Empirical study on the usage of graph query languages in open source Java projects

Whoever is interested in studies of empirical usage of SPARQL, Cypher, Gremlin, GraphQL please read on, otherwise please ignore from hereon.

Please find the following investigation
*Empirical study on the usage of graph query languages in open source Java projects*
here: https://eprints.soton.ac.uk/434439/ <https://eprints.soton.ac.uk/434439/>

I usually don’t advertise papers on this mailing list, because people here watch the space of usual conferences like ISWC, ESWC, IJCAI, etc.
This, however, is an investigation that will appear in a programming language venue, hence please allow me to post this note.

Here is its abstract:
Graph data models are interesting in various domains, in part because of the intuitiveness and flexibility they offer compared to relational models. Specialized query languages, such as Cypher for property graphs or SPARQL for RDF, facilitate their use. In this paper, we present an empirical study on the usage of graph-based query languages in open-source Java projects on GitHub. We investigate the usage of SPARQL, Cypher, Gremlin and GraphQL in terms of popularity and their development over time. We select repositories based on dependencies related to these technologies and employ various popularity and source-code based filters and ranking features for a targeted selection of projects. For the concrete languages SPARQL and Cypher, we analyze the activity of repositories over time. For SPARQL, we investigate common application domains, query use and existence of ontological data modeling in applications that query for concrete instance data. Our results show, that the usage of graph query languages in open-source projects increased over the last years, with SPARQL and Cypher being by far the most popular. SPARQL projects are more active in terms of query related artifact changes and unique developers involved, but Cypher is catching up. Relatively few applications use SPARQL to query for concrete instance data: A majority of those applications employ multiple different ontologies, including project and domain specific ones. Common application domains are management systems and data visualization tools.


Kind regards,
Steffen Staab