A New Histogram-based Technique for Compressing Multi-Dimensional Data

The need to compress data into synopses of summarized information often arises in many application scenarios, where the aim is to retrieve aggregate data efficiently, possibly trading off the computational efficiency with the accuracy of the estimation. A widely used approach for summarizing multidimensional data is the histogram-based representation scheme, which consists in partitioning the data domain into a number of blocks (called buckets), and then storing summary information for each block. In this paper, a new histogram-based summarization technique which is very effective for multi-dimensional data is proposed. This technique exploits a multi-resolution organization of summary data, on which an efficient physical representation model is defined. The adoption of this representation model (based on a hierarchical organization of the buckets) enables some storage space to be saved w.r.t. traditional histograms, which can be invested to obtain finer grain blocks, thus approximating data with more detail.