G-Diff: A Grouping Algorithm for RDF Change Detection on MapReduce

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8943)


Linked Data is a collection of RDF data that can grow exponentially and change over time. Detecting changes in RDF data is important to support Linked Data consuming applications with version management. Traditional approaches for change detection are not scalable. This has led researchers to devise algorithms on the MapReduce framework. Most works simply take a URI as a Map key. We observed that it is not efficient to handle RDF data with a large number of distinct URIs since many Reduce tasks have to be created. Even though the Reduce tasks are scheduled to run simultaneously, too many small Reduce tasks would increase the overall running time. In this paper, we propose G-Diff, an efficient MapReduce algorithm for RDF change detection. G-Diff groups triples by URIs during Map phase and sends the triples to a particular Reduce task rather than multiple Reduce tasks. Experiments on real datasets showed that the proposed approach takes less running time than previous works.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Okcan, A., Riedewald, M.: Processing theta-joins using MapReduce. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, New York, USA (2011)Google Scholar
  2. 2.
    Im, D.-H., Ahn, J., Zong, N., Jung, J., Kim, H.-G.: Link-Diff: change detection tool for linked data using MapReduce framework. In: The Workshop on Big data for Knowledge Engineering in JIST 2012, Nara, Japan, December 2–4, 2012Google Scholar
  3. 3.
    Wang, Y., DeWitt, D.J., Cai, J.-Y.: X-Diff: an effective change detection algorithm for XML documents. In: The Proceeding of Data Engineering (2003)Google Scholar
  4. 4.
    Bizer, C., Heath, T., Berners-Lee, T.: Linked Data The story so far. International Journal on Semantic Web and Information Systems 5(3) (2009)Google Scholar
  5. 5.
    Volkel, M., Groza, T.: SemVersion: an RDF-based ontology versioning system. In: Proceedings of the IADIS International Conference WWW/Internet (2006)Google Scholar
  6. 6.
    Zeginis, D., Tzitzikas, Y., Christophides, V.: On the foundations of computing deltas between RDF models. In: Aberer, K., et al. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 637–651. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  7. 7.
    Apache Hadoop. http://hadoop.apache.org
  8. 8.
    Husain, M., McGlothlin, J., Masud, M.M., Khan, L., Thuraisingham, B.M.: Heuristics-Based Query Processing for Large RDF Graphs Using Cloud Computing. TKDE 23(9), 1312–1327 (2011)Google Scholar
  9. 9.
    Cassidy, S., Ballantine, J.: Version control for RDF triple store. In: ICSOFT (ISDM/EHST/DC), vol. 512 (2007)Google Scholar
  10. 10.
    Vander Sande, M., Colpaert, P., Verborgh, R., Coppens, S., Mannens, E., Van de Walle, R.: R&Wbase: git for triples. In: Proceedings of the 6th Workshop on Linked Data on the Web (2013)Google Scholar
  11. 11.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Biomedical Knowledge Engineering LaboratorySeoul National UniversitySeoulRepublic of Korea
  2. 2.Dental Research InstituteSeoul National UniversitySeoulRepublic of Korea
  3. 3.Department of Computer and Information EngineeringHoseo UniversityCheonanRepublic of Korea

Personalised recommendations