Declarative probabilistic programming with Datalog
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2030
- Type
- D - Journal article
- DOI
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10.1145/3132700
- Title of journal
- ACM Transactions on Database Systems
- Article number
- ARTN 22
- First page
- 1
- Volume
- 42
- Issue
- 4
- ISSN
- 0362-5915
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
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-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work appeared in a special best-of-ICDT 2016 issue of ACM TODS. The declarative nature of the proposed probabilistic programming extension of Datalog has been vital for its adoption in practice as the probabilistic extension of LogicBlox, a commercial Datalog system with scores of clients, which is now a division of Infor (https://developer.logicblox.com/). LogicBlox software engineers have implemented this probabilistic Datalog and used it in practice (Molham Aref, former CEO of LogicBlox, can corroborate � molham.aref@relational.ai).
- Author contribution statement
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- Non-English
- No
- English abstract
- -