A Framework for Building OLAP Cubes on Graphs

  • Amine GhrabEmail author
  • Oscar Romero
  • Sabri Skhiri
  • Alejandro Vaisman
  • Esteban Zimányi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9282)


Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs. Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach.


Graph Topology Closeness Centrality Property Graph Graph Database Graph Lattice 
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.
    Robinson, I., Webber, J., Eifrem, E.: Graph Databases. O’Reilly Media Inc, Sebastopol (2013)Google Scholar
  2. 2.
    Petermann, A., Junghanns, M., Müller, R., Rahm, E.: Graph-based data integration and business intelligence with biiig. Proc. VLDB Endow. 7(13), 1577–1580 (2014)CrossRefGoogle Scholar
  3. 3.
    Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: a multi-dimensional framework for graph data analysis. Knowl. Inf. Syst. 21(1), 41–63 (2009)CrossRefGoogle Scholar
  4. 4.
    Zhao, P., Li, X., Xin, D., Han, J.: Graph cube: on warehousing and OLAP multidimensional networks. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, pp. 853–864. ACM (2011)Google Scholar
  5. 5.
    Wang, Z., Fan, Q., Wang, H., Tan, K.L., Agrawal, D., El Abbadi, A.: Pagrol: parallel graph olap over large-scale attributed graphs. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 496–507, March 2014Google Scholar
  6. 6.
    Ghrab, A., Skhiri, S., Jouili, S., Zimányi, E.: An analytics-aware conceptual model for evolving graphs. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 1–12. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  7. 7.
    Ghrab, A., Romero, O., Skhiri, S., Zimányi, E.: Analytics-Aware Graph Database Modeling, Technical report (2014) .
  8. 8.
    Rodriguez, M.A., Neubauer, P.: Constructions from dots and lines. Bull. Am. Soc. Inf. Sci. Technol. 36(6), 35–41 (2010)CrossRefGoogle Scholar
  9. 9.
    Angles, R., Gutierrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 1:1–1:39 (2008)CrossRefGoogle Scholar
  10. 10.
    Cuzzocrea, A., Bellatreche, L., Song, I.Y.: Data warehousing and OLAP over big data: current challenges and future research directions. In: DOLAP 2013 Proceedings of the Sixteenth International Workshop on Data Warehousing and OLAP, pp. 67–70. ACM, New York (2013)Google Scholar
  11. 11.
    Abelló, A., Darmont, J., Etcheverry, L., Golfarelli, M., Mazón, J.N., Naumann, F., Pedersen, T.B., Rizzi, S., Trujillo, J., Vassiliadis, P., Vossen, G.: Fusion cubes: towards self-service business intelligence. IJDWM 9(2), 66–88 (2013)Google Scholar
  12. 12.
    He, H., Singh, A.: Query language and access methods for graph databases. In: Aggarwal, C.C., Wang, H. (eds.) Managing and Mining Graph Data. ADS, vol. 40, pp. 125–160. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Qu, Q., Zhu, F., Yan, X., Han, J., Yu, P.S., Li, H.: Efficient topological OLAP on information networks. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part I. LNCS, vol. 6587, pp. 389–403. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  14. 14.
    Denis, B., Ghrab, A., Skhiri, S.: A distributed approach for graph-oriented multidimensional analysis. In: IEEE International Conference on Big Data, pp. 9–16 (2013)Google Scholar
  15. 15.
    Yin, M., Wu, B., Zeng, Z.: HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis. In: Proceedings of the 15th International Workshop on Data Warehousing and OLAP, pp. 137–144. ACM (2012)Google Scholar
  16. 16.
    Beheshti, S.-M.-R., Benatallah, B., Motahari-Nezhad, H.R., Allahbakhsh, M.: A framework and a language for on-line analytical processing on graphs. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 213–227. Springer, Heidelberg (2012) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Amine Ghrab
    • 1
    • 2
    • 3
    Email author
  • Oscar Romero
    • 3
  • Sabri Skhiri
    • 1
  • Alejandro Vaisman
    • 4
  • Esteban Zimányi
    • 2
  1. 1.EURA NOVA R&DMont-Saint-GuibertBelgium
  2. 2.Université Libre de BruxellesBrusselsBelgium
  3. 3.Universitat Politècnica de CatalunyaBarcelonaSpain
  4. 4.Instituto Tecnológico de Buenos AiresBuenos AiresArgentina

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