sample selection for dictionary-based corpus compression
Compression of large text corpora has the potential to drastically reduce both storage requirements and per-document access costs. Adaptive methods used for general-purpose compression are ineffective for this application, and historically the most successful methods have been based on word-based dictionaries, which allow use of global properties of the text. However, these are dependent on the text complying with assumptions about content and lead to dictionaries of unpredictable size. In recent work we have described an LZ-like approach in which sampled blocks of a corpus are used as a dictionary against which the complete corpus is compressed, giving compression twice as effective than that of zlib. Here we explore how pre-processing can be used to eliminate redundancy in our sampled dictionary. Our experiments show that dictionary size can be reduced by 50\% or more (less than 0.1\% of the collection size) with no significant effect on compression or access speed.
sampling dictionary compression random access document retrieval