Classification and prediction of whereabouts patterns from the Reality Mining dataset

Pervasive and Mobile Computing

Volumn 9, Issue 4, 2013, Pages 516-527

  Ferrari, Laura ^a   Mamei, Marco ^a  

^a UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

Author keywords

Human mobility; LDA topic extraction; Topic classification; Topic prediction; Whereabouts data

Indexed keywords

BEHAVIORAL RESEARCH; CLASSIFICATION (OF INFORMATION); FORECASTING; STATISTICS; UBIQUITOUS COMPUTING;

COMPUTING APPLICATIONS; CONTINUOUS DATA; HUMAN MOBILITY; LATENT DIRICHLET ALLOCATIONS; PREDICTION MECHANISMS; TOPIC CLASSIFICATION; TOPIC EXTRACTION; WHEREABOUTS DATA;

DATA MINING;

EID: 84878626813     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2012.04.002     Document Type: Article

References (27)

1. S. Patel, J. Kientz, G. Hayes, S. Bhat, G. Abowd, Farther than you may think: an empirical investigation of the proximity of users to their mobile phones, in: International Conference on Ubiquitous Computing, Orange County (CA), USA, 2006.
2. T. Choudhury, S. Consolvo, B. Harrison, J. Hightower, A. LaMarca, L. LeGrand, A. Rahimi, A. Rea, G. Bordello, B. Hemingway, P. Klasnja, K. Koscher, J. Landay, J. Lester, D. Wyatt, and D. Haehnel The mobile sensing platform: an embedded activity recognition system IEEE Pervasive Computing 7 2 2008 32 41
3. M. Massimi, K. Truong, D. Dearman, and G. Hayes Understanding recording technologies in everyday life IEEE Pervasive Computing 9 3 2010 64 71
4. T. Strang, C. Linnhoff-Popien, A context modeling survey, in: Workshop on Advanced Context Modelling, Reasoning and Management, Nottingham, UK, 2004.
5. N. Eagle, and A. Pentland Eigenbehaviors: identifying structure in routine Behavioral Ecology and Sociobiology 63 7 2009 1057 1066
6. K. Farrahi, and D. Gatica-Perez Probabilistic mining of socio-geographic routines from mobile phone data IEEE Journal of Special Topics in Signal Processing 4 4 2010 746 755
7. D. Blei, A. Ng, and M. Jordan Latent dirichlet allocation Journal of Machine Learning Research 3 1 2003 993 1022
8. J. Gemmell, G. Bell, and R. Lueder Mylifebits: a personal database for everything Communications of the ACM 49 1 2006 88 95
9. A. Williams, and P. Dourish Reimagining the city: the cultural dimensions of urban computing IEEE Computer 39 9 2006 38 43
10. R. Mayrhofer, H. Radi, and A. Ferscha Recognizing and predicting context by learning from user behavior, special issue on mobile multimedia Radiomatics: Journal of Communication Engineering 1 1 2004 30 42
11. A. Schmidt, P. Holleis, J. Hakkila, E. Rukzio, R. Atterer, Mobile phones as tool to increase communication and location awareness of users, in: Mobility, Bangkok, Thailand, 2006.
12. C. Bishop Pattern Recognition and Machine Learning 2006 Springer Verlag
13. F. Calabrese, J. Reades, and C. Ratti Eigenplaces: analysing cities using the space-time structure of the mobile phone network IEEE Pervasive Computing 9 1 2010 78 84
14. Q. Mei, X. Shen, C. Zhai, Automatic labeling of multinomial topic models, in: ACM International Conference on Knowledge Discovery and Data Mining, San Jose (CA), USA, 2007.
15. J. Kang, W. Welbourne, B. Stewart, G. Borriello, Extracting places from traces of locations, in: ACM International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, Philadelphia (PA), USA, 2004.
16. N. Bicocchi, G. Castelli, M. Mamei, A. Rosi, F. Zambonelli, Supporting location-aware services for mobile users with the whereabouts diary, in: International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications, Innsbruck, Austria, 2008.
17. Y. Zheng, L. Zhang, X. Xie, W.-Y. Ma, Mining interesting locations and travel sequences from gps trajectories for mobile users, in: International World Wide Web Conference, Madrid, Spain, 2009.
18. J. Hightower, S. Consolvo, A. LaMarca, I. Smith, J. Hughes, Learning and recognizing the places we go, in: International Conference on Ubiquitous Computing, Tokyo, Japan, 2005.
19. D. Kim, J. Hightower, R. Govindan, D. Estrin, Discovering semantically meaningful places from pervasive rf-beacons, in: International Conference on Ubiquitous Computing, Orlando (FL), USA, 2009.
20. T. Sohn, A. Varshavsky, A. LaMarca, M. Chen, T. Choudhury, I. Smith, S. Consolvo, J. Hightower, W. Griswold, E. de Lara, Mobility detection using everyday gsm traces, in: International Conference on Ubiquitous Computing, Orange County (CA), USA, 2006.
21. J. Froehlich, J. Krumm, Route prediction from trip observations, in: Intelligent Vehicle Initiative, Technology Advanced Controls and Navigation Systems, SAE World Congress and Exhibition, Detriot (MI), USA, 2008.
22. F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, Trajectory pattern mining, in: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose (CA), USA, 2007.
23. V.V.M. Sricharan A pragmatic analysis of user mobility patterns in macrocellular wireless networks Pervasive Mobile Computing Journal 4 5 2008 616 632
24. D. Choujaa, and N. Dulay Predicting human behaviour from selected mobile phone data points International Conference on Ubiquitous Computing 2010 Copenhagen Denmark
25. K. Farrahi, D. Gatica-Perez, Daily routine classification from mobile phone data, in: Workshop on Machine Learning and Multimodal Interaction, Utrechts, Netherlands, 2008.
26. L. Ferrari, M. Mamei, Discovering daily routines from google latitude with topic models, IEEE Workshop on Context Modeling and Reasoning, Seattle (WA), USA, 2011.
27. L. Ferrari, A. Rosi, M. Mamei, F. Zambonelli, Extracting urban patterns from location-based social networks, in: ACM SIGSPATIAL International Workshop on Location-based Social Networks, Chicago (IL), USA, 2011.