Graph BI & Analytics: Current State and Future Challenges

  • Amine GhrabEmail author
  • Oscar Romero
  • Salim Jouili
  • Sabri Skhiri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11031)


In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.


  1. 1.
    Bean, R.: Variety, not volume, is driving big data initiatives (2016). Accessed 25 Jan 2018
  2. 2.
    García-Solaco, M., Saltor, F., Castellanos, M.: In: Bukhres, O.A., Elmagarmid, A.K. (eds.) Object-Oriented Multidatabase Systems, pp. 129–202. Prentice Hall International (UK) Ltd, Hertfordshire, UK (1995)Google Scholar
  3. 3.
    Feinberg, D., Heudecker, N.: IT market clock for database management systems (2014). Accessed 02 Jan 2018
  4. 4.
    Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Mining Knowl. Discov. 29(3), 626–688 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Van Vlasselaer, V., Bravo, C., Caelen, O., Eliassi-Rad, T., Akoglu, L., Snoeck, M., Baesens, B.: Apate: a novel approach for automated credit card transaction fraud detection using network-based extensions. Decis. Support Syst. 75, 38–48 (2015)CrossRefGoogle Scholar
  6. 6.
    Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A.A., Joshi, A.: Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th International Conference on Extending Database Technology, EDBT 2008. Advances in database technology, New York, USA, pp. 668–677. ACM (2008)Google Scholar
  7. 7.
    Duan, L., Da Xu, L.: Business intelligence for enterprise systems: a survey. IEEE Trans. Industr. Inform. 8(3), 679–687 (2012)CrossRefGoogle Scholar
  8. 8.
    Lim, E.P., Chen, H., Chen, G.: Business intelligence and analytics: Research directions. ACM Trans. Manag. Inf. Syst. 3(4), 17 (2013)CrossRefGoogle Scholar
  9. 9.
    Cuzzocrea, A., Bellatreche, L., Song, I.Y.: 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 (2013)Google Scholar
  10. 10.
    Skhiri, S., Jouili, S.: Large graph mining: recent developments, challenges and potential solutions. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 103–124. Springer, Heidelberg (2013). Scholar
  11. 11.
    Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Hannachi, L., Benblidia, N., Boussaid, O., Bentayeb, F.: Community cube: a semantic framework for analysing social network data. Int. J. Metadata Semant. Ontol. 10(3), 155–169 (2015)CrossRefGoogle Scholar
  14. 14.
    Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J., Vrgoč, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68 (2017)CrossRefGoogle Scholar
  15. 15.
    Hölsch, J., Schmidt, T., Grossniklaus, M.: On the performance of analytical and pattern matching graph queries in neo4j and a relational database. In: Ioannidis, Y.E., Stoyanovich, J., Orsi, G. (eds.) Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, March 21–24, 2017. Volume 1810 of CEUR Workshop Proceedings, (2017)Google Scholar
  16. 16.
    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). Scholar
  17. 17.
    Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: Multidimensional networks: foundations of structural analysis. World Wide Web 16(5–6), 567–593 (2013)CrossRefGoogle Scholar
  18. 18.
    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
  19. 19.
    Wang, Z., Fan, Q., Wang, H., Tan, K.l., Agrawal, D., El Abbadi, A.: Pagrol: Prallel Graph OLAP over large-scale attributed graphs. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 496–507. IEEE (2014)Google Scholar
  20. 20.
    Ghrab, A., Romero, O., Skhiri, S., Vaisman, A., Zimányi, E.: A framework for building OLAP cubes on graphs. In: Morzy, T., Valduriez, P., Bellatreche, L. (eds.) ADBIS 2015. LNCS, vol. 9282, pp. 92–105. Springer, Cham (2015). Scholar
  21. 21.
    Nebot, V., Berlanga, R.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)CrossRefGoogle Scholar
  22. 22.
    Kämpgen, B., Harth, A.: Transforming statistical linked data for use in OLAP systems. In: Proceedings of the 7th International Conference on Semantic Systems, pp. 33–40. ACM (2011)Google Scholar
  23. 23.
    Beheshti, S.M.R., Benatallah, B., Motahari-Nezhad, H.R.: Scalable graph-based olap analytics over process execution data. Distrib. Parallel Databases 34(3), 379–423 (2016)CrossRefGoogle Scholar
  24. 24.
    Varga, J., Vaisman, A.A., Romero, O., Etcheverry, L., Pedersen, T.B., Thomsen, C.: Dimensional enrichment of statistical linked open data. Web Semant. Sci. Serv. Agents World Wide Web 40, 22–51 (2016)CrossRefGoogle Scholar
  25. 25.
    Nath, R.P.D., Hose, K., Pedersen, T.B., Romero, O.: SETL: a programmable semantic extract-transform-load framework for semantic data warehouses. Inf. Syst. 68, 17–43 (2017)CrossRefGoogle Scholar
  26. 26.
    Lee, K., Lee, K.: Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. with Appl. 42(10), 4851–4858 (2015)CrossRefGoogle Scholar
  27. 27.
    Demesmaeker, F., Ghrab, A., Nijssen, S., Skhiri, S.: Discovering interesting patterns in large graph cubes. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3322–3331 (2017)Google Scholar
  28. 28.
    Bleco, D., Kotidis, Y.: Entropy-based selection of graph cuboids. In: Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems, vol. 2. ACM (2017)Google Scholar
  29. 29.
    Lumsdaine, A., Gregor, D., Hendrickson, B., Berry, J.: Challenges in parallel graph processing. Parallel Process. Lett. 17(01), 5–20 (2007)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Batarfi, O., El Shawi, R., Fayoumi, A.G., Nouri, R., Barnawi, A., Sakr, S., et al.: Large scale graph processing systems: survey and an experimental evaluation. Cluster Comput. 18(3), 1189–1213 (2015)CrossRefGoogle Scholar
  31. 31.
    Denis, B., Ghrab, A., Skhiri, S.: A distributed approach for graph-oriented multidimensional analysis. In: 2013 IEEE International Conference on Big Data, pp. 9–16, October 2013Google Scholar
  32. 32.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: PREGEL: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, pp. 135–146. ACM (2010)Google Scholar
  33. 33.
    Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. VLDB Endow. 5(8), 716–727 (2012)CrossRefGoogle Scholar
  34. 34.
    Gonzalez, J.E., Low, Y., Gu, H., Bickson, D., Guestrin, C.: Powergraph: Distributed graph-parallel computation on natural graphs. In: OSDI, vol. 12, p. 2 (2012)Google Scholar
  35. 35.
    Gonzalez, J.E., Xin, R.S., Dave, A., Crankshaw, D., Franklin, M.J., Stoica, I.: Graphx: Graph processing in a distributed dataflow framework. OSDI. 14, 599–613 (2014)Google Scholar
  36. 36.
    Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud 2010, Berkeley, CA, USA, p. 10 (2010)Google Scholar
  37. 37.
    Junghanns, M., Petermann, A., Gómez, K., Rahm, E.: Gradoop: Scalable graph data management and analytics with hadoop. arXiv preprint arXiv:1506.00548 (2015)
  38. 38.
    Carbone, P., Katsifodimos, A., Ewen, S., Markl, V., Haridi, S., Tzoumas, K.: Apache FLINK: Stream and batch processing in a single engine. In: Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol. 36(4) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Amine Ghrab
    • 1
    • 2
    Email author
  • Oscar Romero
    • 2
  • Salim Jouili
    • 1
  • Sabri Skhiri
    • 1
  1. 1.EURA NOVA R&DMont-Saint-GuibertBelgium
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain

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