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Fine-Grained Complexity of Regular Path Queries

Authors Katrin Casel , Markus L. Schmid

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ACM Subject Classification
  • Theory of computation → Regular languages
  • Theory of computation → Problems, reductions and completeness
  • Theory of computation → Database query languages (principles)
  • Theory of computation → Data structures and algorithms for data management
Keywords
  • Graph Databases
  • Regular Path Queries
  • Enumeration
  • Fine-Grained Complexity

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Abstract

A regular path query (RPQ) is a regular expression q that returns all node pairs (u, v) from a graph database that are connected by an arbitrary path labelled with a word from L(q). The obvious algorithmic approach to RPQ evaluation (called PG-approach), i. e., constructing the product graph between an NFA for q and the graph database, is appealing due to its simplicity and also leads to efficient algorithms. However, it is unclear whether the PG-approach is optimal. We address this question by thoroughly investigating which upper complexity bounds can be achieved by the PG-approach, and we complement these with conditional lower bounds (in the sense of the fine-grained complexity framework). A special focus is put on enumeration and delay bounds, as well as the data complexity perspective. A main insight is that we can achieve optimal (or near optimal) algorithms with the PG-approach, but the delay for enumeration is rather high (linear in the database). We explore three successful approaches towards enumeration with sub-linear delay: super-linear preprocessing, approximations of the solution sets, and restricted classes of RPQs.

Cite As Get BibTex

Katrin Casel and Markus L. Schmid. Fine-Grained Complexity of Regular Path Queries. In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, pp. 19:1-19:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ICDT.2021.19

Author Details

Katrin Casel
  • Hasso Plattner Institute, Universität Potsdam, Germany
Markus L. Schmid
  • Humboldt-Universität zu Berlin, Germany

Funding

  • Casel, Katrin: Supported by the Federal Ministry of Education and Research of Germany (BMBF) in the KI-LAB-ITSE framework - project number 01IS19066.
  • Schmid, Markus L.: Supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) - project number 416776735 (gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 416776735).

Acknowledgements

We wish to thank the anonymous reviewers for their valuable feedback. In particular, following the reviewer’s comments and suggestions, we have included more background information and comprehensive explanations of certain aspects, which substantially improved the overall exposition of this paper.

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