Abstract
Context mining algorithms from sensor data have been researched and successful results have been shown. However, since these existing works are focused on improving the accuracy of context mining, they are established on the assumption that they can acquire a complete set of necessary data. Therefore, the context mining algorithms do not work sufficiently since the data drops easily in the reality. In this paper, to cope with this problem, we propose a middleware named UDS (Uninterruptible Data Supply System). The system compensates the missing data, creates virtually complete dataset and provides upper layer applications. Applications operating over UDS can work sufficiently with some data actually missing. We have defined two types of characteristic data deficit patterns and created a robust model for both patterns utilizing Bayesian Network. In the evaluation, we show UDS can sustain the quality of context over 80% with 40% data missing.
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References
Bill, S., Norman, A., Roy, W.: Context-aware computing applications. In: Proceedings of the Workshop on Mobile Computing Systems and Applications, pp. 85–90. IEEE Computer Society, Los Alamitos
Hans, W.G., Michael, B., Holger, K.: The Media Cup: Awareness Technology Embedded in an Everyday Object. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 308–310. Springer, Heidelberg (1999)
Murao, K., Takegawa, Y., Terada, T., Nishio, S.: A Sensed Data Complementing Method Considering Breakdowns of Wearable Sensors for Wearable Computing Systems. In: DEWS 2007 (2007)
Aipperspach, R., Cohen, E., Canny, J.: Modeling human behavior from simple sensors in the home. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 337–348. Springer, Heidelberg (2006)
Intille, S.S., Larson, K., Tapia, E.M., Beaudin, J.S., Kaushik, P., Nawyn, J., Rockinson, R.: Using a live-in laboratory for ubiquitous computing research. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 349–365. Springer, Heidelberg (2006)
Lester, J., Choudhury, T., Borriello, G.: A practical approach to recognizing physical activities. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 1–16. Springer, Heidelberg (2006)
Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. In: CRC Pr I Llc (2003)
Tapia, E.M., Intille, S.S., Lopez, L., Larson, K.: The Design of a Portable Kit of Wireless Sensors for Naturalistic Data Collection. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 117–134. Springer, Heidelberg (2006)
Barkhuus, L., Dourish, P.: Everyday Encounters with Context-Aware Computing in a Campus Environment. In: Davies, N., Mynatt, E.D., Siio, I. (eds.) UbiComp 2004. LNCS, vol. 3205, pp. 232–249. Springer, Heidelberg (2004)
Cheverst, K., Davies, N., Mitchell, K., Friday, A., Efstratiou, C.: Developing a Context-Aware Electronic Tourist Guide: Some Issues and Experiences. In: Proceedings of MOBICOM 2000. ACM Press, New York (2000)
Burrell, J., Gay, G.K., Kubo, K., Farina, N.: Context-Aware Computing: A Test Case. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, p. 1. Springer, Heidelberg (2002)
Hori, T., Nishida, Y., Aizawa, H., Murakami, S., Mizoguchi, H.: Distributed Sensor Network for a Home for the Aged. In: 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 1577–1582 (2004)
Standord, V.: Using Pervasive Computing to Deliver Elder Care. IEEE Pervasive Computing 1(1), 10–13 (2002)
Hauptmann, A.G., Gao, J., Yan, R., Qi, Y., Yand, J., Watctlar, H.D.: Automated Analysys of Nursing Home Observations. IEEE Pervasive Computing 3(2), 15–21 (2004)
Sixsmith, A., Johnson, N.: A Smart Sensor to Detect the Falls of the Elderly. IEEE Pervasive Computing 3(2), 42–47 (2004)
Ito, M., Katagiri, Y., Ishikawa, M., Tokuda, H.: Airy Notes: An Experiment of Microclimate Monitoring in Shinjuku Gyoen Garden. In: INSS, Networked Sensing Systems (2007)
Iwai, M., Mori, M., Tokuda, H.: Live! Commerce System: A marketing WSN enabling analyzing customers’ attention in the real shops. In: INSS, Networked Sensing Systems (2008)
Sheikh, K., Wegdam, M., Sinderen, M.: Middleware Support for Quality of Context inn Pervasive Context-Aware System. In: Proceedings of Perware 2007 workshop (2007)
Korb, K., Nicholson, A.: Bayesian Artificial Intelligence. In: CRC Pr I Llc (2003)
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Namatame, N., Nakazawa, J., Takashio, K., Tokuda, H. (2009). UDS: Sustaining Quality of Context Using Uninterruptible Data Supply System. In: Rothermel, K., Fritsch, D., Blochinger, W., Dürr, F. (eds) Quality of Context. QuaCon 2009. Lecture Notes in Computer Science, vol 5786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04559-2_10
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DOI: https://doi.org/10.1007/978-3-642-04559-2_10
Publisher Name: Springer, Berlin, Heidelberg
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