NDLI: DatalogRA: datalog with recursive aggregation in the spark RDD model

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DatalogRA: datalog with recursive aggregation in the spark RDD model

Content Provider	ACM Digital Library
Author	Rogala, Marek Sroka, Jacek Hidders, Jan
Abstract	Distributed computations on graphs are becoming increasingly important with the emergence of large graphs such as social networks and the Web that contain huge amounts of useful information. Computations can be easily distributed with the use of specialised frameworks like Hadoop with MapReduce, Giraph/Pregel or GraphLab. Yet, declarative, query-like, but at the same time efficient solutions are lacking. Programmers are needed to code all computations by hand and manually optimise each individual program. This paper presents an implementation of a tool which extends a distributed computations platform, Apache Spark, with the capability of executing queries written in a variant of a declarative query language, Datalog, especially extended to better support graph algorithms. This approach makes it possible to express graph algorithms in a declarative query language, accessible to a broader group of users than typical programming languages, and execute them on an existing infrastructure for distributed computations.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450347808
DOI	10.1145/2960414.2960417
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-06-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rdd Datalog Graph processing Declarative Spark Socialite Resilient distributed datasets
Content Type	Text
Resource Type	Article

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