Time-series Application on Big Data - Visualization of Consumption in Supermarkets

Catarina Maçãs, Pedro Cruz, Hugo Amaro, Evgheni Polisciuc, Tiago Carvalho, Frederico Santos, Penousal Machado

2015

Abstract

The evolution of technology is changing how people work within organizations. Information about customer consumption leads to a new era of business intelligence, wherein Big Data is analyzed to improve business. In this project we apply information visualization in the context of Big Data for product’s consumption. The aim of this project is to visualize the evolution of consumption, to detect typical and periodic behaviors and emphasize the atypical ones. In this article we present our workflow—from finding periodic behaviors to create a final visualization using time-series and small-multiples techniques. With the final visualization we are able to show consumption behaviors and highlight the deviations from typical consumption days.

References

  1. Berkovich, S. and Liao, D. (2012). On clusterization of big data streams. In Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications, page 26. ACM.
  2. Boyandin, I., Bertini, E., Bak, P., and Lalanne, D. (2011). Flowstrates: An Approach for Visual Exploration of Temporal Origin-destination Data. In Computer Graphics Forum, volume 30, pages 971-980. Wiley Online Library.
  3. Byron, L. and Wattenberg, M. (2008). Stacked graphsgeometry & aesthetics. IEEE Trans. Vis. Comput. Graph., 14(6):1245-1252.
  4. Catmull, E. and Rom, R. (1974). A class of local interpolating splines. Computer aided geometric design, 74:317-326.
  5. Cleveland, W. S. (1985). The elements of graphing data. Wadsworth Advanced Books and Software Monterey, CA.
  6. Fisher, D., Drucker, S., and Czerwinski, M. (2014). Business intelligence analytics. Computer Graphics and Applications, IEEE, 34(5):22-24.
  7. Heer, J., Kong, N., and Agrawala, M. (2009). Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1303- 1312. ACM.
  8. Keim, D., Qu, H., and Ma, K.-L. (2013). Big-data visualization. Computer Graphics and Applications, IEEE, 33(4):20-21.
  9. Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual analytics: Scope and challenges. Springer.
  10. Rajpurohit, A. (2013). Big data for business managersbridging the gap between potential and value. In Big Data, 2013 IEEE International Conference on, pages 29-31. IEEE.
  11. Tufte, E. R. (1991). Envisioning information. Optometry & Vision Science, 68(4):322-324.
  12. Tufte, E. R. and Graves-Morris, P. (1983). The visual display of quantitative information, volume 2. Graphics press Cheshire, CT.
  13. Viégas, F. B., Wattenberg, M., and Dave, K. (2004). Studying cooperation and conflict between authors with history flow visualizations. In Proceedings of the SIGCHI conference on Human factors in computing systems, pages 575-582. ACM.
  14. Zhang, J., Chen, Y., and Li, T. (2013). Opportunities of innovation under challenges of big data. In Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on, pages 669-673. IEEE.
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Paper Citation


in Harvard Style

Maçãs C., Cruz P., Amaro H., Polisciuc E., Carvalho T., Santos F. and Machado P. (2015). Time-series Application on Big Data - Visualization of Consumption in Supermarkets . In Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015) ISBN 978-989-758-088-8, pages 239-246. DOI: 10.5220/0005307702390246


in Bibtex Style

@conference{ivapp15,
author={Catarina Maçãs and Pedro Cruz and Hugo Amaro and Evgheni Polisciuc and Tiago Carvalho and Frederico Santos and Penousal Machado},
title={Time-series Application on Big Data - Visualization of Consumption in Supermarkets},
booktitle={Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)},
year={2015},
pages={239-246},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005307702390246},
isbn={978-989-758-088-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2015)
TI - Time-series Application on Big Data - Visualization of Consumption in Supermarkets
SN - 978-989-758-088-8
AU - Maçãs C.
AU - Cruz P.
AU - Amaro H.
AU - Polisciuc E.
AU - Carvalho T.
AU - Santos F.
AU - Machado P.
PY - 2015
SP - 239
EP - 246
DO - 10.5220/0005307702390246