Visual Data Mining in Transportation Using Multiresolution Data Aggregation

Big data pose fundamental challenges to both data mining and data visualization in transportation research. This paper presents a strategy of using multiresolution data aggregation as an efficient representation of large data for visual data mining. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensional tree index. Such a multiresolution data representation has build-in support of data scalability. Existing visualization techniques are extended to support this data representation.