Declarative and distributed graph analytics with GRADOOP
Summary: Gradoop combines graph DB capabilities with distributed graph processing to enable declarative, scalable graph analytics. It exposes graph pattern matching and graph grouping operators, supports up to 10B edges, and runs locally or on clusters as an open-source framework. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Martin Junghanns (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
- 2. Max Kießling (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
- 3. Niklas Teichmann (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
- 4. Kevin Gómez (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
- 5. André Petermann (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
- 6. Erhard Rahm (Scalable Data Services and Solutions Dresden/Leipzig; University of Leipzig)
BibTeX Citation
@article{junghanns_vldb18,
title = {{Declarative and distributed graph analytics with GRADOOP}},
author = {Junghanns, Martin and Kießling, Max and Teichmann, Niklas and Gómez, Kevin and Petermann, André and Rahm, Erhard},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2006--2009},
doi = {10.14778/3229863.3236246},
url = {https://doi.org/10.14778/3229863.3236246},
year = {2018}
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 426 | The LDBC Social Network Benchmark: Interactive Workload | 2015 | SIGMOD | 0.00018692185 |
| 5,833 | Managing and Mining Large Graphs: Systems and Implementations | 2012 | SIGMOD | 6.072775e-05 |
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