A visualization platform for spatio-temporal data: A data intensive computation framework

Data visualization, as an intuitive approach to help people realize data and knowledge discovering, has been developed with diverse perspectives and objectives, and they may render different analysis results even with the same application case or dataset treated. With the explosive increase of data volume and data dimension, the performance of most of the existing spatio-temporal information visualization toolkits decreases sharply in capacity and efficiency. In this paper, we present a visual analytics platform in data intensive computation environment that supports large-scale spatio-temporal data. By redefining task model, data model, and visual mapping strategies, this platform supports processing and visualizing many kinds of Big Data with spatio-temporal attributes. The processing and visualizing can be done in seconds by distributed storage, data reorganization, distributed query, spatial indices, and segmented fetch, even though it has a terabyte of data. In the experimental implementation, the taxi trajectory dataset with 1TB volume and four typical spatio-temporal queries are used to testify our platform's effectiveness and efficiency.