BIGDATA: Multi-level predictive analytics & motif discovery across massive dynamic spatio-temporal networks in complex socio-technical systems: An organizational genetics approach

Supported by the National Science Foundation (NSF BIGDATA NSF-14476570)

Investigators:

Prof. Youngjin Yoo, Management Information Systems Department, Temple University

Prof. Sunil Wattal, Management Information Systems Department, Temple University

Prof. Obradovic Zoran, Data Analytics and Biomedical Informatics Center, Temple University

Rob J. Kulathinal, Biology Department, Temple University

Abstract

We are developing a method to predict the emergence of system-level behaviors by analyzing large volumes of digital trace data using evolutionary social ontology to build a multi-level model of complex socio-technical systems. We use analytical techniques developed in evolutionary biology and systems biology: (1) to characterize a stream of digital trace data from a complex socio-technical system with finite genetic elements; (2) to predict the behavior of socio-technical systems based on the pattern of “behavioral gene” interactions; and (3) to explore the impact of mutational input, gene flow, and recombination in “behavioral genes” on the evolution of socio-technical systems. We test our model in GitHub, one of the largest open source communities that includes over 5 million open source software development projects and Twitter, one of the largest social media site, that has over 500 million messages per day.

We believe our model can be used for other types of massive digital trace data including sensor data from Internet of the Things and mobile user data. Our work will also allow scholars in different field to study the emergence of complex systems behavior through the interaction of low-level events.