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Datenbank-Spektrum

, Volume 17, Issue 3, pp 233–243 | Cite as

VAT: A Scientific Toolbox for Interactive Geodata Exploration

  • Christian Beilschmidt
  • Johannes Drönner
  • Michael Mattig
  • Marco Schmidt
  • Christian Authmann
  • Aidin Niamir
  • Thomas Hickler
  • Bernhard Seeger
Schwerpunktbeitrag

Abstract

Data-driven research requires interactive systems supporting fast and intuitive data exploration. An important component is the user interface that facilitates this process. In biodiversity research, data is commonly of spatio-temporal nature. This poses unique opportunities for visual analytics approaches. In this paper we present the core concepts of the web-based front end of our vat (Visualization, Analysis and Transformation) system, a distributed geo-processing application. We present the results of two user studies and highlight unique features, among others for the management of time and the generalization of data.

Keywords

Visualization Biodiversity Scientific Workflows 

Notes

Acknowledgements

This work has been supported by the Deutsche Forschungsgemeinschaft (DFG) under grant no. SE 553/7-2 and by the Bundesministerium für Bildung und Forschung (BMBF) under grant no. 01LL1301.

References

  1. 1.
    Altintas I, Berkley C, Jaeger E, Jones MB, Ludäscher B, Mock S (2004) Kepler: An Extensible System for Design and Execution of Scientific Workflows. In: SSDBM, pp. 423–424. IEEE Computer Society.  https://doi.org/10.1109/SSDM.2004.1311241
  2. 2.
    Andrienko N, Andrienko G, Gatalsky P (2003) Exploratory Spatio-Temporal Visualization: An Analytical Review. J Vis Lang Comput 14(6):503–541CrossRefGoogle Scholar
  3. 3.
    Authmann C, Beilschmidt C, Drönner J, Mattig M, Seeger B (2015) Rethinking Spatial Processing in Data-Intensive Science. In: Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2.–3. März 2015, Hamburg, Germany. BTW Workshops, LNI, vol. 242, Bonn, Germany, pp. 161–170Google Scholar
  4. 4.
    Authmann C, Beilschmidt C, Drönner J, Mattig M, Seeger B (2015) VAT: A System for Visualizing, Analyzing and Transforming Spatial Data in Science. Datenbank-Spektrum 15(3):175–184CrossRefGoogle Scholar
  5. 5.
    Beilschmidt C, Drönner J, Mattig M, Schmidt M, Authmann C, Niamir A, Hickler T, Seeger B (2017) Interactive Data Exploration for Geoscience. In: Datenbanksysteme für Business, Technologie und Web (BTW 2017), 17. Fachtagung des GI-Fachbereichs Datenbanken und Informationssysteme (DBIS), 6.–10. März 2017, Stuttgart, Germany, Workshopband. BTW (Workshops), LNI, vol. P‑266, Bonn, Germany, pp. 117–126Google Scholar
  6. 6.
    Beilschmidt C, Drönner J, Mattig M, Seeger B (2017) VAT: A System for Data-Driven Biodiversity Research. In: EDBT, pp. 546–549. http://OpenProceedings.org Google Scholar
  7. 7.
    Beilschmidt C, Fober T, Mattig M, Seeger B (2017) Quality Measures for Visual Point Clustering in Geospatial Mapping. In: W2GIS vol. 10181. LNCS, Cham, Switzerland, pp. 153–168Google Scholar
  8. 8.
    Buneman P, Davidson S, Frew J (2016) Why Data Citation is a Computational Problem. Communications of the ACM 59(9):50–57.  https://doi.org/10.1145/2893181 CrossRefGoogle Scholar
  9. 9.
    Diepenbroek M, Glöckner F, Grobe P et al (2014) Towards an Integrated Biodiversity and Ecological Research Data Management and Archiving Platform: The German Federation for the Curation of Biological Data (GFBio). In: GI-Jahrestagung, LNI, vol. 232, pp. 1711–1721. GIGoogle Scholar
  10. 10.
    Eddelbuettel D (2013) Seamless R and C++ Integration with Rcpp. Springer, New York, USACrossRefzbMATHGoogle Scholar
  11. 11.
    Gebbert S, Pebesma E (2017) The GRASS GIS Temporal Framework. Int J Geogr Inf Sci 31(7):1273–1292CrossRefGoogle Scholar
  12. 12.
    Jänicke S, Heine C, Stockmann R, Scheuermann G (2012) Comparative Visualization of Geospatial-temporal Data. In: GRAPP/IVAPP, pp. 613–625. SciTePressGoogle Scholar
  13. 13.
    Jetz W, McPherson J, Guralnick R (2012) Integrating Biodiversity Distribution Knowledge: Toward a global Map of Life. Trends Ecol Evol 27(3):151–159CrossRefGoogle Scholar
  14. 14.
    McLennan M, Clark SM, McKenna F, Deelman E et al (2013) Bringing Scientific Workflow to the Masses via Pegasus and HUBZero. In: IWSG, CEUR Workshop Proceedings, vol. 993. http://CEUR-WS.org
  15. 15.
    Open Geospatial Consortium Inc. (2011) OpenGIS Implementation Standard for Geographic information – Simple feature access – Part 1: Common architectureGoogle Scholar
  16. 16.
    Roth RE (2013) Interactive maps: What we know and what we need to know. J Spatial Inf Sci 2013(6):59–115Google Scholar
  17. 17.
    Shi W, Cheung C (2006) Performance Evaluation of Line Simplification Algorithms for Vector Generalization. Cartogr J 43(1):27–44CrossRefGoogle Scholar
  18. 18.
    Steed CA, Ricciuto DM, Shipman G et al (2013) Big Data Visual Analytics for Exploratory Earth System Simulation Analysis. In: Computers and Geosciences vol. 61. Elsevier, New York, USA, pp. 71–82Google Scholar
  19. 19.
    Wolstencroft K, Haines R, Fellows D et al (2013) The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud. Nucleic Acids Research 41(Webserver-Issue), pp. 557–561Google Scholar
  20. 20.
    Zhang J, You S, Gruenwald L (2017) Towards GPU-Accelerated Web-GIS for Query-Driven Visual Exploration. In: W2GIS, Lecture Notes in Computer vol. 10181. Science, Cham, Switzerland, pp. 119–136Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2017

Authors and Affiliations

  • Christian Beilschmidt
    • 1
  • Johannes Drönner
    • 1
  • Michael Mattig
    • 1
  • Marco Schmidt
    • 2
  • Christian Authmann
    • 1
  • Aidin Niamir
    • 2
  • Thomas Hickler
    • 2
    • 3
  • Bernhard Seeger
    • 1
  1. 1.Department of Mathematics and Computer ScienceUniversity of MarburgMarburgGermany
  2. 2.Senckenberg Biodiversity and Climate Research Centre (BiK-F)Frankfurt am MainGermany
  3. 3.Department of Physical GeographyGoethe UniversityFrankfurt am MainGermany

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