Fast datalog evaluation for batch and stream graph processing

Autor: Muhammad Imran, Gábor E. Gévay, Jorge-Arnulfo Quiané-Ruiz, Volker Markl
Rok vydání: 2022
Předmět:
Zdroj: World Wide Web. 25:971-1003
ISSN: 1573-1413
1386-145X
Popis: Implementing complex algorithms for big data, artificial intelligence, and graph processing requires enormous effort. Succinct, declarative programs to solve complex problems that can be efficiently executed for batching and streaming data are in demand. This paper presents Nexus, a distributed Datalog evaluation system. It evaluates Datalog programs using the semi-naive algorithm for batch and streaming data using incremental and asynchronous iteration. Furthermore, we evaluate Datalog programs with aggregates to determine the advantages of implementing the semi-naive algorithm using incremental iteration on its performance. Our experimental results show that Nexus significantly outperforms acyclic dataflow-based systems.
Databáze: OpenAIRE
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