Improved index compression techniques for versioned document collections

  • Authors:

  • Jinru He;Junyuan Zeng;Torsten Suel

  • Affiliations:

  • New York University, Brooklyn, NY, USA;New York University, Brooklyn, NY, USA;New York University, Brooklyn, NY, USA

  • Venue:
  • CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
  • Year:
  • 2010

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Abstract

Current Information Retrieval systems use inverted index structures for efficient query processing. Due to the extremely large size of many data sets, these index structures are usually kept in compressed form, and many techniques for optimizing compressed size and query processing speed have been proposed. In this paper, we focus on versioned document collections, that is, collections where each document is modified over time, resulting in multiple versions of the document. Consecutive versions of the same document are often similar, and several researchers have explored ideas for exploiting this similarity to decrease index size. We propose new index compression techniques for versioned document collections that achieve reductions in index size over previous methods. In particular, we first propose several bitwise compression techniques that achieve a compact index structure but that are too slow for most applications. Based on the lessons learned, we then propose additional techniques that come close to the sizes of the bitwise technique while also improving on the speed of the best previous methods.