Interactive Visual Exploration of Large Relational Data: Using multiresolution data aggregations built on a high-dimensional index

Visualization is a powerful tool to capture profound insights. However, interactive visual exploration of massive data in many activities from business decision making to scientific analysis poses fundamental technical challenges to both data visualization and database management systems. In this work, we propose a density-based methodology to address the challenge. We present multiresolution data aggregation as an intermediate representation of data between visualization tools and databases. Data aggregated at multiple resolutions are stored in internal nodes of a partition-based high dimensional tree index while the individual records are stored in leaf nodes. Such a piggyback ride of aggregated data efficiently supports resolution-based data access patterns of large relational data. Multiresolution data aggregation provides density- based data input to visualization techniques. A software tool (mVis) is developed to demonstrate the feasibility and effectiveness of the new input. It is a dynamic, flexible, and extendible framework which currently constitutes several visualization techniques that support the new data representation.