Information Guided Data Sampling and Recovery Using Bitmap Indexing

Creating a data representation is a common approach for efficient and effective data management and exploration. The compressed bitmap indexing is one of the emerging data representation used for large-scale data exploration. Performing sampling on the bitmapindexing based data representation allows further reduction of storage overhead and be more flexible to meet the requirements of different applications. In this paper, we propose two approaches to solve two potential limitations when exploring and visualizing the data using sampling-based bitmap indexing data representation. First, we propose an adaptive sampling approach called information guided stratified sampling (IGStS) for creating compact sampled datasets that preserves the important characteristics of the raw data. Furthermore, we propose a novel data recovery approach to reconstruct the irregular subsampled dataset into a volume dataset with regular grid structure for qualitative post-hoc data exploration and visualization. The quantitative and visual efficacy of our proposed data sampling and recovery approaches are demonstrated through multiple experiments and applications.