OLAP on Information Networks: A New Framework for Dealing with Bibliographic Data

  • Wararat JakawatEmail author
  • Cécile Favre
  • Sabine Loudcher
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 241)


In the context of decision making, data warehouses support OLAP technology and they have been very useful for efficient analysis onto structured data. For several years, OLAP is also used to analyze and visualize more complex data. Now, many data sets of interest can be described as a linked collection of interrelated objects. They could be represented as heterogeneous information networks, in which there are multiple object and link types. In this paper, we are focusing on bibliographic data. This type of data constitutes a rich source that is the starting point of research on bibliometrics, scientometrics domains. In this context, we discuss the interest of combining information networks, OLAP and data mining technologies. We propose a framework to materialize this combination and discuss the main challenges to build this framework. The basic idea is to be able to analyze various networks built from the bibliographic data representing different points of view (authors networks, citations networks...) and their dynamic.


OLAP Data Warehouse Information Networks Bibliographic Data Data Mining 


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  1. 1.
    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
  2. 2.
    Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. ACM SIGMOD 26(1), 65–74 (1997)CrossRefGoogle Scholar
  3. 3.
    Chen, C., Yan, X., Zhu, F., Han, J., Yu, P.S.: Graph OLAP: Towards online analytical processing on graphs. In: ICDM 2008, pp. 103–112 (2008)Google Scholar
  4. 4.
    Deng, H., King, I., Lyu, M.R.: Formal Models for Expert Finding on DBLP Bibliography Data. In: ICDM 2008, pp. 163–172 (2008)Google Scholar
  5. 5.
    Gupta, M., Aggarwal, C.C., Han, J., Sun, Y.: Evolutionary Clustering and Analysis of Bibliographic Networks. In: ASONAM 2011, pp. 63–70 (2011)Google Scholar
  6. 6.
    Han, J.: Mining Heterogeneous Information Networks by Exploring the Power of Links. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS, vol. 5808, pp. 13–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Huang, Z., Yan, Y., Qiu, Y., Qiao, S.: Exploring Emergent Semantic Communities from DBLP Bibliography Database. In: ASONAM 2009, pp. 219–214 (2009)Google Scholar
  8. 8.
    Kampgen, B., Harth, A.: Transforming statistical linked data for use in OLAP systems. In: I-SEMANTICS, pp. 33–40 (2011)Google Scholar
  9. 9.
    Klink, S., Reuther, P., Weber, A., Walter, B., Ley, M.: Analysing Social Networks Within Bibliographical Data. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 234–243. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Muhlenbach, F., Lallich, S.: Discovering Research Communities by Clustering Bibliographical Data. In: WI-IAT 2010, vol. 1, pp. 500–507 (2009)Google Scholar
  11. 11.
    Pham, M.C., Klamma, R.: The Structure of the Computer Science Knowledge Network. In: ASONAM 2010, pp. 17–24 (2010)Google Scholar
  12. 12.
    Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: RankClus: integrating clustering with ranking for heterogeneous information network analysis. In: EDBT 2009, pp. 565–576 (2009)Google Scholar
  13. 13.
    Trifonova, T.G.: Warehousing and OLAP Analysis of Bibliographic Data. Intelligent Information Management 3, 109–197 (2011)Google Scholar
  14. 14.
    Yu, P.S.: Information networks mining and analysis. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds.) APWeb 2011. LNCS, vol. 6612, pp. 1–2. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    Tian, Y., Hankins, R.A., Patel, L.M.: Efficient Aggregation for Graph Summarization. In: SIGMOD Conference, pp. 567–580 (2008)Google Scholar
  17. 17.
    Wei, W.: Complex network virtualization and link OLAP (2007)Google Scholar
  18. 18.
    Yin, M., Wu, B., Aeng, Z.: HMGraph OLAP: a Novel Framework for Multi-dimensional Heterogeneous Network Analysis. In: DOLAP 2012, pp. 137–144 (2012)Google Scholar
  19. 19.
    Zhao, P., Li, X., Xin, D., Han, J.: Graph cube: on warehousing and OLAP multidimensional networks. In: SIGMOD 2011, pp. 853–864 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wararat Jakawat
    • 1
    Email author
  • Cécile Favre
    • 1
  • Sabine Loudcher
    • 1
  1. 1.Université de Lyon (ERIC LYON 2)LyonFrance

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