VisInfo: a digital library system for time series research data based on exploratory search—a user-centered design approach

  • Jürgen BernardEmail author
  • Debora Daberkow
  • Dieter Fellner
  • Katrin Fischer
  • Oliver Koepler
  • Jörn Kohlhammer
  • Mila Runnwerth
  • Tobias Ruppert
  • Tobias Schreck
  • Irina Sens


To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This trend is expected to continue, considering future DL challenges such as content-based access to new document types, visual search, and exploration for information landscapes, or big data in general. To cope with these challenges, DL actors need to collaborate with external specialists from different domains to complement each other and succeed in given tasks such as making research data publicly available. Through these interdisciplinary approaches, the DL ecosystem may contribute to applications focused on data-driven science and digital scholarship. In this work, we present VisInfo (2014) , a web-based digital library system (DLS) with the goal to provide visual access to time series research data. Based on an exploratory search (ES) concept (White and Roth in Synth Lect Inf Concepts Retr Serv 1(1):1–98, 2009), VisInfo at first provides a content-based overview visualization of large amounts of time series research data. Further, the system enables the user to define visual queries by example or by sketch. Finally, VisInfo presents visual-interactive capability for the exploration of search results. The development process of VisInfo was based on the user-centered design principle. Experts from computer science, a scientific digital library, usability engineering, and scientists from the earth, and environmental sciences were involved in an interdisciplinary approach. We report on comprehensive user studies in the requirement analysis phase based on paper prototyping, user interviews, screen casts, and user questionnaires. Heuristic evaluations and two usability testing rounds were applied during the system implementation and the deployment phase and certify measurable improvements for our DLS. Based on the lessons learned in VisInfo, we suggest a generalized project workflow that may be applied in related, prospective approaches.


Information visualization Visual analytics Exploratory search Research data Time series analysis Digital library system 



The authors would like to thank Gerd König-Langlo, Rainer Sieger, Hannes Grobe, and their group at the Alfred Wegener Institute for generously supporting the project and kindly providing data and expert feedback. Special thanks also to the Baseline Surface Radiation Network for providing the research data. The usability tests were also conducted with scientists from the BSRN community. The participants provided helpful insights and feedback. This work was supported by a grant from the Leibniz Association as part of the “Joint Initiative for Research and Innovation” program.

Supplementary material

Supplementary material 1 (mp4 63720 KB)


  1. 1.
    Ahmed, Z., Yost, P., McGovern, A., Weaver, C.: Steerable clustering for visual analysis of ecosystems. EuroVA, 49–52 (2011)Google Scholar
  2. 2.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, London (2011). doi: 10.1007/978-0-85729-079-3 CrossRefGoogle Scholar
  3. 3.
    Ailamaki, A., Kantere, V., Dash, D.: Managing scientific data. Commun. ACM 53(6), 68–78 (2010)CrossRefGoogle Scholar
  4. 4.
    Belkin, N., Dumais, S., Scholtz, J., Wilkinson, R.: Evaluating interactive information retrieval systems. In: Extended abstracts of the 2004 conference on Human factors and computing systems - CHI ’04, p. 1594. ACM Press, New York (2004). doi: 10.1145/985921.986162
  5. 5.
    Bernard, J., Brase, J., Fellner, D., Koepler, O., Kohlhammer, J., Ruppert, T., Schreck, T., Sens, I.: A visual digital library approach for time-oriented scientific primary data. Springer Int. J. Digit. Libr., ECDL 2010 Special Issue 11(2), 111–123 (2011)Google Scholar
  6. 6.
    Bernard, J., König-Langlo, G., Sieger, R.: Time-oriented earth observation measurements from the baseline surface radiation network (bsrn) in the years 1992 to 2012, reference list of 6813 datasets. (2012). DOI doi: 10.1594/PANGAEA.787726
  7. 7.
    Bernard, J., Ruppert, T., Goroll, O., May, T., Kohlhammer, J.: Visual-interactive preprocessing of time series data. In: Kerren, A., Seipel, S. (eds.) SIGRAD, vol. 81, pp. 39–48. Linköping University Electronic Press, Sweden (2012)Google Scholar
  8. 8.
    Bernard, J., Ruppert, T., Scherer, M., Kohlhammer, J., Schreck, T.: Content-based layouts for exploratory metadata search in scientific research data. In: Proceedings of JCDL, pp. 139–148. ACM (2012)Google Scholar
  9. 9.
    Bernard, J., Ruppert, T., Scherer, M., Schreck, T., Kohlhammer, J.: Guided discovery of interesting relationships between time series clusters and metadata properties. In: Proceedings of i-KNOW, pp. 22:1–22:8. ACM (2012). DOI doi: 10.1145/2362456.2362485
  10. 10.
    Bernard, J., Steiger, M., Widmer, S., LckeTieke, H., May, T., Kohlhammer, J.: Visual-interactive exploration of interesting multivariate relations in mixed research data sets. Comput. Graph. Forum 33(3), 291–300 (2014). doi: 10.1111/cgf.12385 CrossRefGoogle Scholar
  11. 11.
    Bernard, J., Wilhelm, N., Krüger, B., May, T., Schreck, T., Kohlhammer, J.: MotionExplorer: exploratory search in human motion capture data based on hierarchical aggregation. IEEE TVCG 19(12), 2257–2266 (2013). doi: 10.1109/TVCG.2013.178 Google Scholar
  12. 12.
    Bernard, J., Wilhelm, N., Scherer, M., May, T., Schreck, T.: TimeSeriesPaths: projection-based explorative analysis of multivariate time series data. J. WSCG 20, 97–106 (2012)Google Scholar
  13. 13.
    Borgman, C., Wallis, J.C., Enyedy, N.: Building Digital Libraries for Scientific Data: An Exploratory Study of Data Practices in Habitat Ecology. In: ECDL 2006, pp. 170–183. Springer. LINCS (2006)Google Scholar
  14. 14.
    Brase, J.: Datacite - A Global Registration Agency for Research Data. In: Cooperation and Promotion of Information Resources in Science and Technology, 2009. COINFO ’09, pp. 257–261 (2009)Google Scholar
  15. 15.
    Candela, L., Castelli, D., Ferro, N., Koutrika, G., Meghini, C., Pagano, P., Ross, S., Soergel, D., Agosti, M., Dobreva, M. (eds.): The DELOS Digital Library Reference model. Foundations for digital Libraries (Version 0.98). ISTI-CNR at Gruppo ALI, Pisa (2008).
  16. 16.
    Card, S.K., Mackinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization: Using Vision to Think. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  17. 17.
    Costas, R., Meijer, I., Zahedi, Z., Wouters, P.: The value of research data-metrics for datasets from a cultural and technical point of view. A Knowledge Exchange Report. Available from: (2013)
  18. 18.
    Davenport, T.H., Patil, D., et al.: Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. 90(10), 70–77 (2012)Google Scholar
  19. 19.
    Deelman, E., Gannon, D., Shields, M., Taylor, I.: Workflows and e-science: an overview of workflow system features and capabilities. Future Gener. Comput. Syst. 25(5), 528–540 (2009). doi: 10.1016/j.future.2008.06.012 CrossRefGoogle Scholar
  20. 20.
    Duke, M., Day, M., Heery, R., Carr, L.A., Coles, S.J.: Enhancing access to research data: the challenge of crystallography. In: Proceedings of JCDL, pp. 46–55. ACM, New York, NY, USA (2005)Google Scholar
  21. 21.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–34. American Association for Artificial Intelligence, Menlo Park, CA, USA (1996)Google Scholar
  22. 22.
    Fu, Tc: A review on time series data mining. Eng. Appl. Artif. Intell 24(1), 164–181 (2011). doi: 10.1016/j.engappai.2010.09.007 CrossRefGoogle Scholar
  23. 23.
    Fuchs, R., Hauser, H.: Visualization of multi-variate scientific data. Comput. Graph. Forum 28, 1670–1690 (2009)CrossRefGoogle Scholar
  24. 24.
    Hearst, M.A.: Search User Interfaces, 1st edn. Cambridge University Press, Cambridge (2009)CrossRefGoogle Scholar
  25. 25.
    Herrmannova, D., Knoth, P.: Visual search for supporting content exploration in large document collections. D-Lib Magazine 18, 7–8 (2012)Google Scholar
  26. 26.
    Hey, A.J.G., Tansley, S., Tolle, K.M.: The Fourth Paradigm: Data-Intensive Scientific Discovery. Microsoft Research (2009)Google Scholar
  27. 27.
    Hill, L.L., Carver, L., Larsgaard, M., Dolin, R., Smith, T.R., Frew, J., Rae, M.A.: Alexandria digital library: user evaluation studies and system design. J. ASIS 51(3), 246–259 (2000). doi: 10.1002/(SICI)1097-4571(2000)51:3<246::AID-ASI4>3.0.CO;2-6
  28. 28.
    Hoeber, O.: User Evaluation Methods for Visual Web Search Interfaces. 2009 13th International Conference Information Visualisation pp. 139–145 (2009). doi: 10.1109/IV.2009.21
  29. 29.
    Hull, D., Pettifer, S.R., Kell, D.B.: Defrosting the digital library: bibliographic tools for the next generation web. PLos Comput. Biol. 4(10):e1000204 (2008)Google Scholar
  30. 30.
    Jeng, J.: Usability assessment of academic digital libraries: effectiveness, efficiency, satisfaction, and learnability. Libri 55(2–3), 96–121 (2005). doi: 10.1515/LIBR.2005.96 Google Scholar
  31. 31.
    Jeng, J.H.: Usability of digital libraries. In: Proceedings of JCDL, p. 407. ACM Press, New York, NY, USA (2004). doi: 10.1145/996350.996473
  32. 32.
    John, B.E.: Evaluating usability evaluation techniques. ACM Comput. Surv. 28(4es), 139-es (1996)CrossRefGoogle Scholar
  33. 33.
    Kehrer, J., Hauser, H.: Visualization and visual analysis of multifaceted scientific data: A survey. IEEE TVCG 19(3), 495–513 (2013). doi: 10.1109/TVCG.2012.110 Google Scholar
  34. 34.
    Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F. (eds.): Mastering the Information Age: Solving Problems with Visual Analytics. Eurographics Association, Aire-la-Ville (2011)Google Scholar
  35. 35.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inf. Syst. 3, 263–286 (2000)CrossRefGoogle Scholar
  36. 36.
    Kohonen, T., Schroeder, M.R., Huang, T.S. (eds.): Self-Organizing Maps, 3rd edn. Springer-Verlag New York Inc, Secaucus, NJ, USA (2008)Google Scholar
  37. 37.
    Lewis, C., Polson, P.G., Wharton, C., Rieman, J.: Testing a walkthrough methodology for theory-based design of walk-up-and-use interfaces. In: Proceedings of the SIGCHI, pp. 235–242. ACM Press, New York, NY, USA (1990). doi: 10.1145/97243.97279
  38. 38.
    Liao, T.W.: Clustering of time series data-a survey. Pattern Recognit. 38, 1857–1874 (2005)CrossRefzbMATHGoogle Scholar
  39. 39.
    Ludäscher, B., Lin, K., Bowers, S., Jaeger-Frank, E., Brodaric, B., Baru, C.: Managing scientific data: from data integration to scientific workflows. Geoinformatics: Data to Knowledge, Geolog. Society of America Special Paper 397 pp. 109–129 (2006)Google Scholar
  40. 40.
    Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)CrossRefGoogle Scholar
  41. 41.
    Marcial, L.H., Hemminger, B.M.: Scientific data repositories on the web: an initial survey. JASIST 61(10), 2029–2048 (2010)CrossRefGoogle Scholar
  42. 42.
    Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009). doi: 10.1109/tvcg.2009.111 CrossRefGoogle Scholar
  43. 43.
    Nielsen, J.: The usability engineering life cycle. Computer 25(3), 12–22 (1992). doi: 10.1109/2.121503 CrossRefGoogle Scholar
  44. 44.
    Nielsen, J., Landauer, T.K.: A mathematical model of the finding of usability problems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems—CHI ’93, pp. 206–213. ACM Press, New York (1993). doi: 10.1145/169059.169166
  45. 45.
    Nielsen, J., Molich, R.: Heuristic evaluation of user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Empowering People–CHI ’90, April, pp. 249–256. ACM Press, New York (1990). DOI doi: 10.1145/97243.97281
  46. 46.
    Nocke, T., Sterzel, T., Böttinger, M., Wrobel, M.: Visualization of climate and climate change data: an overview. Digital Earth Summit on Geoinformatics 2008, Tools for Global Change Research, pp. 226–232. Herbert Wichmann, Heidelberg (2008)Google Scholar
  47. 47.
    Ohmura, A., Dutton, E.G., Forgan, B., Fröhlich, C., Gilgen, H., Hegner, H., Heimo, A., König-Langlo, G., Mcarthur, B., Müller, G., Philipona, R., Pinker, R., Whitlock, C.D., Dehne, K., Wild, M.: Baseline surface radiation network (BSRN/WCRP): new precision radiometry for climate research. Bull. Am. Met. Soc. 79, 2115–2136 (1998)Google Scholar
  48. 48.
    PANGAEA—Data Publisher for Earth and Environmental Science: Accessed 14 Oct 2014. doi: 10.1594/pangaea
  49. 49.
    Plaisant, C.: The challenge of information visualization evaluation. In: Proceedings of the Working Conference on Advanced Visual Interfaces, pp. 109–116. ACM Press, New York (2004). doi: 10.1145/989863.989880
  50. 50.
    Reeves, T.C., Buhr, S., Barker, L.: Evaluating digital libraries: toward evolution of concepts. In: Proceedings of JCDL, vol. 49, p. 420. ACM Press, New York (2005). doi: 10.1145/1065385.1065525
  51. 51.
    Rubin, J., Chisnell, D.: Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests, 2nd edn. Wiley, Indianapolis (2008)Google Scholar
  52. 52.
    Ruppert, T., Bernard, J., Kohlhammer, J.: Bridging knowledge gaps in policy analysis with information visualization. In: EGOV/ePart Ongoing Research, LNI, vol. 221, pp. 92–103. GI (2013)Google Scholar
  53. 53.
    Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)Google Scholar
  54. 54.
    Saracevic, T.: Digital library evaluation: toward evolution of concepts. Libr. Trend 49(2), 350–369 (2000)Google Scholar
  55. 55.
    Scherer, M., Bernard, J., Schreck, T.: Retrieval and exploratory search in multivariate research data repositories using regressional features. In: Proceedings of JCDL, pp. 363–372. ACM, New York (2011)Google Scholar
  56. 56.
    Schreck, T., Bernard, J., Tekušová, T., Kohlhammer, J.: Visual cluster analysis of trajectory data with interactive Kohonen maps. Palgrave Macmillan Inf. Vis. 8, 14–29 (2009).
  57. 57.
    SDSS—The Sloan Digital Sky Survey. Accessed 15 Oct 2014
  58. 58.
    Sedlmair, M., Meyer, M., Munzner, T.: Design study methodology: reflections from the trenches and the stacks. IEEE Trans. Vis. Comput. Graph. (Proc. InfoVis) 18(12), 2431–2440 (2012)CrossRefGoogle Scholar
  59. 59.
    Shackel, B.: Usability context, framework, definition, design and evaluation. In: Shackel, B., Richardson, S.J. (eds.) Human Factors for Informatics Usability, pp. 21–37. Cambridge University Press, New York (1991)Google Scholar
  60. 60.
    Sheble, L.: Greenstone User and Developer Survey 2009 (2009).
  61. 61.
    Stein, L.D.: Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nat. Rev. Genet. 9(9), 678–688 (2008)CrossRefGoogle Scholar
  62. 62.
    Strötgen, J., Gertz, M.: Event-centric search and exploration in document collections. In: Proceedings of JCDL, pp. 223–232. ACM, New York (2012)Google Scholar
  63. 63.
    Suber, P.: Open Access. MIT Press, Cambridge (2012)Google Scholar
  64. 64.
    Tominski, C., Donges, J.F., Nocke, T.: Information visualization in climate research. In: Information Visualisation, pp. 298–305. IEEE Computer Society, Washington (2011)Google Scholar
  65. 65.
    Tsatsaronis, G., Varlamis, I., Torge, S., Reimann, M., Nørvåg, K., Schroeder, M., Zschunke, M.: How to become a group leader? Or modeling author types based on graph mining. In: Proceedings of TPDL, pp. 15–26. Springer, Berlin (2011)Google Scholar
  66. 66.
    Van Wijk, J., Van Selow, E.: Cluster and calendar based visualization of time series data. In: Symposium on Information Visualization, pp. 4–9. IEEE Comp. Soc. (1999)Google Scholar
  67. 67.
    VisInfo—Visual Access to Time-oriented Research Data. Accessed 14 Oct 2014
  68. 68.
    White, R., Roth, R.: Exploratory search: beyond the query-response paradigm. Synth. Lect. Inf. Concepts Retr. Serv. 1(1), 1–98 (2009)Google Scholar
  69. 69.
    Yuan, X., Zhang, X., Trofimovsky, A.: Testing visualization on the use of information systems. In: Proceedings of IIiX, p. 365 (2010). doi: 10.1145/1840784.1840840
  70. 70.
    Ziegler, H., Jenny, M., Gruse, T., Keim, D.: Visual market sector analysis for financial time series data. In: Proceedings of VAST, pp. 83–90 (2010). doi: 10.1109/VAST.2010.5652530

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Jürgen Bernard
    • 1
    Email author
  • Debora Daberkow
    • 2
  • Dieter Fellner
    • 3
  • Katrin Fischer
    • 2
  • Oliver Koepler
    • 2
  • Jörn Kohlhammer
    • 1
  • Mila Runnwerth
    • 2
  • Tobias Ruppert
    • 1
  • Tobias Schreck
    • 4
  • Irina Sens
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.German National Library of Science and TechnologyHannoverGermany
  3. 3.Technische Universität DarmstadtDarmstadtGermany
  4. 4.University of KonstanzKonstanzGermany

Personalised recommendations