An SDI Approach for Big Data Analytics: The Case on Sensor Web Event Detection and Geoprocessing Workflow

In the big data era, scientific and social data could complement each other for enhanced data analysis and scientific discovery. Such capabilities could be achieved by taking an infrastructure-based approach, compared to existing algorithm-based approaches. This paper investigates how scientific and social data could work together in a spatial data infrastructure (SDI) enabled by interoperable services. It takes a human-as-sensor perspective and treats the social data as a special kind of sensor data, which could be mined and used for event detection in the Sensor Web environment. Sensor Web, social data mining, and geoprocessing workflows are combined together for timely decision support from social and sensor data. The result is an SDI approach for big data analytics. A use case on haze-related data mining and analysis illustrates the applicability of the approach.

I. Introduction

In the big data era, scientific and social data could complement each other for enhanced data analysis and scientific discovery. For example, when thick fog and haze shroud some areas, we could see an increasing social engagement from the Web and online social media, either commenting or communicating the environmental situation. The event detected from the social data could alert and trigger analysis processes on scientific observation data, or confirm results from scientific analysis. The whole process could be accomplished using information technologies and infrastructure services. In the past decade, information infrastructures, such as spatial data and information infrastructure, e-science, and cyberinfrastructure, have been benefiting scientific communities and supporting scientific research. The infrastructures could be used to exploit the big data including social data.