Datalog rewriting for Guarded TGDs

We deal with the problem of fact entailment with respect to a database and a set of integrity constraints, focusing on the case of Guarded tuple-generating dependencies (GTGDs). The original approach to the problem in the literature is via forward reasoning or "chasing", where one complete...

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Main Authors: Benedikt, M, Buron, M, Germano, S, Kappelmann, K, Motik, B
Format: Conference item
Language:English
Published: CEUR Workshop Proceedings 2022
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author Benedikt, M
Buron, M
Germano, S
Kappelmann, K
Motik, B
author_facet Benedikt, M
Buron, M
Germano, S
Kappelmann, K
Motik, B
author_sort Benedikt, M
collection OXFORD
description We deal with the problem of fact entailment with respect to a database and a set of integrity constraints, focusing on the case of Guarded tuple-generating dependencies (GTGDs). The original approach to the problem in the literature is via forward reasoning or "chasing", where one completes the input database by adding fresh elements and facts. This completion process may be infinite, but in the case of GTGDs it is known that one can compute a point where the chase can be cut off without missing any base facts. Another approach is by forming an automaton and checking it for emptiness. Neither of these approaches scales to large input datasets. An alternative approach is to <i>rewrite the constraints into Datalog</i>: the Datalog rewriting can be generated in advance of any dataset and will produce the same base facts as the original constraints. It is known that Datalog rewritings always exist. But to our knowledge the approach has never been implemented. In this work we overview effective algorithms to Datalog rewriting of GTGDs. This presents work that will appear in VLDB 2022.
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spelling oxford-uuid:f6b724d6-bb1c-4c35-a9ea-b71e541c06412023-06-09T11:03:34ZDatalog rewriting for Guarded TGDsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f6b724d6-bb1c-4c35-a9ea-b71e541c0641EnglishSymplectic ElementsCEUR Workshop Proceedings2022Benedikt, MBuron, MGermano, SKappelmann, KMotik, BWe deal with the problem of fact entailment with respect to a database and a set of integrity constraints, focusing on the case of Guarded tuple-generating dependencies (GTGDs). The original approach to the problem in the literature is via forward reasoning or "chasing", where one completes the input database by adding fresh elements and facts. This completion process may be infinite, but in the case of GTGDs it is known that one can compute a point where the chase can be cut off without missing any base facts. Another approach is by forming an automaton and checking it for emptiness. Neither of these approaches scales to large input datasets. An alternative approach is to <i>rewrite the constraints into Datalog</i>: the Datalog rewriting can be generated in advance of any dataset and will produce the same base facts as the original constraints. It is known that Datalog rewritings always exist. But to our knowledge the approach has never been implemented. In this work we overview effective algorithms to Datalog rewriting of GTGDs. This presents work that will appear in VLDB 2022.
spellingShingle Benedikt, M
Buron, M
Germano, S
Kappelmann, K
Motik, B
Datalog rewriting for Guarded TGDs
title Datalog rewriting for Guarded TGDs
title_full Datalog rewriting for Guarded TGDs
title_fullStr Datalog rewriting for Guarded TGDs
title_full_unstemmed Datalog rewriting for Guarded TGDs
title_short Datalog rewriting for Guarded TGDs
title_sort datalog rewriting for guarded tgds
work_keys_str_mv AT benediktm datalogrewritingforguardedtgds
AT buronm datalogrewritingforguardedtgds
AT germanos datalogrewritingforguardedtgds
AT kappelmannk datalogrewritingforguardedtgds
AT motikb datalogrewritingforguardedtgds