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

我们的生活越来越多地与大数据联系在一起。由人类行为者和人造物品(称为社会物品)组成的复杂社会技术系统的活动和事件产生了大量数字痕迹数据。这种复杂性来自当代数字世界的大规模互联和计算本质。这些数据集不同于传统数据，因为它们通常是海量、非结构化、细粒度、异质、动态和可执行的数据。使用大数据，研究人员能够理解和预测复杂社会技术系统的行为。为了支持这些努力，研究人员将建立一个基于进化本体论的新方法框架，该本体论将变异视为现实，并将其视为进化的燃料。具体地说，研究人员分析了来自Twitter(最大的社交媒体网站之一)和Github(最大的开源社区)的数据集，以测试和验证他们的框架。随着大数据在我们社会中的作用不断增加，研究人员计划开发在线课程，帮助学生学习如何通过各种方法访问、管理、分析和可视化大数据集。研究人员正在开发一种方法，通过使用进化社会本体论分析大量数字跟踪数据来预测系统级行为的出现，以构建复杂社会技术系统的多层次模型。他们使用进化生物学和系统生物学中发展起来的分析技术：(1)表征具有有限遗传元素的复杂社会技术系统的数字痕迹数据流；(2)基于“行为基因”相互作用的模式预测社会技术系统的行为；以及(3)探索“行为基因”中的突变输入、基因流动和重组对社会技术系统进化的影响。研究人员在GitHub和Twitter上测试了他们的模型。GitHub是最大的开源社区之一，包括500多万个开源软件开发项目，Twitter是最大的社交媒体网站之一，每天有超过5亿条消息。该模型可用于其他类型的海量数字痕迹数据，包括来自物联网的传感器数据和来自智能手机的移动数据。

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.