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

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Our lives are becoming increasingly connected with Big Data. Massive amounts of digital trace data are being generated from the activities and events of complex socio-technical systems consisting of human actors and man-made artifacts (which refer to as social objects). Such complexity comes from the massively interconnected and computed nature of the contemporary digital world. These data sets are different from traditional data as they are typically massive, unstructured, granular, heterogeneous, dynamic, and performative. Using Big Data, the researchers are able to understand and predict behaviors of complex socio-technical systems. To support such efforts, the researchers will build a new methodological framework based on an evolutionary ontology that treats variation as real and as the fuel of evolution. Specifically, the researchers analyze data set from Twitter (one of the largest social media sites) and Github (the largest open source community) to test and validate their framework. As the role of Big Data continues to increase in our society, the researchers plan to develop online curricula to help students learn how to access, manage, analyze, and visualize big data sets via a variety of approaches.The researchers 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. They 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. The researchers test their 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. The model generated from this research can be used for other types of massive digital trace data including sensor data from Internet of the Things and mobile data from smartphones.