T-patterns revisited: Mining for temporal patterns in sensor data

Sensors

Volumn 10, Issue 8, 2010, Pages 7496-7513

  Salah, Albert Ali ^a   Pauwels, Eric ^b   Tavenard, Romain ^c   Gevers, Theo ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

^b CWI   (Netherlands)

^c UNIVERSITÉ DE RENNES 1   (France)

Author keywords

Gaussian mixture model; Lempel Ziv; MERL motion data; Sensor networks; T patterns; Temporal pattern extraction

Indexed keywords

GAUSSIAN MIXTURE MODEL; LEMPEL-ZIV; MOTION DATA; T-PATTERNS; TEMPORAL PATTERN;

BEHAVIORAL RESEARCH; INFRARED DETECTORS; SENSOR NETWORKS;

ALGORITHMS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER PROGRAM; DATA MINING; METHODOLOGY; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; DATA MINING; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SOFTWARE;

EID: 77957220418     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s100807496     Document Type: Article

References (39)

1. Magnusson, M.S. Discovering hidden time patterns in behavior: T-patterns and their detection. Behav. Res. Methods Instrum. Comput. 2000, 32, 93-110.
2. Tavenard, R.; Salah, A.A.; Pauwels, E.J. Searching for Temporal Patterns in AmI Sensor Data. In Constructing Ambient Intelligence: Proceedings of AmI-07 Workshops, Mühlhäuser, M., Ferscha, A., Aitenbichler, E. Eds.; Springer Verlag: Berlin, Germany, 2007; Volume CCIS 11, pp. 53-62.
3. Wren, C.; Ivanov, Y.; Leigh, D.; Westhues, J. The MERL Motion Dataset: 2007 Workshop on Massive Datasets. Technical report TR2007-069 2007.
4. Tan, P.; Steinbach, M.; Kumar, V.; Potter, C.; Klooster, S.; Torregrosa, A. Finding spatio-temporal patterns in earth science data. In Proceedings of the KDD Workshop on Temporal Data Mining, San Francisco, CA, USA, August 2001.
5. Klemettinen, M.; Mannila, H.; Toivonen, H. Rule Discovery in Telecommunication Alarm Data. J. Netw. Syst. Manag. 1999, 7, 395-423.
6. Kordic, S.; Lam, P.; Xiao, J.; Li, H.; Australia, W. Analysis of Alarm Sequences in a Chemical Plant. In Proceedings of the 4th International Conference on Advanced Data Mining and Applications, LNCS 5139, Springer: Chengdu, China, October 2008; pp. 135-146.
7. Casari, P.; Castellani, A.; Cenedese, A.; Lora, C.; Rossi, M.; Schenato, L.; Zorzi, M. The Wireless Sensor Networks for City-Wide Ambient Intelligence (WISE-WAI) Project. Sensors 2009, 9,4056-4082.
8. Tseng, V.; Lin, K. Energy efficient strategies for object tracking in sensor networks: A data mining approach. J. Syst. Softw. 2007, 80, 1678-1698.
9. Tseng, V.; Lu, E. Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns. J. Syst. Softw. 2009, 82, 697-706.
10. Caris-Verhallen, W.; Timmermans, L.; van Dulmen, S. Observation of nurse-patient interaction in oncology: Review of assessment instruments. Patient Educ. Couns. 2004, 54, 307-320.
11. Ermes, M.; Parkka, J.; Mantyjarvi, J.; Korhonen, I. Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Tran. Inf. Technol. Biomed. 2008,12,20-26.
12. Honda, S.; Fukui, K.; Moriyama, K.; Kurihara, S.; Numao, M. Extracting human behaviors with infrared sensor network. In Proceedings of the 4th International Conference on Networked Sensing Systems, Braunschweig, Germany, June 2007; pp. 122-125.
13. Ivanov, Y.; Bobick, A. Recognition of visual activities and interactions by stochastic parsing. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 852-872.
14. Micheloni, C.; Snidaro, L.; Foresti, G. Exploiting temporal statistics for events analysis and understanding. Im. Vis. Comp. 2009, 27, 1459-1469.
15. van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B. Accurate activity recognition in a home setting. In Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, South Korea, September 2008; pp. 1-9.
16. Wilson, D.; Atkeson, C. Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In Proceedings of the International Conference on Pervasive Computing, Springer: Kauai Island, HI, USA, March 2005; pp. 62-79.
17. Artikis, A.; Paliouras, G. Behaviour Recognition using the Event Calculus. In Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications and Innovations, Springer: Thessaloniki, Greece, April 2009; pp. 469-478.
18. Fox, D.; Hightower, J.; Liao, L.; Schulz, D.; Borriello, G. Bayesian filtering for location estimation. IEEE Pervasive Comput. 2003, 24-33.
19. Dietterich, T.; Michalski, R. Discovering patterns in sequences of events. Artif. Intell.1985, 25,117-246.
20. Elman, J. Finding structure in time. Cogn. Sci. 1990, 14, 179-211.
21. Rao, S.; Cook, D. Predicting inhabitant action using action and task models with application to smart homes. Int. J. Artif. Intell. Tools. 2004, 13, 81-99.
22. Bobick, A.; Wilson, A. A State-based approach to the representation and recognition of gesture. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19, 1325-1337.
23. Wada, T.; Matsuyama, T. Multi-object behavior recognition by event driven selective attention method. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 873-887.
24. Guralnik, V.; Srivastava, J. Event detection from time series data. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 1999; pp. 33-42.
25. Han, J.; Pei, J.; Yin, Y.; Mao, R. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Min. Knowl. Discov. 2004, 8, 53-87.
26. Last, M.; Klein, Y.; Kandel, A. Knowledge discovery in time series databases. IEEE Trans. Syst. Man Cybern. Part B 2001, 31, 160-169.
27. Mannila, H.; Toivonen, H.; Inkeri Verkamo, A. Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1997, 1, 259-289.
28. Parthasarathy, S.; Zaki, M.; Ogihara, M.; Dwarkadas, S. Incremental and Interactive Sequence Mining. In Proceedings of the 8th International Conference on Information and Knowledge Management, Kansas City, MO, USA, November 1999; pp. 251-258.
29. Pei, J.; Han, J.; Mortazavi-Asl, B.; Pinto, H.; Chen, Q.; Dayal, U.; Hsu, M. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In Proceedings of the IEEE International Conference on Data Engineering, Heidelberg, Germany, April 2001; pp. 215-224.
30. Pinto, H.; Han, J.; Pei, J.; Wang, K.; Chen, Q.; Dayal, U. Multi-dimensional Sequential Pattern Mining. In Proceedings of the 10th International Conference on Information and Knowledge Management, Atlanta, GA, USA, November 2001; pp. 81-88.
31. Casas-Garriga, G. Discovering unbounded episodes in sequential data.In Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 2003; pp. 83-94.
32. Cule, B.; Goethals, B.; Robardet, C. A New Constraint for Mining Sets in Sequences. In Proceedings of the SIAM International Conference on Data Mining, Sparks, NV, USA, April 2009; pp. 317-328.
33. Eagle, N.; Pentland, A. Eigenbehaviors: Identifying structure in routine. Behav. Ecol. Sociobiol. 2009,63,1057-1066.
34. Cook, D.J. Prediction Algorithms for Smart Environments. In Smart Environments: Technologies, Protocols, and Applications, Cook, D.J., Das, S.K., Eds.; Wiley Series on Parallel and Distributed Computing: Hoboken, NJ, USA, 2005; pp. 175-192.
35. Borrie, A.; Jonsson, G.; Magnusson, M. Temporal pattern analysis and its applicability in sport: An explanation and exemplar data. J. Sports Sci. 2002, 20, 845-852.
36. Duncan Jr, S.; Collier, N. C-quence: A tool for analyzing qualitative sequential data. Behav. Res. Methods Instrum. Comput. 2002, 34, 108-116.
37. Connolly, C.; Burns, J.; Bui, H. Recovering social networks from massive track datasets. SRI International Technical Note 564 2007.
38. Choset, H.; Burdick, J. Sensor-based exploration: The hierarchical generalized Voronoi graph. Int. J. Rob. Res. 2000, 19, 96-122.
39. Beeson, P.; Jong, N.; Kuipers, B. Towards autonomous topological place detection using the extended Voronoi graph. In Proceedings of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, April 2005; Vol. 4, pp. 4373-4379.