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Efficient Tree-Structured Categorical Retrieval

Authors Djamal Belazzougui, Gregory Kucherov

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ACM Subject Classification
  • Theory of computation → Pattern matching
  • Information systems → Document representation
  • Information systems → Information retrieval query processing
  • Theory of computation → Data structures design and analysis
Keywords
  • pattern matching
  • document retrieval
  • category tree
  • space-efficient data structures

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Abstract

We study a document retrieval problem in the new framework where D text documents are organized in a category tree with a pre-defined number h of categories. This situation occurs e.g. with taxomonic trees in biology or subject classification systems for scientific literature. Given a string pattern p and a category (level in the category tree), we wish to efficiently retrieve the t categorical units containing this pattern and belonging to the category. We propose several efficient solutions for this problem. One of them uses n(logσ(1+o(1))+log D+O(h)) + O(Δ) bits of space and O(|p|+t) query time, where n is the total length of the documents, σ the size of the alphabet used in the documents and Δ is the total number of nodes in the category tree. Another solution uses n(logσ(1+o(1))+O(log D))+O(Δ)+O(Dlog n) bits of space and O(|p|+tlog D) query time. We finally propose other solutions which are more space-efficient at the expense of a slight increase in query time.

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Djamal Belazzougui and Gregory Kucherov. Efficient Tree-Structured Categorical Retrieval. In 31st Annual Symposium on Combinatorial Pattern Matching (CPM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 161, pp. 4:1-4:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.CPM.2020.4

Author Details

Djamal Belazzougui
  • CAPA, DTISI, Centre de Recherche sur l'Information Scientifique et Technique, Algiers, Algeria
Gregory Kucherov
  • CNRS and LIGM/Univ Gustave Eiffel, Marne-la-Vallée, France
  • Skolkovo Institute of Science and Technology, Moscow, Russia

Funding

GK was partially funded by RFBR, project 20-07-00652, and joint RFBR and JSPS project 20-51-50007.

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