Hardware-Oriented Succinct-Data-Structure for Text Processing Based on Block-Size-Constrained Compression

Abstract

Succinct data structures are introduced to efficiently solve a given problem while representing the data using as little space as possible. However, the full potential of the succinct data structures has not been utilized in software-based implementations due to the large storage size and the memory access bottleneck. This paper proposes a hardware-oriented data compression method based on clustering of blocks. We use a parallel processing architecture to reduce the decompression overhead. According to the evaluation, we achieve 73% and 27% of storage size and memory access reductions respectively.