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arXiv:2308.04214cs.DB

On The Suitability of Differential Dataflow For Datalog Interpretation In Highly Dynamic Settings

Bruno Rucy Carneiro Alves de Lima, Merlin Kramer, Kalmer Apinis

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Abstract

In the domain of knowledge representation and reasoning within AI, datalog engines play an ever-increasingly crucial role. The crux of their operation lies in materialization: the evaluation of a data- log program and its incorporation into a database. This operation becomes complex and resource-intensive, especially when the data is highly dynamic, as it is common in distributed environments. Thus, incremental materialization, adjusting the computation to new data instead of restarting it, is the norm. However, handling the deletion of data is significantly more complicated than addition due to the cascading effects of what is being removed. Differential Dataflow offers a computational model that effectively addresses this, ensuring consistent performance for both data additions and deletions. In this paper, we delve into the efficiency of materializa- tion using three distinct datalog implementations: one based on a streamlined relational engine and two others that implement the same algorithm, but with one utilizing differential-dataflow, and another not. Our insights provide a roadmap for enhancing datalog- driven computations, particularly in dynamic data environments like the cloud.

arXiv comments
8 pages, AICCC 2023
Journal reference
Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference (AICCC 2023), Kyoto, Japan, December 2023, pp. 218-225. ACM, New York, NY, USA
DOI
doi:10.1145/3639592.3639622
Subjects
cs.DBDatabases cs.AIArtificial Intelligence cs.PLProgramming Languages

On Hacker News

Linked by 1 identifiable commenter in 4 comments across 3 threads, from Sep 2023 to Feb 2025. Found in 1 HN submission from Aug 2023 to Aug 2023. Open submission discussions.

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Discussed in comments3 threads

  1. Is the ArXiv safe from the current US Government attacks? 1 comment

    • I have zero compassion for ArXiv. Allow me to share a horror story: I was the victim of a pretty bizarre super in-your-face academic theft. Someone snooped a half-finished paper draft of mine off GitHub and...actually got it published in ArXiv and a "real" journal…

      rebanevapustusView on HNParent context

  2. Fraud, so much fraud 2 comments

    • After almost a year, with an unsurmountable amount of open-source evidence and the thief having had every single paper he has ever written retracted for fraud!! the best the journal did is to add a notice: https://www.mdpi.com/2674-113X/2/3/20 Arxiv cared even less. They allowed…

      rebanevapustusView on HNParent context

    • He managed to DMCA strike the corrected version of the paper: https://arxiv.org/abs/2308.04214 We then contacted Stanford, and somehow got their legal team to message Arxiv telling them that Matthew is a fraudster. In spite (!!) of Arxiv removing his stolen version thanks to…

      rebanevapustusView on HNParent context

  3. A Differential Datalog Interpreter 1 comment

    • This article is making the rounds. There's a long thread on twitter about how it has been stolen from somebody else from my faculty, alongside with claims that the author has false affiliation with Stanford (confirmed by arXiv) 1. False affiliation proof(violated the author…

      rebanevapustusView on HNParent context

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