Parallel relational databases for diameter calculation of large graphs
I tiakina i:
| I whakaputaina i: | Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA) (2016), p. 213-220 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | |
| I whakaputaina: |
The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp)
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| Urunga tuihono: | Citation/Abstract Full Text Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| Whakarāpopotonga: | Parallel relational databases are seldom considered as a solution for representing and processing large graphs. Current literature shows a strong body of work on graph processing using either the MapReduce model or NoSQL databases specifically designed for graphs. However, parallel relational databases have been shown to outperform MapReduce implementations in a number of cases, and there are no clear reasons to assume that graph processing should be any different. Graph databases, on the other hand, do not commonly support the parallel execution of single queries and are therefore limited to the processing power of single nodes. In this paper, we compare a parallel relational database (Greenplum), a graph database (Neo4J) and a MapReduce implementation (Hadoop) for the problem of calculating the diameter of a graph. Results show that Greenplum produces the best execution times, and that Hadoop barely outperforms Neo4J even when using a much larger set of computers. |
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| Puna: | Advanced Technologies & Aerospace Database |