Exploring Message Correlation in Crowd-Based Data Using Hyper Coordinates Visualization Technique

  • Tien-Dung CaoEmail author
  • Dinh-Quyen Nguyen
  • Hien Duy Tran
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 753)


Analytical exploration for necessary information and insights from heterogeneous and multivariate dataset is challenging in visual analytics research due to the complexity of data and tasks. One of the data analytics target is to examine the relationship in the dataset, such as considering how the data elements and subsets are connected together. This work takes into account the direct and indirect connection relations: elements and subsets of elements might not only be directly linked together, but also possibly be indirectly associated via the relationships from other elements/subsets as well. Stream of messages instantly put on the cyberspace from the crowd is an example for such kind of dataset. In this paper, we present an approach to estimate the correlation between streaming messages collection in terms of large scale data processing, whilst the Hyper Coordinates visualization technique is designed to support those correlations exploration. The prototype tool is built to demonstrate the concepts for crowd-based data in the financial market domain.


Hyper Coordinates Multivariate data visualization Message correlation Direct/indirect relationship 



The authors would like to thank Quang M. Le and Tuan A. Ta at Sentifi AG (Ho Chi Minh City office, Vietnam) for their supports and discussions on working scenario.


  1. 1.
    Spark: Lightning-fast cluster computing.
  2. 2.
    Andrews, D.F.: Plots of high-dimensional data. Biometrics 28(1), 125–136 (1972)CrossRefGoogle Scholar
  3. 3.
    Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press, Madison (1983)Google Scholar
  4. 4.
    Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE Trans. Vis. Comput. Graph. 19(12), 2376–2385 (2013)CrossRefGoogle Scholar
  5. 5.
    Collin, C., Carpendale, S.: VisLink: revealing relationships amongst visualizations. IEEE Trans. Vis. Comput. Graph. 13(6), 1192–1199 (2007)CrossRefGoogle Scholar
  6. 6.
    Collins, C., Viegas, F.B., Wattenberg, M.: Parallel tag clouds to explore and analyze faceted text corpora. In: IEEE Symposium on VAST 2009, pp. 91–98, October 2009Google Scholar
  7. 7.
    Collins, C., Penn, G., Carpendale, S.: Bubble sets: revealing set relations with isocontours over existing visualizations. IEEE Trans. Vis. Comput. Graph. 15(6), 1009–1016 (2009)CrossRefGoogle Scholar
  8. 8.
    Cui, W., Qu, H., Zhou, H., Zhang, W., Skiena, S.: Watch the story unfold with textWheel: visualization of large-scale news streams. ACM Trans. Intell. Syst. Technol. 3(2), 20:1–20:17 (2012)CrossRefGoogle Scholar
  9. 9.
    Elmqvist, N., Dragicevic, P., Fekete, J.D.: Rolling the dice: multidimensional visual exploration using scatterplot matrix navigation. IEEE Trans. Vis. Comput. Graph. 14(6), 1148–1539 (2008)CrossRefGoogle Scholar
  10. 10.
    Gansner, E.R., Yifan, H., North, S.C.: Interactive visualization of streaming text data with dynamic maps. J. Graph Algorithms Appl. 17(4), 515–540 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Ghoniem, M., Fekete, J.-D., Castagliola, P.: On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Inf. Vis. 4(2), 114–135 (2005)CrossRefGoogle Scholar
  12. 12.
    Grinstein, G., Trutschl, M., Cvek, U.: High-dimensional visualizations. In: Proceedings of the Visual Data Mining Workshop, KDD (2001)Google Scholar
  13. 13.
    Guo, P., Xiao, H., Wang, Z., Yuan, X.: Interactive local clustering operations for high dimensional data in parallel coordinates. In: 2010 IEEE Pacific Visualization Symposium (PacificVis), pp. 97–104, March 2010Google Scholar
  14. 14.
    Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans. Vis. Comput. Graph. 12(5), 741–748 (2006)CrossRefGoogle Scholar
  15. 15.
    Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Jakobson, G., Weissman, M.: Alarm correlation. IEEE Netw. 7(6), 52–59 (1993)CrossRefGoogle Scholar
  17. 17.
    Kim, K., Ko, S., Elmqvist, N., Ebert, D.S.: WordBridge: using composite tag clouds in node-link diagrams for visualizing content and relations in text corpora. In: 2011 44th Hawaii International Conference on System Sciences (HICSS), pp. 1–8, January 2011Google Scholar
  18. 18.
    Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: TwitInfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2011), pp. 227–236, New York. ACM (2011)Google Scholar
  19. 19.
    Motahari-Nezhad, H.R., Saint-Paul, R., Casati, F., Benatallah, B.: Event correlation for process discovery from web service interaction logs. Int. J. Very Large Data Bases 20(3), 417–444 (2011)CrossRefGoogle Scholar
  20. 20.
    Nguyen, D.Q., Le, D.D.: Hyper word clouds: a visualization technique for data and relationships examination. In: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication (IMCOM 2016), pp. 66:1–66:7, New York. ACM (2016)Google Scholar
  21. 21.
    Pape, C., Reissmann, S., Rieger, S.: Restful correlation and consolidation of distributed logging data in cloud environments. In: The 8th International Conference on Internet and Web Applications and Services, pp. 194–199 (2013)Google Scholar
  22. 22.
    Reguieg, H., Toumani, F., Motahari-Nezhad, H.R., Benatallah, B.: Using Mapreduce to scale events correlation discovery for business processes mining. In: 10th International Conference Business Process Management, pp. 279–284 (2012)Google Scholar
  23. 23.
    Ren, D., Zhang, X., Wang, Z., Li, J., Yuan, X.: WeiboEvents: a crowd sourcing Weibo visual analytic system. In: Proceedings of the 2014 IEEE Pacific Visualization Symposium (PACIFICVIS 2014), Washington, DC, pp. 330–334. IEEE Computer Society (2014)Google Scholar
  24. 24.
    Sarkar, M., Brown, M.H.: Graphical fisheye views of graphs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1992), New York, pp. 83–91. ACM (1992)Google Scholar
  25. 25.
    Serrour, B., Gasparotto, D.P., Kheddouci, H., Benatallah, B.: Message correlation and business protocol discovery in service interaction logs. In: 20th International Conference Advanced Information Systems Engineering, pp. 405–419 (2008)Google Scholar
  26. 26.
    Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the 1996 IEEE Symposium on Visual Languages, Washington, DC, USA, pp. 336–343. IEEE Computer Society, September 1996Google Scholar
  27. 27.
    Thomas, J.J., Cook, K.A. (eds.): Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE CS Press, Los Alamitos (2005)Google Scholar
  28. 28.
    Tominski, C., Abello, J., Schumann, H.: Axes-based visualizations with radial layouts. In: Proceedings of the 2004 ACM Symposium on Applied Computing (SAC 2004), New York, pp. 1242–1247. ACM (2004)Google Scholar
  29. 29.
    van Ham, F., Perer, A.: Search, show context, expand on demand: supporting large graph exploration with degree-of-interest. IEEE Trans. Vis. Comput. Graph. 15(6), 953–960 (2009)CrossRefGoogle Scholar
  30. 30.
    van Ham, F., Wattenberg, M., Viegas, F.B.: Mapping text with phrase nets. IEEE Trans. Vis. Comput. Graph. 15(6), 1169–1176 (2009)CrossRefGoogle Scholar
  31. 31.
    Wang, M., Holub, V., Parsons, T., Murphy, J., OSullivan, P.: Scalable run-time correlation engine for monitoring in a cloud computing environment. In: 17th IEEE International Conference and Workshops on Engineering of Computer Based Systems, pp. 29–38 (2010)Google Scholar
  32. 32.
    Wattenberg, M.: Arc diagrams: visualizing structure in strings. In: Proceedings of the IEEE Symposium on Information Visualization (InfoVis 2002), Washington, DC, pp. 110–116. IEEE Computer Society (2002)Google Scholar
  33. 33.
    Wong, P.C., Bergeron, R.D.: 30 years of multidimensional multivariate visualization. In: Scientific Visualization, Overviews, Methodologies, and Techniques, Washington, DC, pp. 3–33. IEEE Computer Society (1997)Google Scholar
  34. 34.
    Ye, Z., Li, S., Zhou, X.: GCplace: geo-cloud based correlation aware data replica placement. In: The 28th Annual ACM Symposium on Applied Computing, pp. 371–376 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Tien-Dung Cao
    • 1
    Email author
  • Dinh-Quyen Nguyen
    • 1
  • Hien Duy Tran
    • 1
  1. 1.School of EngineeringTan Tao UniversityDuc HoaVietnam

Personalised recommendations