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Document Retrieval Hacks

Authors Simon J. Puglisi, Bella Zhukova

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Author Details

Simon J. Puglisi
  • Department of Computer Science, University of Helsinki, Finland
Bella Zhukova
  • Department of Computer Science, University of Helsinki, Finland

Funding

This work was funded in part by the Academy of Finland via grant 319454.

Acknowledgements

Our thanks go to Dustin Cobas for prompt help in getting his codebase to compile on our system, and to Massimiliano Rossi for assistance with datasets.

Cite As Get BibTex

Simon J. Puglisi and Bella Zhukova. Document Retrieval Hacks. In 19th International Symposium on Experimental Algorithms (SEA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 190, pp. 12:1-12:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.SEA.2021.12

Abstract

Given a collection of strings, document listing refers to the problem of finding all the strings (or documents) where a given query string (or pattern) appears. Index data structures that support efficient document listing for string collections have been the focus of intense research in the last decade, with dozens of papers published describing exotic and elegant compressed data structures. The problem is now quite well understood in theory and many of the solutions have been implemented and evaluated experimentally. A particular recent focus has been on highly repetitive document collections, which have become prevalent in many areas (such as version control systems and genomics - to name just two very different sources).
The aim of this paper is to describe simple and efficient document listing algorithms that can be used in combination with more sophisticated techniques, or as baselines against which the performance of new document listing indexes can be measured. Our approaches are based on simple combinations of scanning and hashing, which we show to combine very well with dictionary compression to achieve small space usage. Our experiments show these methods to be often much faster and less space consuming than the best specialized indexes for the problem.

Subject Classification

ACM Subject Classification
  • Information systems → Data compression
Keywords
  • String Processing
  • Pattern matching
  • Document listing
  • Document retrieval
  • Succinct data structures
  • Repetitive text collections

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References

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