TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes


Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18


  1. Akoglu, L., Tong, H., Koutra, D. (2015). Graph-based anomaly detection and description: a survey. Data Mining and Knowledge Discovery, 29(3), 626–688.

    Article  Google Scholar 

  2. Benatallah, B., Motahari-Nezhad, H.R., et al. (2015). Scalable graph-based OLAP analytics over process execution data. Distributed and Parallel Databases, 1–45.

  3. Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D. (2013). Multidimensional networks: foundations of structural analysis. World Wide Web, 16(5–6), 567–593.

    Article  Google Scholar 

  4. Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S. (2009). Graph OLAP: a multi-dimensional framework for graph data analysis. Knowledge and Information Systems, 21(1), 41–63.

    Article  Google Scholar 

  5. Cuzzocrea, A., Bellatreche, L., Song, I.-Y. (2013a). Data warehousing and OLAP over big data: current challenges and future research directions. In Proceedings of the sixteenth international workshop on data warehousing and OLAP (pp. 67–70): ACM.

  6. Cuzzocrea, A., Saccà, D., Ullman, J.D. (2013b). Big data: a research agenda. In Proceedings of the 17th international database engineering & applications symposium (pp. 198–203): ACM.

  7. Denis, B., Ghrab, A., Skhiri, S. (2013). A distributed approach for graph-oriented multidimensional analysis. In 2013 IEEE international conference on big data workshops (pp. 9–16): IEEE.

  8. Ghrab, A., Romero, O., Skhiri, S., Vaisman, A., Zimányi, E. (2015). A framework for building OLAP cubes on graphs. In East European conference on advances in databases and information systems (pp. 92–105): Springer.

  9. Ghrab, A., Romero, O., Jouili, S., Skhiri, S. (2018). Graph BI & analytics: current state and future challenges. In International conference on big data analytics and knowledge discovery (pp. 3–18): Springer.

  10. Gómez, L., Kuijpers, B., Vaisman, A. (2017). Performing olap over graph data: query language, implementation, and a case study. In Proceedings of the international workshop on real-time business intelligence and analytics (pp. 1–8): ACM.

  11. He, H., & Singh, A.K. (2006). Closure-tree: an index structure for graph queries. In Proceedings of the 22nd international conference on data engineering (pp. 38–): IEEE.

  12. Jin, X., Han, J., Cao, L., Luo, J., Ding, B., Lin, C.X. (2010). Visual cube and on-line analytical processing of images. In Proceedings of the 19th ACM international conference on information and knowledge management (pp. 849–858): ACM.

  13. Kang, S., Lee, S., Kim, J. (2019). Distributed graph cube generation using spark framework. J Supercomput, 1–22.

  14. Lenz, H.-J., & Shoshani, A. (1997). Summarizability in OLAP and statistical data bases. In Proceedings of the ninth international conference on scientific and statistical database management (pp. 132–143): IEEE.

  15. Leskovec, J., & Krevl, A. (2014). SNAP datasets: Stanford large network dataset collection.

  16. Li, C., Yu, P.S., Zhao, L., Xie, Y., Lin, W. (2011). InfoNetOLAPer: Integrating InfoNetWarehouse and InfoNetCube with InfoNetOLAP. PVLDB, 4(12), 1422–1425.

    Google Scholar 

  17. Lin, C.X., Ding, B., Han, J., Zhu, F., Zhao, B. (2008). Text cube: computing IR measures for multidimensional text database analysis. In Eighth IEEE International conference on data mining, 2008. ICDM’08 (pp. 905–910): IEEE.

  18. Loudcher, S., Jakawat, W., Soriano-Morales, E.-P., Favre, C. (2015). Combining OLAP and information networks for bibliographic data analysis: a survey. Scientometrics, 103, 471–487.

    Article  Google Scholar 

  19. Petermann, A., Junghanns, M., Müller, R., Rahm, E. (2014). Graph-based data integration and business intelligence with BIIIG. Proc. VLDB Endow., 7(13), 1577–1580.

    Article  Google Scholar 

  20. Qu, Q., Zhu, F., Yan, X., Han, J., Philip, S.Y., Li, H. (2011). Efficient topological OLAP on information networks. In Database systems for advanced applications (pp. 389–403): Springer.

  21. Queiroz-Sousa, P.O., & Salgado, A.C. (2019). A review on olap technologies applied to information networks. ACM Transactions on Knowledge Discovery from Data, 14(1), 8,1–8,25.

    Google Scholar 

  22. Rodriguez, M., & Neubauer, P. (2010). Constructions from dots and lines. Bulletin of the American Society for Information Science and Technology, 36(6), 35–41.

    Article  Google Scholar 

  23. Russell, M.A. (2013). Mining the social web: data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O’Reilly Media, Inc.

  24. Skhiri, S., & Jouili, S. (2013). Large graph mining: recent developments, challenges and potential solutions. In Aufaure, M.-A., & Zimányi, E. (Eds.) Business intelligence, volume 138 of lecture notes in business information processing (pp. 103–124): Springer.

  25. Vaisman, A., & Zimányi, E. (2014). Data warehouse systems: design and implementation. Springer.

  26. van der Aalst, W.M. (2013). Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In Asia-Pacific conference on business process management (pp. 1–22): Springer.

  27. Wang, Z., Fan, Q., Wang, H., Tan, K.-l., Agrawal, D., El Abbadi, A. (2014). Pagrol: parallel graph OLAP over large-scale attributed graphs. In 2014 IEEE 30th international conference on data engineering (ICDE) (pp. 496–507): IEEE.

  28. Wang, P., Wu, B., Wang, B. (2015). TSMH graph cube: a novel framework for large scale multi-dimensional network analysis. In 2015 IEEE international conference on data science and advanced analytics (DSAA) (pp. 1–10): IEEE.

  29. Wu, X., Wu, B., Wang, B. (2017). P&D graph cube: model and parallel materialization for multidimensional heterogeneous network. In 2017 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC) (pp. 95–104): IEEE.

  30. Yin, M., Wu, B., Zeng, Z. (2012). 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.

  31. Zhao, P., Yu, J.X., Yu, P.S. (2007). Graph Indexing: Tree + Delta <= Graph. In Proceedings of the 33rd international conference on very large data bases (pp. 938–949): VLDB Endowment.

  32. Zhao, P., Li, X., Xin, D., Han, J. (2011). 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.

Download references

Author information



Corresponding author

Correspondence to Amine Ghrab.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ghrab, A., Romero, O., Skhiri, S. et al. TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes. Inf Syst Front 23, 203–226 (2021).

Download citation


  • Graph cube
  • OLAP cube
  • Graph processing
  • Graph mining
  • Multidimensional graph