Name
Abstract | ||
|---|---|---|
Graph pattern matching is an important and challenging operation on graph data. Typical use cases are related to graph analytics. Since analysts are often non-programmers, a graph system will only gain acceptance, if there is a comprehensible way to declare pattern matching queries. However, respective query languages are currently only supported by graph databases but not by distributed graph processing systems. To enable pattern matching on a large scale, we implemented the declarative graph query language Cypher within the distributed graph analysis platform Gradoop. Using LDBC graph data, we show that our query engine is scalable for operational as well as analytical workloads. The implementation is open-source and easy to extend for further research. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3078447.3078450 | GRADES@SIGMOD/PODS |
Field | DocType | Citations |
Data mining,Graph database,Query language,Use case,Computer science,Power graph analysis,Theoretical computer science,Clique-width,Pattern matching,Graph (abstract data type),Database,Scalability | Conference | 7 |
PageRank | References | Authors |
0.47 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Junghanns | 1 | 50 | 5.48 |
| Max Kießling | 2 | 12 | 1.26 |
| Alex Averbuch | 3 | 73 | 3.42 |
| André Petermann | 4 | 51 | 6.17 |
| Erhard Rahm | 5 | 7415 | 655.09 |