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Conjunctive Queries with Free Access Patterns Under Updates

Authors Ahmet Kara , Milos Nikolic , Dan Olteanu , Haozhe Zhang

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Subject Classification

ACM Subject Classification
  • Theory of computation → Database query processing and optimization (theory)
  • Information systems → Database views
  • Information systems → Data streams
Keywords
  • fully dynamic algorithm
  • enumeration delay
  • complexity trade-off
  • dichotomy

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Abstract

We study the problem of answering conjunctive queries with free access patterns under updates. A free access pattern is a partition of the free variables of the query into input and output. The query returns tuples over the output variables given a tuple of values over the input variables.
We introduce a fully dynamic evaluation approach for such queries. We also give a syntactic characterisation of those queries that admit constant time per single-tuple update and whose output tuples can be enumerated with constant delay given an input tuple. Finally, we chart the complexity trade-off between the preprocessing time, update time and enumeration delay for such queries. For a class of queries, our approach achieves optimal, albeit non-constant, update time and delay. Their optimality is predicated on the Online Matrix-Vector Multiplication conjecture. Our results recover prior work on the dynamic evaluation of conjunctive queries without access patterns.

Cite As Get BibTex

Ahmet Kara, Milos Nikolic, Dan Olteanu, and Haozhe Zhang. Conjunctive Queries with Free Access Patterns Under Updates. In 26th International Conference on Database Theory (ICDT 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 255, pp. 17:1-17:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ICDT.2023.17

Author Details

Ahmet Kara
  • Universität Zürich, Switzerland
Milos Nikolic
  • University of Edinburgh, UK
Dan Olteanu
  • Universität Zürich, Switzerland
Haozhe Zhang
  • Universität Zürich, Switzerland

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 682588.

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