Paper Info
Title
Declarative and distributed graph analytics with GRADOOP
Abstract
AbstractWe demonstrate Gradoop, an open source framework that combines and extends features of graph database systems with the benefits of distributed graph processing. Using a rich graph data model and powerful graph operators, users can declaratively express graph analytical programs for distributed execution without needing advanced programming experience or a deeper understanding of the underlying system. Visitors of the demo can declare graph analytical programs using the Gradoop operators and also visually experience two of our advanced operators: graph pattern matching and graph grouping. We provide real world and artificial social network data with up to 10 billion edges and allow running the programs either locally or on a remote research cluster to demonstrate scalability.
Year
DOI
Venue
2018
10.14778/3229863.3236246
Hosted Content
Field
DocType
Volume
Data mining,Graph,Graph pattern matching,Graph database,Social network,Graph data model,Computer science,Graph analytics,Theoretical computer science,Operator (computer programming),Scalability
Journal
11
Issue
ISSN
Citations 
12
2150-8097
5
PageRank 
References 
Authors
0.45
0
6
Name
Order
Citations
PageRank
Martin Junghanns1505.48
Max Kießling2121.26
Niklas Teichmann3131.61
kevin gomez4201.72
André Petermann5516.17
Erhard Rahm67415655.09