Skip to main content
SpringerLink
Log in
Book cover

AVI Workshop on Big Data Applications

AVI-BDA 2016: Advanced Visual Interfaces. Supporting Big Data Applications pp 104–114Cite as

Visual Analytics and Mining over Big Data. Discussing Some Issues and Challenges, and Presenting a Few Experiences

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10084))

Abstract

In this short position paper, we present a few concrete experiences of Visual Analytics (VA) over big data; as our experiences have been gained on the application domains of cyber-security and Open Source Intelligence (OSINT), which are very relevant and crucial domains targets of possible Virtual Research Environments (VREs), we also discuss and propose an high-level reference architecture and pipeline for a Big Data service in VREs dealing with such aspects, in which the VA part is crucial in order to provide effectiveness to users.

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

Notes

  1. 1.

    cf. UNECE. Classification of types of big data. http://www1.unece.org/stat/platform/display/bigdata/Classification+of+Types+of+Big+Data. Online (accessed on 31 August 2015).

  2. 2.

    cf. A. Carusi, T. Reimer. Virtual research environment collaborative landscape study. JISC, Bristol, 2010. Online: http://www.jisc.ac.uk/media/documents/publications/vrelandscapereport.pdf.

  3. 3.

    EU FP-7 Panoptesec project, http://www.panoptesec.eu.

References

  1. Angelini, M., Prigent, N., Santucci, G.: Percival: proactive and reactive attack and response assessment for cyber incidents using visual analytics. In: 2015 IEEE Symposium on Visualization for Cyber Security (VizSec), pp. 1–8. IEEE (2015)

    Google Scholar 

  2. Angelini, M., Santucci, G.: Modeling incremental visualizations. In: Proceedings of the EuroVis Workshop on Visual Analytics (EuroVA 2013), pp. 13–17 (2013)

    Google Scholar 

  3. Angelini, M., Santucci, G.: Visual cyber situational awareness for critical infrastructures. In: Proceedings of the 8th International Symposium on Visual Information Communication and Interaction (2015)

    Google Scholar 

  4. Jagadish, H., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)

    Article  Google Scholar 

  5. Keim, D.A., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the information age-solving problems with visual analytics (2010)

    Google Scholar 

  6. Keim, D.A., Kohlhammer, J., Santucci, G., Mansmann, F., Wanner, F., Schaefer, M.: Visual analytics challenges. In: eChallenges 2009 (2009)

    Google Scholar 

  7. Keim, D.A., Mansmann, F., Schneidewind, J., Ziegler, H.: Challenges in visual data analysis. In: Information Visualization (IV 2006). IEEE (2006)

    Google Scholar 

  8. Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H.: Visual analytics: scope and challenges. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds.) visual data mining. LNCS, vol. 4404, pp. 76–90. Springer, Heidelberg (2008). doi:10.1007/978-3-540-71080-6_6

    Chapter  Google Scholar 

  9. Sedig, K., Ola, O.: The challenge of big data in public health: an opportunity for visual analytics. Online J. Public Health Inform. 5(3), 223 (2014)

    Article  Google Scholar 

  10. Shneiderman, B.: Extreme visualization: squeezing a billion records into a million pixels. In: SIGMOD 2008 (2008)

    Google Scholar 

  11. Thomas, J., Kielman, J.: Challenges for visual analytics. In: Information Visualization 2009 (2009)

    Google Scholar 

  12. Wong, P.C., Shen, H.-W., Johnson, C.R., Chen, C., Ross, R.B.: The top 10 challenges in extreme-scale visual analytics. IEEE Comput. Graph. Appl. 32(4), 63 (2012)

    Article  Google Scholar 

  13. Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., Keim, D.: Visual analytics for the big data era - A comparative review of state-of-the-art commercial systems. In:2012 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE (2012)

    Google Scholar 

  14. Bornschlegl, M.X., Berwind, K., Kaufmann, M., Hemmje, M.L.: Towards a reference model for advanced visual interfaces supporting big data analysis. In: 17th International Conference on Internet Computing and Internet of Things, ICOMP 2016 (2016)

    Google Scholar 

Download references

Acknowledgments

This work has been partly supported by the EU FP7 project PANOPTESEC, and the Italian projects Social Museum e Smart Tourism (CTN01_00034_23154), NEPTIS (PON03PE_00214_3), and RoMA - Resilence of Metropolitan Areas (SCN_00064).

Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria Informatica Automatica e Gestionale, Sapienza Università di Roma, via Ariosto 25, 00185, Rome, Italy

    Marco Angelini, Tiziana Catarci, Massimo Mecella & Giuseppe Santucci

Authors
  1. Marco Angelini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tiziana Catarci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Massimo Mecella
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Giuseppe Santucci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Massimo Mecella .

Editor information

Editors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Hagen, Hagen, Germany

    Marco X. Bornschlegl

  2. Faculty of Mathematics and Computer Science, University of Hagen, Hagen, Germany

    Felix C. Engel

  3. School of Computing and Mathematics, University of Ulster, Newtownabbey, Antrim, UK

    Raymond Bond

  4. Faculty of Mathematics and Computer Science, University of Hagen, Hagen, Germany

    Matthias L. Hemmje

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Angelini, M., Catarci, T., Mecella, M., Santucci, G. (2016). Visual Analytics and Mining over Big Data. Discussing Some Issues and Challenges, and Presenting a Few Experiences. In: Bornschlegl, M.X., Engel, F.C., Bond, R., Hemmje, M.L. (eds) Advanced Visual Interfaces. Supporting Big Data Applications. AVI-BDA 2016. Lecture Notes in Computer Science(), vol 10084. Springer, Cham. https://doi.org/10.1007/978-3-319-50070-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50070-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50069-0

  • Online ISBN: 978-3-319-50070-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation