Implementing iterative algorithms with SPARQL

Robert W. Techentin, Barry K. Gilbert, Adam Lugowski, Kevin Deweese, John Gilbert, Eric Dull, Mike Hinchey, Steven P. Reinhardt

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


The SPARQL declarative query language includes innovative capabilities to match subgraph patterns within a semantic graph database, providing a powerful base upon which to implement complex graph algorithms for very large data. Iterative algorithms are useful in a wide variety of domains, in particular in the data-mining and machine-learning domains relevant to graph analytics. In this paper we describe a general mechanism for implementing iterative algorithms via SPARQL queries, illustrate that mechanism with implementation of three algorithms (peer-pressure clustering, graph di.usion, and label propagation) that are valuable for graph analytics, and observe the strengths and weaknesses of this approach. We find that writing iterative algorithms in this style is straightforward to implement, with scalability to very large data and good performance.

Original languageEnglish (US)
Pages (from-to)216-223
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2014
Event2014 Joint Workshops on International Conference on Extending Database Technology, EDBT 2014 and International Conference on Database Theory, ICDT 2014 - Athens, Greece
Duration: Mar 28 2014 → …


  • Clustering
  • Data mining
  • Graph analysis
  • Iterative algorithms
  • Performance
  • Query languages

ASJC Scopus subject areas

  • Computer Science(all)


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