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Choosing between graph databases and RDF engines for consuming and mining linked data

Abstract:

Graphs naturally represent Linked Data and implementations of graph-based tasks are required not only for data consumption, but also for mining patterns among links. Despite efficient graph-based algorithms and engines have been implemented, there is no clear understanding of how these solutions may behave on Linked Data. We evaluate both general purpose graph database and state-of-the-art RDF engines, and our experimental results reveal characteristics of linked datasets and graph-based tasks that may affect their performance. These results can be considered as a further step for solving the problem of choosing between graph databases to consume and mine Linked Data.