EAGER: CISSDA: A Unified Cyberinfrastructure Framework for Scalable Spatiotemporal Data Analytics

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Massive spatiotemporal datasets are often collected using global positioning systems (GPS) and other location-aware devices. Spatiotemporal data mining and analysis have become increasingly important as continued growth in geographic information science and technology enables scientific investigations and decision-making support in a plethora of fields. Big data and extensive computational capabilities are needed to mine and analyze the massive quantities of complex spatiotemporal data collected across multiple scales and used for diverse applications. However, conventional methods and tools for spatiotemporal data mining and analysis are developed primarily using sequential computing, and cannot adequately handle this increasing data intensity, complexity, and diversity of applications. Only by seamlessly harnessing heterogeneous and advanced computing and information infrastructure - cyberinfrastructure - can large and complex spatiotemporal data be efficiently analyzed on a wide scale.This project creates a unified cyberinfrastructure framework by adapting and integrating heterogeneous modalities of computing and information infrastructure (e.g., cloud, high-performance computing, and high-throughput computing) for scalable spatiotemporal data analytics. The framework encompasses two types of novel and complementary capabilities: 1) a suite of methods and algorithms for scalable spatiotemporal data analytics through synthesis of data mining, information network analysis, and parallel and cloud computing; and 2) a geographic information system (GIS) based on advanced cyberinfrastructure (i.e., cyberGIS) to facilitate the use of the methods and algorithms by a large number of users. These novel capabilities help overcome many current limitations in geographic and social science research involving huge amount of spatiotemporal data, and bring forth useful insights for formulating new policies. The framework is designed to gain new fundamental understanding about individual activity patterns and spaces in the domain of environmental health through scalable analysis of massive space-time trajectory data that depict the movement of individuals over space and time. By the ubiquitous use of spatiotemporal data, the project will lead to both transformative and broad impacts on almost all disciplines that employ geospatial technologies for scientific problem solving and decision-making support.