions Graph and modeled on the Neo4j graph platform. A graph model framework to represent data driven interactions such that a number of network properties can be computed from the proposed graph is introduced. Finally, we discussed data utilization and data collaboration behaviors of the social working network.