Abstract
In this work, the structure for the prototype construction of an application that can be framed within ubiquitous sensing is proposed. The objective of application is to allow that a user knows through his mobile device which other users of his environment are doing the same activity as him. Therefore, the knowledge is obtained from data acquired by pervasive sensors. The FIWARE infrastructure is used to allow to homogenize the data flows.
An important element of the application is the Intelligent Data Analysis module where, within the Apache Storm technology, a Data Mining technique will be used. This module identifies the activity carried out by mobile device user based on the values obtained by the different sensors of the device.
The Data Mining technique used in this module is an extension of the Nearest Neighbors technique. This extension allows the imperfect data processing, and therefore, the effort that must be made in the data preprocessing to obtain the minable view of data is reduced. It also allows us to parallelize part of the process by using the Apache Storm technology.
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References
de la Concepción, M.Á.Á., Morillo, L.M.S., García, J.A.Á., González-Abril, L.: Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive Mob. Comput. 34, 3–13 (2017). https://doi.org/10.1016/j.pmcj.2016.05.002
Barth, R.S., Galante, R.M.: Passenger density and flow analysis and city zones and bus stops classification for public bus service management. In: Proceedings of the Brazilian Symposium on Databases, Salvador, Brazil, pp. 217–222 (2016)
Cadenas, J.M., Garrido, M.C., Martínez, R., Muñoz, E., Bonissone, P.P.: A fuzzy K-nearest neighbor classifier to deal with imperfect data. Soft. Comput. 22, 18 (2017). https://doi.org/10.1007/s00500-017-2567-x
Ceapa, I., Smith, C., Capra, L.: Avoiding the crowds: understanding tube station congestion patterns from trip data. In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, New York, 134–141 (2012)
Chen, S.M.: New methods for subjective mental workload assessment and fuzzy risk analysis. Cybern. Syst. 27(5), 449–472 (1996). https://doi.org/10.1080/019697296126417
DeLuca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf. Control 20(4), 301–312 (1972). https://doi.org/10.1016/S0019-9958(72)90199-4
Fitbit: The fitness app for everyone, San Francisco, CA, Fitbit App. https://www.fitbit.com/es/app
FIWARE Community. To build an open sustainable ecosystem around public, royalty-free and implementation-driven software platform standards that will ease the development of new Smart Applications in multiple sectors everyone. https://www.fiware.org
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 1–18 (2009)
Janikow, C.Z.: FID3.5: one of the FID programs originally proposed in “Fuzzy decision trees: issues and methods”. IEEE Trans. Man Syst. Cybern. 28(1), 1–14 (1998). http://www.cs.umsl.edu/janikow/fid/index.html
Kozina, S., Gjoreski, H., Gams, M., Luštrek, M.: Efficient activity recognition and fall detection using accelerometers. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds.) EvAAL 2013. CCIS, vol. 386, pp. 13–23. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41043-7_2
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2010). https://doi.org/10.1145/1964897.1964918
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)
Lau, S.L., König, I., David, K., Parandian, B., Carius-Düssel, C., Schultz, M.: Supporting patient monitoring using activity recognition with a smartphone. In: Proceedings of the 7th International Symposium on Wireless Communication Systems, York, UK, pp. 810–814 (2010)
Mashita, T., Shimatani, K., Iwata, M., Miyamoto, H., Komaki, D., Hara, T., Kiyokawa, K., Takemura, H., Nishio, S.: Human activity recognition for a content search system considering situations of smartphone users. In: Proceedings of the IEEE Virtual Reality Workshops, Costa Mesa, CA, pp. 1–2 (2012)
Mun, M., Estrin, D., Burke, J., Hansen, M.: Parsimonious mobility classification using GSM and WiFi traces. In: Proceedings of the Fifth Workshop on Embedded Networked Sensors, Charlottesville, Virginia, USA (2008)
NikeFuel: A universal way to measure movement, Beaverton, OR, NikeFuel App. https://secure-nikeplus.nike.com/plus/what_is_fuel
Pan, G., Qi, G., Wu, Z., Zhang, D., Li, S.: Land-use classification using taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 14(1), 113–123 (2013)
Perez, A.J., Labrador, M.A., Barbeau, S.J.: G-sense: a scalable architecture for global sensing and monitoring. IEEE Netw. 24(4), 57–64 (2010). https://doi.org/10.1109/MNET.2010.5510920
Santini, S., Jain, R.: Similarity is a geometer. Multimed. Tools Appl. 5(3), 277–306 (1997). https://doi.org/10.1023/A:1009651725256
Sohn, T., Varshavsky, A., LaMarca, A., Chen, M.Y., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W.G., Lara, E.: Mobility detection using everyday GSM traces. In: Proceedings of the 8th International Conference on Ubiquitous Computing, Orange County, CA, pp. 212–224 (2006)
Tryon, W.W., Tryon, G.S., Kazlausky, T., Gruen, W., Swanson, J.M.: Reducing hyperactivity with a feedback actigraph: initial findings. Clin. Child Psychol. Psychiatry 11(4), 607–617 (2006). https://doi.org/10.1177/1359104506067881
Weiss, G.M., Lockhart, J.W.: The impact of personalization on smartphone-based activity recognition. In: Proceedings of the AAAI 2012 Workshop on Activity Context, Toronto, CA, pp. 98–104 (2012)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)
Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.-Y.: Understanding mobility based on GPS data. In: Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea, pp. 312–321 (2008)
Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China, pp. 89–98 (2011)
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Supported by the project TIN2017-86885-R (AEI/FEDER, UE) granted by the Ministry of Economy, Industry and Competitiveness of Spain (including ERDF support).
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Cadenas, J.M., Garrido, M.C., Villa, C. (2018). Towards an App Based on FIWARE Architecture and Data Mining with Imperfect Data. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-319-91476-3_7
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