Junghanns, M. ; Kießling, M. ; Teichmann, N. ; Gomez, K. ; Petermann, A. ; Rahm, E.

Declarative and distributed graph analytics with GRADOOP


2018 / 08


Futher information: http://www.vldb.org/pvldb/vol11/p2006-junghanns.pdf


We 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 sys-tem. Visitors of the demo can declare graph analytical pro-grams using the Gradoop operators and also visually expe-rience 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.