Convergence of datalog over (Pre-) Semirings

Author:

Abo Khamis Mahmoud1ORCID,Ngo Hung Q.1ORCID,Pichler Reinhard2ORCID,Suciu Dan3ORCID,Wang Yisu Remy3ORCID

Affiliation:

1. Relational AI, Berkeley, USA

2. TU Wien, Vienna, Austria

3. University of Washington, Seattle, USA

Abstract

Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this article, we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naïve evaluation algorithm on any datalog program.

Funder

NSF IIS

Austrian Science Fund

Vienna Science and Technology Fund

Publisher

Association for Computing Machinery (ACM)

Reference88 articles.

1. Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI’16), Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 265–283. Retrieved from: https://www.usenix.org/conference/osdi16/technical-sessions/presentation/abadi

2. Convergence of Datalog over (Pre-) Semirings

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4. The generalized distributive law

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