Compression, indexing, and retrieval for massive string data

  • Authors:

  • Wing-Kai Hon;Rahul Shah;Jeffrey Scott Vitter

  • Affiliations:

  • National Tsing Hua University, Taiwan;Louisiana State University;Texas A&M University

  • Venue:
  • CPM'10 Proceedings of the 21st annual conference on Combinatorial pattern matching
  • Year:
  • 2010

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Abstract

The field of compressed data structures seeks to achieve fast search time, but using a compressed representation, ideally requiring less space than that occupied by the original input data. The challenge is to construct a compressed representation that provides the same functionality and speed as traditional data structures. In this invited presentation, we discuss some breakthroughs in compressed data structures over the course of the last decade that have significantly reduced the space requirements for fast text and document indexing. One interesting consequence is that, for the first time, we can construct data structures for text indexing that are competitive in time and space with the well-known technique of inverted indexes, but that provide more general search capabilities. Several challenges remain, and we focus in this presentation on two in particular: building I/O-efficient search structures when the input data are so massive that external memory must be used, and incorporating notions of relevance in the reporting of query answers.