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ForBackBench: a benchmark for chasing vs. query-rewriting

ForBackBench: a benchmark for chasing vs. query-rewriting
ForBackBench: a benchmark for chasing vs. query-rewriting
The problems of Data Integration/Exchange (DE) and Ontology Based Data Access (OBDA) have been extensively studied across different communities. The underlying problem is common: using a number of differently structured data-sources mapped to a mediating schema/ontology/knowledge-graph, answer a query posed on the latter. In DE, forward-chaining algorithms, collectively known as the chase, transform source data to a new materialised instance that satisfies the ontology and can be directly queried. In OBDA, backward-chaining algorithms rewrite the query over the source schema, taking the ontology into account, in order to execute the rewriting directly on the sources. These two reasoning approaches have seen an individual rise in algorithms, practical implementations, and benchmarks. However, there has not been a principled methodology to compare solutions across both areas. In this paper we provide an original methodology and a benchmark infrastructure - a set of test scenarios, generator and translator tools, and an experimental infrastructure - to allow the translation and execution of a DE/OBDA scenario across areas and among different chase and query-rewriting systems. In the process, we also present a syntactic restriction of linear Tuple Generating Dependencies that precisely captures DL-LiteR, a correspondence previously uninvestigated. We perform cross-approach experiments under a wide range of assumptions, such as the use of different source-to-target mapping languages, shedding light to the interplay between forward-and backward-chaining. Our preliminary results show that, indeed, chase can compete and might overcome query rewriting even in the face of large data especially for complex mapping languages.
2150-8097
1519-1532
Alhazmi, Afnan
6403c214-71bb-4694-8079-64c7447c630e
Blount, Tom
7c4e5a1d-d105-4c18-8f02-42bd65e3f3a7
Konstantinidis, George
f174fb99-8434-4485-a7e4-bee0fef39b42
Alhazmi, Afnan
6403c214-71bb-4694-8079-64c7447c630e
Blount, Tom
7c4e5a1d-d105-4c18-8f02-42bd65e3f3a7
Konstantinidis, George
f174fb99-8434-4485-a7e4-bee0fef39b42

Alhazmi, Afnan, Blount, Tom and Konstantinidis, George (2022) ForBackBench: a benchmark for chasing vs. query-rewriting. Proceedings of the VLDB Endowment, 15 (8), 1519-1532.

Record type: Article

Abstract

The problems of Data Integration/Exchange (DE) and Ontology Based Data Access (OBDA) have been extensively studied across different communities. The underlying problem is common: using a number of differently structured data-sources mapped to a mediating schema/ontology/knowledge-graph, answer a query posed on the latter. In DE, forward-chaining algorithms, collectively known as the chase, transform source data to a new materialised instance that satisfies the ontology and can be directly queried. In OBDA, backward-chaining algorithms rewrite the query over the source schema, taking the ontology into account, in order to execute the rewriting directly on the sources. These two reasoning approaches have seen an individual rise in algorithms, practical implementations, and benchmarks. However, there has not been a principled methodology to compare solutions across both areas. In this paper we provide an original methodology and a benchmark infrastructure - a set of test scenarios, generator and translator tools, and an experimental infrastructure - to allow the translation and execution of a DE/OBDA scenario across areas and among different chase and query-rewriting systems. In the process, we also present a syntactic restriction of linear Tuple Generating Dependencies that precisely captures DL-LiteR, a correspondence previously uninvestigated. We perform cross-approach experiments under a wide range of assumptions, such as the use of different source-to-target mapping languages, shedding light to the interplay between forward-and backward-chaining. Our preliminary results show that, indeed, chase can compete and might overcome query rewriting even in the face of large data especially for complex mapping languages.

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More information

Published date: 1 April 2022

Identifiers

Local EPrints ID: 504289
URI: http://eprints.soton.ac.uk/id/eprint/504289
ISSN: 2150-8097
PURE UUID: e0097cbb-f8b4-4e4f-ab49-4467d45de2fc
ORCID for Afnan Alhazmi: ORCID iD orcid.org/0009-0008-6398-4090

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Date deposited: 02 Sep 2025 17:10
Last modified: 02 Sep 2025 17:10

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Contributors

Author: Afnan Alhazmi ORCID iD
Author: Tom Blount
Author: George Konstantinidis

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