Abstract
The H2020 TOREADOR Project adopts a model-driven architecture to streamline big data analytics and make it widely available to companies as a service. Our work in this context focuses on visualization, in particular on how to automate the translation of the visualization objectives declared by the user into a suitable visualization type. To this end we first define a visualization context based on seven prioritizable coordinates for assessing the user’s objectives and describing the data to be visualized; then we propose a skyline-based technique for automatically translating a visualization context into a set of suitable visualization types. Finally, we evaluate our approach on a real use case excerpted from the pilot applications of TOREADOR.
This work was partly supported by the EU-funded project TOREADOR (contract n. H2020-688797).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abela, A.: Advanced Presentations by Design. Pfeiffer, San Francisco (2008)
Ardagna, C., Bellandi, V., Damiani, E., Bezzi, M., Hebert, C.: A model-driven methodology for big data analytics-as-a-service. In: Proceedings of the IEEE International Congress on Big Data, Honolulu, Hawaii (2017)
Bertin, J.: Semiology of Graphics. Esri Press, Redlands (1983)
Börner, K.: Atlas of Knowledge: Anyone Can Map. MIT Press, Cambridge (2015)
Chandra, J., Madhu Shudan, S.: IBA graph selector algorithm for big data visualization using defense data set. Int. J. Sci. Eng. Res. 4(3), 1–7 (2013)
Dadzie, A.S., Rowe, M.: Approaches to visualising linked data: a survey. Semant. web 2(2), 89–124 (2011)
Few, S.: Show Me The Numbers: Designing Tables and Graphs to Enlighten. Analytics Press, Berkeley (2004)
Kano, N., Nobuhiku, S., Fumio, T., Shinichi, T.: Attractive quality and must-be quality. J. Jpn. Soc. Qual. Control 14(2), 39–48 (1984)
Keim, D.: Exploring big data using visual analytics. In: Proceedings of the EDBT/ICDT Workshops (2014)
Keim, D.A.: Information visualization and visual data mining. IEEE Trans. Vis. Comput. Graph. 8(1), 1–8 (2002)
Kleppe, A., Warmer, J., Bast, W.: MDA Explained - The Model Driven Architecture: Practice and Promise. Addison-Wesley, Boston (2003)
Marty, R.: Applied Security Visualization. Addison-Wesley, Boston (2009)
Mindolin, D., Chomicki, J.: Preference elicitation in prioritized skyline queries. VLDB J. 20(2), 157–182 (2011)
Peña, O., Aguilera, U., López-de-Ipiña, D.: Exploring LOD through metadata extraction and data-driven visualizations. Program 50(3), 270–287 (2016)
Russom, P.: Big data analytics. Technical report, TDWI Best Practices Report (2011)
Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343 (1996)
Stevens, S.S.: On the theory of scales of measurement. Science 103(2684), 677–680 (1946)
Wehrend, S., Lewis, C.: A problem-oriented classification of visualization techniques. In: Proceedings of the IEEE Conference on Visualization, pp. 139–143 (1990)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Golfarelli, M., Pirini, T., Rizzi, S. (2017). Goal-Based Selection of Visual Representations for Big Data Analytics. In: de Cesare, S., Frank, U. (eds) Advances in Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10651. Springer, Cham. https://doi.org/10.1007/978-3-319-70625-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-70625-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70624-5
Online ISBN: 978-3-319-70625-2
eBook Packages: Computer ScienceComputer Science (R0)