3 ms·

A Differential Datalog Interpreter

3 points by MLIR 3y ago

MLIR 3y ago
The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization; that is, to adjust the computation to new data instead of restarting from scratch. One of the major caveats is that deleting data is notoriously more involved than adding since one has to take into account all possible data that has been entailed from what is being deleted. Differential dataflow is a computational model that provides efficient incremental maintenance, notoriously with equal performance between additions and deletions, and work distribution of iterative dataflows. In this paper, we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm with the same optimizations. Experimental results suggest that monotonic aggregation is more powerful than ascenting merely the powerset lattice.
rebanevapustus 3y ago
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 policy of arXiv): https://arxiv.org/abs/2307.14810 https://arxiv.org/abs/2307.14810 2. The actual article: https://arxiv.org/abs/2308.04214 https://arxiv.org/abs/2308.04214 3. Citation 22 refers to the repository of somebody else: https://web.archive.org/web/20221207090646/https://github.com/brurucy/shapiro https://web.archive.org/web/20221207090646/https://github.co... which so happens to be the first author of the actual article 4. Twitter thread: https://twitter.com/JulesJacobs5/status/1689199796085510144 https://twitter.com/JulesJacobs5/status/1689199796085510144 Furthermore, he has history, having had already plagiarised an article in the medical field: 1. Original: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993799/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993799/ 2. Stolen: https://www.researchgate.net/publication/368731786_Adenoma_Replacement_due_to_Invasion_of_a_Metastatic_Mass_A_Case_Report_and_Literature_Review https://www.researchgate.net/publication/368731786_Adenoma_R... And in fact, "Shapiro"(the repository linked in the article) has already been posted here in hackernews: https://news.ycombinator.com/item?id=33892235 https://news.ycombinator.com/item?id=33892235
- MLIR 3y ago
  Author was at Stanford's department of computer science at 2020. Your department 'did' plagiarize at the following article https://dl.acm.org/doi/10.1145/3453483.3454026 https://dl.acm.org/doi/10.1145/3453483.3454026 A twitter thread from a random graduate student shows nothing. And the medical articles you're linking are related to a patient that was misdiagnosed in Canada; a proper diagnosis was made and a subsequent publication was made. The "actual article" has been withdrawn because it violated copyright and was already accepted on the 28th.

3 points by MLIR 3y ago

MLIR 3y ago

The core reasoning task for datalog engines is materialization, the evaluation of a datalog program over a database alongside its physical incorporation into the database itself. The de-facto method of computing is through the recursive application of inference rules. Due to it being a costly operation, it is a must for datalog engines to provide incremental materialization; that is, to adjust the computation to new data instead of restarting from scratch. One of the major caveats is that deleting data is notoriously more involved than adding since one has to take into account all possible data that has been entailed from what is being deleted. Differential dataflow is a computational model that provides efficient incremental maintenance, notoriously with equal performance between additions and deletions, and work distribution of iterative dataflows. In this paper, we investigate the performance of materialization with three reference datalog implementations, out of which one is built on top of a lightweight relational engine, and the two others are differential-dataflow and non-differential versions of the same rewrite algorithm with the same optimizations. Experimental results suggest that monotonic aggregation is more powerful than ascenting merely the powerset lattice.

rebanevapustus 3y ago

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 policy of arXiv): https://arxiv.org/abs/2307.14810 https://arxiv.org/abs/2307.14810 2. The actual article: https://arxiv.org/abs/2308.04214 https://arxiv.org/abs/2308.04214 3. Citation 22 refers to the repository of somebody else: https://web.archive.org/web/20221207090646/https://github.com/brurucy/shapiro https://web.archive.org/web/20221207090646/https://github.co... which so happens to be the first author of the actual article 4. Twitter thread: https://twitter.com/JulesJacobs5/status/1689199796085510144 https://twitter.com/JulesJacobs5/status/1689199796085510144 Furthermore, he has history, having had already plagiarised an article in the medical field: 1. Original: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993799/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9993799/ 2. Stolen: https://www.researchgate.net/publication/368731786_Adenoma_Replacement_due_to_Invasion_of_a_Metastatic_Mass_A_Case_Report_and_Literature_Review https://www.researchgate.net/publication/368731786_Adenoma_R... And in fact, "Shapiro"(the repository linked in the article) has already been posted here in hackernews: https://news.ycombinator.com/item?id=33892235 https://news.ycombinator.com/item?id=33892235

MLIR 3y ago
Author was at Stanford's department of computer science at 2020. Your department 'did' plagiarize at the following article https://dl.acm.org/doi/10.1145/3453483.3454026 https://dl.acm.org/doi/10.1145/3453483.3454026 A twitter thread from a random graduate student shows nothing. And the medical articles you're linking are related to a patient that was misdiagnosed in Canada; a proper diagnosis was made and a subsequent publication was made. The "actual article" has been withdrawn because it violated copyright and was already accepted on the 28th.