Kojaph: Visual Definition and Exploration of Patterns in Graph Databases

  • Walter DidimoEmail author
  • Francesco Giacchè
  • Fabrizio Montecchiani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9411)


We present Kojaph, a new system for the visual definition and exploration of patterns in graph databases. It offers an expressive visual language integrated in a simple user interface, to define complex patterns as a combination of topological properties and node/edge attribute properties. Users can also interact with the query results and visually explore the graph incrementally, starting from such results. From the application perspective, Kojaph has been designed to run on top of every desired graph database management system (GDBMS). As a proof of concept, we integrated it with Neo4J, the most popular GDBMS.


Query Language Property Tree Graph Database Visual Language Attribute Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Angles, R., Gutiérrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1–39 (2008)CrossRefGoogle Scholar
  2. 2.
    Bhowmick, S.S., Choi, B., Zhou, S.: VOGUE: towards A visual interaction-aware graph query processing framework. In: CIDR 2013 (2013)Google Scholar
  3. 3.
    Blau, H., Immerman, N., Jensen, D.: A visual language for querying and updating graphs. Technical report UM-CS-2002-037, University of Massachusetts Amherst, Computer Science DepartmentGoogle Scholar
  4. 4.
    Chau, D.H., Faloutsos, C., Tong, H., Hong, J.I., Gallagher, B., Eliassi-Rad, T.: GRAPHITE: a visual query system for large graphs. In: ICDM 2008, pp. 963–966. IEEE (2008)Google Scholar
  5. 5.
    Dominguez-Sal, D., et al.: Survey of graph database performance on the HPC scalable graph analysis benchmark. In: Shen, H.T., et al. (eds.) WAIM 2010. LNCS, vol. 6185, pp. 37–48. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  6. 6.
    Gallagher, B.: Matching structure and semantics: a survey on graph-based pattern matching. Artif. Intell. 6, 45–53 (2006)Google Scholar
  7. 7.
    Hung, H.H., Bhowmick, S.S., Truong, B.Q., Choi, B., Zhou, S.: QUBLE: towards blending interactive visual subgraph search queries on large networks. VLDB J. 23(3), 401–426 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, Z., Jiang, B., Heer, J.: imMens: Real-time visual querying of big data. Comput. Graph. Forum 32(3), 421–430 (2013)CrossRefGoogle Scholar
  9. 9.
    Stolte, C., Tang, D., Hanrahan, P.: Polaris: a system for query, analysis, and visualization of multidimensional databases. Commun. ACM 51(11), 75–84 (2008)CrossRefGoogle Scholar
  10. 10.
    Wood, P.T.: Query languages for graph databases. SIGMOD Record 41(1), 50–60 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Walter Didimo
    • 1
    Email author
  • Francesco Giacchè
    • 1
  • Fabrizio Montecchiani
    • 1
  1. 1.Università Degli Studi di PerugiaPerugiaItaly

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