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Generative Datalog with Continuous Distributions

Grohe, M; Kaminski, BL; Katoen, J-P; Lindner, P; (2020) Generative Datalog with Continuous Distributions. In: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS’20). (pp. pp. 347-360). ACM Green open access

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Abstract

Arguing for the need to combine declarative and probabilistic programming, Bárány et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a "purely declarative probabilistic programming language." We revisit this language and propose a more foundational approach towards defining its semantics. It is based on standard notions from probability theory known as stochastic kernels and Markov processes. This allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Bárány et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, ICDT 2020), and we show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.

Type: Proceedings paper
Title: Generative Datalog with Continuous Distributions
Event: ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS’20)
Location: Portland, Oregon, USA
Dates: 14 June 2020 - 19 June 2020
ISBN-13: 978-1-4503-7108-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3375395.3387659
Publisher version: http://doi.org/10.1145/3375395.3387659
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10094730
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