Name
Abstract | ||
|---|---|---|
Graphs are an intuitive way to model complex relationships between real-world data objects. Thus, graph analytics plays an important role in research and industry. As graphs often reflect heterogeneous domain data, their representation requires an expressive data model including the abstraction of graph collections, for example, to analyze communities inside a social network. Further on, answering complex analytical questions about such graphs entails combining multiple analytical operations. To satisfy these requirements, we propose the Extended Property Graph Model, which is semantically rich, schema-free and supports multiple distinct graphs. Based on this representation, it provides declarative and combinable operators to analyze both single graphs and graph collections. Our current implementation is based on the distributed dataflow framework Apache Flink. We present the results of a first experimental study showing the scalability of our implementation on social network data with up to 11 billion edges. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2980523.2980527 | NDA@SIGMOD |
Field | DocType | Citations |
Data mining,Programming language,Social network,Abstraction,Computer science,Theoretical computer science,Dataflow,Operator (computer programming),Graph theory,Data model,Database,Graph (abstract data type),Scalability | Conference | 8 |
PageRank | References | Authors |
0.48 | 15 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Junghanns | 1 | 50 | 5.48 |
| André Petermann | 2 | 51 | 6.17 |
| Niklas Teichmann | 3 | 13 | 1.61 |
| kevin gomez | 4 | 20 | 1.72 |
| Erhard Rahm | 5 | 7415 | 655.09 |