Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

Included in the following conference series:

  • 2144 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://sentifi.com.

  2. 2.

    We define here a binary relationship between two tables, i.e., we pick one attribute up from each table to make a relationship. In real database, a relationship may be created from several attributes. However, we can transform them into a single attribute by specifying representative identifier.

  3. 3.

    http://spark.apache.org/graphx/.

  4. 4.

    https://d3js.org/.

  5. 5.

    http://jquery.com/.

  6. 6.

    http://getbootstrap.com/.

  7. 7.

    http://fontawesome.io/.

References

  1. Spark: Lightning-fast cluster computing. http://spark.apache.org

  2. Andrews, D.F.: Plots of high-dimensional data. Biometrics 28(1), 125–136 (1972)

    Article  Google Scholar 

  3. Bertin, J.: Semiology of Graphics: Diagrams, Networks, Maps. University of Wisconsin Press, Madison (1983)

    Google Scholar 

  4. Brehmer, M., Munzner, T.: A multi-level typology of abstract visualization tasks. IEEE Trans. Vis. Comput. Graph. 19(12), 2376–2385 (2013)

    Article  Google Scholar 

  5. Collin, C., Carpendale, S.: VisLink: revealing relationships amongst visualizations. IEEE Trans. Vis. Comput. Graph. 13(6), 1192–1199 (2007)

    Article  Google Scholar 

  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 2009

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  12. Grinstein, G., Trutschl, M., Cvek, U.: High-dimensional visualizations. In: Proceedings of the Visual Data Mining Workshop, KDD (2001)

    Google Scholar 

  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 2010

    Google Scholar 

  14. Holten, D.: Hierarchical edge bundles: visualization of adjacency relations in hierarchical data. IEEE Trans. Vis. Comput. Graph. 12(5), 741–748 (2006)

    Article  Google Scholar 

  15. Inselberg, A.: The plane with parallel coordinates. Vis. Comput. 1(2), 69–91 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  16. Jakobson, G., Weissman, M.: Alarm correlation. IEEE Netw. 7(6), 52–59 (1993)

    Article  Google Scholar 

  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 2011

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  20. Nguyen, D.Q., Le, D.D.: Hyper word clouds: a visualization technique for last.fm 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. 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. 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. 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. 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. 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. 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 1996

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  30. van Ham, F., Wattenberg, M., Viegas, F.B.: Mapping text with phrase nets. IEEE Trans. Vis. Comput. Graph. 15(6), 1169–1176 (2009)

    Article  Google Scholar 

  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. 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. 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. 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 

Download references

Acknowledgment

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.

Author information

Authors and Affiliations

  1. School of Engineering, Tan Tao University, Duc Hoa, Long An, Vietnam

    Tien-Dung Cao, Dinh-Quyen Nguyen & Hien Duy Tran

Authors
  1. Tien-Dung Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dinh-Quyen Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hien Duy Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tien-Dung Cao .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, TD., Nguyen, DQ., Tran, H.D. (2018). Exploring Message Correlation in Crowd-Based Data Using Hyper Coordinates Visualization Technique. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70942-0_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation