Workshops Archive

Building n-Dimensional Trees for Resolution-Based Progressive Compression

Workshop: The 11th International Workshop on Data Analysis and Reduction for Big Scientific Data

Authors: Brandon Alexander Burtchell and Martin Burtscher (Texas State University)

Abstract: Floating-point data is typically compressed at strict error bounds to reduce storage cost while facilitating scientific analyses. Unfortunately, this tends to yield large compressed files. In some cases, however, a user might not need the data at a high fidelity. Progressive compression addresses this issue by refactoring the data into a hierarchical series of increasing fidelity, allowing users to download the data at an initial fidelity and subsequently retrieve higher fidelities. This paper studies a resolution-based progressive compression approach that achieves competitive compression ratios against traditional compression methods. Furthermore, it studies how the progression of resolution affects the quality of the data.

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