EvOLAP Graph – Evolution and OLAP-Aware Graph Data Model

  • Ewa GuminskaEmail author
  • Teresa Zawadzka
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)


The objective of this paper is to propose a graph model that would be suitable for providing OLAP features on graph databases. The included features allow for a multidimensional and multilevel view on data and support analytical queries on operational and historical graph data.

In contrast to many existing approaches tailored for static graphs, the paper addresses the issue for the changing graph schema.

The model, named Evolution and OLAP-aware Graph (EvOLAP Graph), has been implemented on a time-based, versioned property graph model implemented in Neo4j graph database.


Graph database Multidimensional data model OLAP Neo4j Property graph EvOLAP Graph 


  1. 1.
    Chavalier, M., Malki, M.E., Kopliku, A., Teste, O., Tournier, R.: Document-oriented data warehouses: models and extended cuboids, extended cuboids in oriented document. In: 2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS), pp. 1–11, June 2016Google Scholar
  2. 2.
    Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: towards online analytical processing on graphs. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 103–112 (2008)Google 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.
    Dehdouh, K., Bentayeb, F., Boussaid, O., Kabachi, N.: Using the column oriented NoSQL model for implementing big data warehouses. In: International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA 2015), pp. 469–475 (2015).
  5. 5.
    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). Scholar
  6. 6.
    Guminska, E.: Analytical dimensional model in graph databases. Master’s thesis, Gdansk University of Technology (2017)Google Scholar
  7. 7.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit. The Definitive Guide to Dimensional Modeling (2013).
  8. 8.
    Laborie, S., Ravat, F., Song, J., Teste, O.: Combining business intelligence with semantic web: overview and challenges. In: INFORSID (2015)Google Scholar
  9. 9.
    Liu, X., et al.: SocialCube: a text cube framework for analyzing social media data. In: 2012 International Conference on Social Informatics, pp. 252–259, December 2012Google Scholar
  10. 10.
    Liu, Y., Vitolo, T.M.: Graph data warehouse: steps to integrating graph databases into the traditional conceptual structure of a data warehouse. In: 2013 IEEE International Congress on Big Data, pp. 433–434, June 2013Google Scholar
  11. 11.
    Malinowski, E.: Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications. Springer, Heidelberg (2009). Scholar
  12. 12.
    Nebot, V., Berlanga, R.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)CrossRefGoogle Scholar
  13. 13.
    Park, B.K., Song, I.Y.: Toward total business intelligence incorporating structured and unstructured data. In: Proceedings of the 2nd International Workshop on Business Intelligence and the WEB, BEWEB 2011, pp. 12–19. ACM, New York (2011)Google Scholar
  14. 14.
    Robinson, I.: Time-Based Versioned Graphs. time-based-versioned-graphs/. Accessed 07 Sept 2017
  15. 15.
    Yin, M., Wu, B., Zeng, Z.: HMGraph OLAP: a novel framework for multi-dimensional heterogeneous network analysis. In: Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP, DOLAP 2012, pp. 137–144. ACM, New York (2012)Google Scholar
  16. 16.
    Zhao, P., Li, X., Xin, D., Han, J.: Graph cube: on warehousing and OLAP multidimensional networks. In: SIGMOD - Proceedings of the 2011 International Conference on Management of Data, New York, NY (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Electronics, Telecommunications and InformaticsGdansk University of TechnologyGdanskPoland

Personalised recommendations