Monitoring smart cities is a key challenge due the variety of data streams generated from different process (traffic, human dynamics, pollution, energy supply, water supply, etc.). All these streams show us what is happening and as to where and when in the city. The purpose of this paper was to apply different types of glyphs for showing real-time stream evolution of data gathered in the city. The use of glyphs is intended to make the most out of the human capacity for detecting visual patterns.
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Blog SC (2014) Video ‘must haves’ for active surveillance. http://security.americandynamics.net/blog/bid/69402/Video-must-haves-for-active-surveillance
Cai Y (2007) Instinctive computing. In: Artifical intelligence for human computing, Lecture notes in computer Science, vol 4451. Springer, Berlin, pp 17–46
Cavallari R, Martelli F, Rosini R, Buratti C, Verdone R (2014) A survey on wireless body area networks: technologies and design challenges. Commun Surv Tutor IEEE 16(3):1635–1657
Childs H, Geveci B, Schroeder W, Meredith J, Moreland K, Sewell C, Kuhlen T, Bethel E (2013) Research challenges for visualization software. Computer 46(5):34–42
Haklay MM, Weber P (2008) Openstreetmap: user-generated street maps. IEEE Pervasive Comput 7(4):12–18
Hao M, Marwah M, Mittelstadt S, Janetzko H, Keim D, Dayal U, Bash C, Felix C, Patel C, Hsu M, Chen Y (2012) Exploring cyber physical data streams using radial pixel visualizations. In: Visual analytics science and technology (VAST), 2012 IEEE Conference on, pp 225–226. doi:10.1109/VAST.2012.6400541
Henning M (2004) A new approach to object-oriented middleware. IEEE Internet Comput 8(1):66–75
Hsu C-W, Chang CC, Lin CJ (2010) A practical guide to support vector classification. National Taiwan University
Moya F, Villa D, Villanueva FJ, Barba J, Rincön F, Löpez JC (2009) Embedding standard distributed object-oriented middlewares in wireless sensor networks. Wirel Commun Mob Comput 9(3):335–345. doi:10.1002/wcm.545
Park HK, Lee YW, Jang SI, Lee IP (2010) Online visualization of urban noise in ubiquitous-city middleware. In: Advanced communication technology (ICACT), 2010 The 12th international conference on, vol 1, pp 268–271
Park JW, Yun CH, Jung HS, Lee YW (2011) Visualization of urban air pollution with cloud computing. In: Services (SERVICES), 2011 IEEE world congress on, pp 578–583
Rhyne T, Chen M (2013) Cutting-edge research in visualization. Computer 46(5):22–24
Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. Comput Intell Mag IEEE 4(2):24–38
Tory M, Möller T (2004) Human factors in visualization research. IEEE Trans Vis Comput Graph 10:72–84
Villanueva F, Santofimia M, Villa D, Barba J, Lopez J (2013) Civitas: the smart city middleware, from sensors to big data. In: Innovative mobile and internet services in ubiquitous computing (IMIS), 2013 Seventh international conference on, pp 445–450
This work has been partly funded by the Spanish Ministry of Economy and Competitiveness under project REBECCA (TEC2014-58036-C4-1-R) and by the Regional Government of Castilla-La Mancha under project SAND (PEII_2014_046_P).
Communicated by A. Jara, M.R. Ogiela, I. You and F.-Y. Leu.
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Villanueva, F.J., Aguirre, C., Rubio, A. et al. Data stream visualization framework for smart cities. Soft Comput 20, 1671–1681 (2016). https://doi.org/10.1007/s00500-015-1829-8
- Smart cities
- Data visualization
- Human behaviour understanding