2026-07-22T12:32:57Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:5017 2024-03-06T10:35:42Z ddc:004 open_access

Yedalog: Exploring Knowledge at Scale Chin, Brian von Dincklage, Daniel Ercegovac, Vuk Hawkins, Peter Miller, Mark S. Och, Franz Olston, Christopher Pereira, Fernando Datalog MapReduce With huge progress on data processing frameworks, human programmers are frequently the bottleneck when analyzing large repositories of data. We introduce Yedalog, a declarative programming language that allows programmers to mix data-parallel pipelines and computation seamlessly in a single language. By contrast, most existing tools for data-parallel computation embed a sublanguage of data-parallel pipelines in a general-purpose language, or vice versa. Yedalog extends Datalog, incorporating not only computational features from logic programming, but also features for working with data structured as nested records. Yedalog programs can run both on a single machine, and distributed across a cluster in batch and interactive modes, allowing programmers to mix different modes of execution easily. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Brian Chin and Daniel von Dincklage and Vuk Ercegovac and Peter Hawkins and Mark S. Miller and Franz Och and Christopher Olston and Fernando Pereira 2015 Is Part Of LIPIcs, Volume 32, 1st Summit on Advances in Programming Languages (SNAPL 2015) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SNAPL.2015.63 urn:nbn:de:0030-drops-50172 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SNAPL.2015.63 eng https://creativecommons.org/licenses/by/3.0/legalcode