Big data as a service from an urban information system

Abstract

Big Data has already proven itself as a valuable tool that lets geographers and urban researchers utilize large data resources to generate new insights. However, wider adoption of Big Data techniques in these areas is impeded by a number of difficulties in both knowledge discovery and data and science production. Typically users face such problems as disparate and scattered data, data management, spatial searching, insufficient computational capacity for data-driven analysis and modelling, and the lack of tools to quickly visualize and summarize large data and analysis results. Here we propose an architecture for an Urban Information System (UrbIS) that mitigates these problems by utilizing the Big Data as a Service (BDaaS) concept. With technological roots in High-performance Computing (HPC), BDaaS is based on the idea of outsourcing computations to different computing paradigms, scalable to super-computers. UrbIS aims to incorporate federated metadata search, integrated modeling and analysis, and geovisualization into a single seamless workflow. The system is under active development and is built around various emerging technologies that include hybrid and NoSQL databases, massively parallel systems, GPU computing, and WebGL-based geographic visualization. UrbIS is designed to facilitate the use of Big Data across multiple cities to better understand how urban areas impact the environment and how climate change and other environmental change impact urban areas.