Modeling and Discovering Occupancy Patterns in Sensor Networks Using Latent Dirichlet Allocation - Foundations on Natural and Artificial Computation - page 488

a long-term dataset. In the future we would like to obtain most relevant results
for the building occupancy environment.
With the purpose of stimulate similar works using this dataset, we plan to pro-
vide the data and the developed vocabulary, to researchers interested in running
further analysis.
Acknowledgments
The first author would like to thank the 2010 UC3M grant: Ayudas de movilidad
del programa propio de investigacion and projects CICYT TIN2008-06742-C02-
02/TSI, CICYT TEC2008-06732-C02-02/TEC and CAM CONTEXTS 2009/
TIC-1485 for partially support this research.
The authors want to thank Erik Degroof and Luc Peeters of Innotek Belgium
for their cooperation and the use of their dataset.
References
1. Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. The Journal of Machine
Learning Research 3, 993-1022 (2003)
2. Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd
Annual International ACM SIGIR Conference on Research and Development in
Information Retrieval, pp. 50-57. ACM, New York (1999)
3. Griths, T., Steyvers, M.: Finding scientific topics. Proceedings of the National
Academy of Sciences of the United States of America 101(suppl. 1), 5228-5235
(2004)
4. Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories
using spatial-temporal words. International Journal of Computer Vision 79(3),
299-318 (2008)
5. Wren, C.R., Ivanov, Y.A., Leigh, D., Westhues, J.: The merl motion detector
dataset. In: Proceedings of the 2007 workshop on Massive datasets, Ser. MD 2007,
pp. 10-14. ACM, New York (2007),
http://doi.acm.org/10.1145/1352922.1352926
6. Connolly, C., Burns, J., Bui, H.: Recovering social networks from massive track
datasets. In: IEEE Workshop on Applications of Computer Vision, WACV 2008,
pp. 1-8. IEEE, Los Alamitos (2008)
7. Salah, A., Pauwels, E., Tavenard, R., Gevers, T.: T-Patterns Revisited: Mining for
Temporal Patterns in Sensor Data. Sensors 10(8), 7496-7513 (2010)
8. Magnusson, M.: Discovering hidden time patterns in behavior: T-patterns and their
detection. Behavior Research Methods Instruments and Computers 32(1), 93-110
(2000)
9. McKeever, S., Ye, J., Coyle, L., Bleakley, C., Dobson, S.: Activity recognition
using temporal evidence theory. Journal of Ambient Intelligence and Smart Envi-
ronments 2(3), 253-269 (2010)
10. Skogster, P., Uotila, V., Ojala, L.: From mornings to evenings: is there variation
in shopping behaviour between different hours of the day? International Journal of
Consumer Studies 32(1), 65-74 (2008)