M. Arntzenius, and N. Krishnaswami. Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming - ICFP 2016, page 214--227. New York, NY, USA, ACM Press, (2016)
DOI: 10.1145/2951913.2951948
Abstract
Datalog may be considered either an unusually powerful query language or a carefully limited logic programming language. Datalog is declarative, expressive, and optimizable, and has been applied successfully in a wide variety of problem domains. However, most use-cases require extending Datalog in an application-specific manner. In this paper we define Datafun, an analogue of Datalog supporting higher-order functional programming. The key idea is to track monotonicity with types.
%0 Conference Paper
%1 Arntzenius2016Datafun
%A Arntzenius, Michael
%A Krishnaswami, Neelakantan R.
%B Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming - ICFP 2016
%C New York, NY, USA
%D 2016
%I ACM Press
%K 68n15-programming-languages 68n17-logic-programming 68n18-functional-programming-and-lambda-calculus 68q01-theory-of-computing 68q55-semantics
%P 214--227
%R 10.1145/2951913.2951948
%T Datafun: a Functional Datalog
%U http://www.rntz.net/files/datafun.pdf
%X Datalog may be considered either an unusually powerful query language or a carefully limited logic programming language. Datalog is declarative, expressive, and optimizable, and has been applied successfully in a wide variety of problem domains. However, most use-cases require extending Datalog in an application-specific manner. In this paper we define Datafun, an analogue of Datalog supporting higher-order functional programming. The key idea is to track monotonicity with types.
%@ 9781450342193
@inproceedings{Arntzenius2016Datafun,
abstract = {{Datalog may be considered either an unusually powerful query language or a carefully limited logic programming language. Datalog is declarative, expressive, and optimizable, and has been applied successfully in a wide variety of problem domains. However, most use-cases require extending Datalog in an application-specific manner. In this paper we define Datafun, an analogue of Datalog supporting higher-order functional programming. The key idea is to track monotonicity with types.}},
added-at = {2019-03-01T00:11:50.000+0100},
address = {New York, NY, USA},
author = {Arntzenius, Michael and Krishnaswami, Neelakantan R.},
biburl = {https://www.bibsonomy.org/bibtex/2dfd90d41217a6c04a3d78b696ab9d82e/gdmcbain},
booktitle = {Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming - ICFP 2016},
citeulike-article-id = {14171128},
citeulike-attachment-1 = {arntzenius_16_datafun.pdf; /pdf/user/gdmcbain/article/14171128/1121277/arntzenius_16_datafun.pdf; b74f75ae47d1e7ae20450bac9306c8359b9c4210},
citeulike-linkout-0 = {http://www.rntz.net/files/datafun.pdf},
citeulike-linkout-1 = {http://portal.acm.org/citation.cfm?id=2951948},
citeulike-linkout-2 = {http://dx.doi.org/10.1145/2951913.2951948},
comment = {Suggested for Sydney Paper Club 'Paper 36':https://www.meetup.com/Sydney-Paper-Club/events/244271708/},
doi = {10.1145/2951913.2951948},
file = {arntzenius_16_datafun.pdf},
interhash = {1b4111cf47bc78b591ac5097d9339114},
intrahash = {dfd90d41217a6c04a3d78b696ab9d82e},
isbn = {9781450342193},
keywords = {68n15-programming-languages 68n17-logic-programming 68n18-functional-programming-and-lambda-calculus 68q01-theory-of-computing 68q55-semantics},
location = {Nara, Japan},
pages = {214--227},
posted-at = {2017-10-24 22:55:47},
priority = {4},
publisher = {ACM Press},
series = {ICFP 2016},
timestamp = {2019-03-01T00:11:50.000+0100},
title = {Datafun: a Functional {D}atalog},
url = {http://www.rntz.net/files/datafun.pdf},
year = 2016
}