Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Chapter 1. Introduction
Summary of Contributions
Related Work
Overview of Our Work
Data
Some Interesting Results Are the Following
Lda-Based User Analysis
Finding Variations in Individual Lives Over Time
Atm-Based Analysis
Lda-Based Analysis
Data and Model Parameters
Exploratory Analysis
Overview
Methodology
Vocabulary Analysis
Data Representation
Experiments and Results
Dataset
Dataset Characteristics
Sensors and Data
Political Opinion Questionnaires
Results
Conclusion
Limitations
Future Work
References

A Probabilistic Approach to Socio-Geographic Reality Mining

Profile image of Katayoun FarrahiKatayoun Farrahi

2011, ACM SIGMultimedia Records

Abstract

As we live our daily lives, our surroundings know about it. Our surroundings consist of people, but also our electronic devices. Our mobile phones, for example, continuously sense our movements and interactions. This socio-geographic data could be continuously captured by hundreds of millions of people around the world and promises to reveal important behavioral clues about humans in a manner never before possible. Mining patterns of human behavior from large-scale mobile phone data has deep potential impact ...

Related papers

Reality mining in urban space
Symeon Papadopoulos, Spiros Nikolopoulos

The widespread adoption of smartphones and other sensor-capable devices and the availability of infrastructure capable of storing and sharing sensor information have been the main enablers for the intriguing and ambitious concept of Reality Mining. Reality mining is concerned with the collection and analysis of sensed data pertaining to different aspects of human social behavior, allowing to observe the societal dynamics and predict what individuals will do next, model the behavior of large organizations, detect trends, spot emerging phenomena and events, mine public opinion, etc. The concept of reality mining is even more intriguing when applied in the context of urban space, since user-generated content can be analyzed to reveal the citizens' perspective on various aspects of the city reality. In this paper, after elaborating on the concept of reality mining in urban space, we present three cases where user-generated content is employed to discover the citizens' perspective about: a) the city attractions, b) the city issues/problems, and c) major events in the city.

View PDFchevron_right
Extracting Human Behavioral Patterns by Mining Geo-Social Networks
Farid Karimipour

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Accessibility of positioning technologies such as GPS offer the opportunity to store one’s travel experience and publish it on the web. Using this feature in web-based social networks and considering location information shared by users as a bridge connecting the users’ network to location information layer leads to the formation of Geo-Social Networks. The availability of large amounts of geographical and social data on these networks provides rich sources of information that can be utilized for studying human behavior through data analysis in a spatial-temporal-social context. This paper attempts to investigate the behavior of around 1150 users of Foursquare network by making use of their check-ins. The authors analyzed the metadata associated with the whereabouts of the users, with an emphasis on the type of places, to uncover patterns across different temporal and geographical scales for venue category usage. The authors found five groups of meaningful patterns that can explore ...

View PDFchevron_right
Human activity spatio-temporal indicators using mobile phone data
rodolfo metulini

Human activity spatio-temporal indicators using mobile phone data, 2019

In the context of Smart Cities, monitoring the dynamic of the presence of people is a crucial aspect for the well-being of an urban area. We use mobile phone data as a proxy for the total number of people (Carpita & Simonetto 2014), with the specific aim of computing spatio-temporal region specific indicators. Telecom Italia Mobile (TIM), which is the largest operator in Italy, thanks to a research agreement with the Statistical Office of the Municipality of Brescia, provided to us about two years (April 2014 to June 2016) of High-Frequency Daily Mobile Phone Density Profiles (DMPDPs) in the form of a regular grid polygon each 15 minutes. Densities have to be rescaled in order to express the total amount of people rather than just TIM users. Separately for selected regions in the province of Brescia, characterized by being either working or residential areas, we group similar DMPDPs and we characterize groups by their spatial and temporal components. In doing so, we propose a mixed-approach procedure.

View PDFchevron_right
Social Data Mining through Distributed Mobile Sensing
john gekas

2013

In this article, we present a distributed framework for collecting and analyzing environmental and location data recorded by human users (carriers) with the use of portable sensors. We demonstrate the data mining analysis potential among the recorded environmental and location variables, as well as the potential for classification analysis of human activities. We recognize that the success of such an experimental framework relies on the adoption rate by its candidate user network; thus, we have built our experimental prototype on top of hardware equipment already embedded within the potential users' everyday routine - i.e. hardware sensors installed on modern mobile phones. Finally, we present preliminary analysis results on our collected data sample, as well as potential further work directions and proposed use case scenarios.

View PDFchevron_right
Using spatial-temporal signatures to infer human activities from personal trajectories on location-enabled mobile devices
Yingjie Hu
View PDFchevron_right
Towards rich mobile phone datasets: Lausanne data collection campaign
Olivier Dousse, Juha Laurila

2010

Mobile phones have recently been used to collect large-scale continuous data about human behavior. In a paradigm known as people centric sensing, users are not only the carriers of sensing devices, but also the sources and consumers of sensed events. This paper describes a data collection campaign wherein Nokia N95 phones are allocated to a heterogeneous sample of nearly 170 participants from Lausanne, a mid-tier city in Switzerland, to be used over a period of one year. The data collection software runs on the background of the phones in a non-intrusive manner, yielding data on modalities such as social interaction and spatial behavior. The main motivations for organizing a new campaign on top of the ones that have been successfully conducted in the past are the following: First, in comparison to the Reality Mining data, generated in 2004-2005, the present data set is expected to provide a richer means to study location attributes, in particular, because today's mobile phones are more powerful and equipped with more sensors. Second, we aim to recruit a heterogeneous set of participants, comprising family and leisure related social networks in addition to organizationally driven ones. This paper provides a methodological description of the project and shows the potential of the resulting data set in terms of illuminating multiple aspects of human behavior.

View PDFchevron_right
Mining Human Mobility in Location-Based Social Networks
Huiji Gao

Synthesis Lectures on Data Mining and Knowledge Discovery, 2015

e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. Potential topics include: data mining algorithms, innovative data mining applications, data mining systems, mining text, web and semi-structured data, high performance and parallel/distributed data mining, data mining standards, data mining and knowledge discovery framework and process, data mining foundations, mining data streams and sensor data, mining multi-media data, mining social networks and graph data, mining spatial and temporal data, pre-processing and post-processing in data mining, robust and scalable statistical methods, security, privacy, and adversarial data mining, visual data mining, visual analytics, and data visualization.

View PDFchevron_right
Inferring User Context from Spatio-Temporal Pattern Mining for Mobile Application Services
Daniel Pereira

Recent research on geographical data mining that focuses on user behavior is lacking some fundamental aspects; measurements rely on large quantities of geographic data and lack contextual information. This work introduces a novel knowledge discovery architecture that brings together machine learning techniques with readily available information from popular Location Social Networks, in order to enrich geographical locations with context and add meaning to user behavior. Results show that through analysis of context enriched data we are capable of inferring context for detected user points of interest and patterns, such as where the user lives, works and spends his free time, without a large quantity of information or prior knowledge of the user and his private data.

View PDFchevron_right
Mobility and sociocultural events in mobile phone data records
Martin Minnoni

AI Communications, 2015

The massive amounts of geolocation data collected from mobile phone records have sparked an ongoing effort to understand and predict the mobility patterns of human beings. In this work, we study the extent to which social phenomena are reflected in mobile phone data, providing some proof-of-principle examples. We illustrate in various ways how these events are reflected in the data, and show how information about the events can be used to improve predictability in a simple model for a mobile phone user's location. We further propose a method for the automatic detection of such events and discuss their relation to the social fabric as derived from mobile phone communications.

View PDFchevron_right
Geo-Social Co-location Mining
Matthias Renz

Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data, 2015

Modern technology to capture geo-spatial information produce a huge flood of geo-spatial and geo-spatio-temporal data as a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discovery new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very space check-in data, and thus very space trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.

View PDFchevron_right

References (109)

  1. Akoush, S. and Sameh, A. (2007). Mobile user movement prediction using bayesian learning for neural networks. In International Wireless Communications and Mobile Computing Conference (ACM IWCMC), pages 191-196, Honolulu, Hawaii, USA.
  2. Asuncion, A., Welling, M., Smyth, P., and Teh, Y. W. (2009). On smoothing and inference for topic models. In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI), Montreal, Canada.
  3. Bernard, H., Killworth, P., Kronenfeld, D., and Sailer, L. (1984). The problem of informant accuracy: The validity of retrospective data. Annual Reviews in Anthropology.
  4. Bimbot, F., Pieraccini, R., Levin, E., and Atal, B. (1995). Variable-length sequence modeling: multi- grams. IEEE Signal Processing Letters, 2(6), 111-113.
  5. Bishop, C. M. (2007). Pattern Recognition and Machine Learning (Information Science and Statis- tics). Springer, 1st ed. 2006. corr. 2nd printing edition.
  6. Blei, D. and Lafferty, J. (2006). Dynamic topic models. In Proc. of the 23rd International Conference on Machine Learning (ICML), Pittsburgh, Pennsylvania, USA.
  7. Blei, D. and Lafferty, J. (2007). A correlated topic model of science. Annals of Applied Statistics, 1(1), 17-35.
  8. Blei, D. and Lafferty, J. (2009). Topic models. In A. Srivastava and M. Sahami, editors, Text Mining: Theory and Applications. Taylor and Francis.
  9. Blei, D. and McAuliffe, J. (2007). Supervised topic models. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada.
  10. Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
  11. Blei, D. M., Griffiths, T. L., Jordan, M. I., and Tenenbaum, J. B. (2004). Hierarchical topic models and the nested chinese restaurant process. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada.
  12. Brewer, D. and Webster, C. (2000). Forgetting of friends and its effects on measuring friendship networks. Social Networks, 21(4), 361 -373.
  13. Candia, J., Gonzalez, M., Wang, P., Schoenharl, T., Madey, G., and Barabasi, A.-L. (2008). Un- covering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical , 41(22), 224015+.
  14. Choudhury, T. (2003). Sensing and Modeling Human Networks. Ph.D. thesis, M.I.T.
  15. Choudhury, T. and Basu, S. (2004). Modeling conversational dynamics as a mixed memory markov process. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada.
  16. Choudhury, T. and Pentland, A. (2003). Sensing and modeling human networks using the sociometer. In IEEE International Symposium on Wearable Computers (ISWC), pages 216-, Washington, USA.
  17. Choudhury, T., Philipose, M., Wyatt, D., and Lester, J. (2006). Towards activity databases: Using sensors and statistical models to summarize people's lives. In IEEE Data Eng. Bull , pages 49-58.
  18. Chronis, L., Madan, A., and Pentland, A. (2009). Socialcircuits: The art of using mobile phones for modeling personal interactions. In ICMI-MLMI Workshop on Multimodal Sensor-Based Systems and Mobile Phones for Social Computing, Cambridge, Massachusetts, USA.
  19. Church, K. and Gale, W. (1991). Concordances for parallel text. In Proc. of the 7th Annual Conf. of the UW Centre for the New OED and Text Research, pages 40-62.
  20. Davis, M., Van House, N., Towle, J., King, S., Ahern, S., Burgener, C., Perkel, D., Finn, M., Viswanathan, V., and Rothenberg, M. (2005). MMM2: mobile media metadata for media sharing. In Extended Abstracts on Human Factors in Computing Systems (CHI), pages 1335-1338, Portland, Oregan, USA.
  21. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the Amercian Society for Information Science, 41(6), 391-407.
  22. Duda, R., Hart, P., and Stork, D. (2000). Pattern Classification (2nd Ed.). Wiley-Interscience.
  23. Dunning, T. E. (1993). Accurate methods for the statistics of surprise and coincidence. Computa- tional Linguistics, 19(1), 61-74.
  24. Duong, T. V., Bui, H. H., Phung, D. Q., and Venkatesh, S. (2005). Activity recognition and abnor- mality detection with the switching hidden semi-markov model. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) -Volume 1 , pages 838-845, Washington, USA.
  25. Eagle, N. and Pentland, A. (2009). Eigenbehaviors: Identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7), 1057-1066.
  26. Eagle, N., Pentland, A., and Lazer, D. (2009). Inferring social network structure using mobile phone data. Proc. of the National Academy of Sciences (PNAS), 106(36), 15274-15278.
  27. Eagle, N., Macy, M., and Claxton, R. (2010). Network diversity and economic development. Science, 328(5981), 1029-1031.
  28. Erosheva, E., Fienberg, S., and Lafferty, J. (2004). Mixed-membership models of scientific publica- tions. Proc. of the National Academy of Sciences (PNAS USA), 101(Suppl 1), 5220-5227.
  29. Farrahi, K. and Gatica-Perez, D. (2008a). Daily routine classification from mobile phone data. In 5th Joint Workshop on Machine Learning and Multimodal Interaction (MLMI), Utrecht, Netherlands.
  30. Farrahi, K. and Gatica-Perez, D. (2008b). Discovering human routines from cell phone data with topic models. In IEEE International Symposium on Wearable Computers (ISWC), Pittsburgh, Penn- sylvania, USA.
  31. Farrahi, K. and Gatica-Perez, D. (2008c). What did you do today? Discovering daily routines from large-scale mobile data. In ACM Int. Conf. on Multimedia (ACM MM), Vancouver, Canada.
  32. Farrahi, K. and Gatica-Perez, D. (2009). Learning and predicting multimodal daily life patterns from cell phones. In ICMI-MLMI , Cambridge, Massachusetts USA.
  33. Farrahi, K. and Gatica-Perez, D. (2010a). Mining human location-routines using a multi-level topic model. In Socialcom Symposium on Social Intelligence and Networking (Socialcom SIN-10).
  34. Farrahi, K. and Gatica-Perez, D. (2010b). Probabilistic mining of socio-geographic routines from mobile phone data. IEEE Journal of Selected Topics in Signal Processing (J-STSP), 4(4), 746-755.
  35. Farrahi, K. and Gatica-Perez, D. (2011). Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology, Special Issue on Intelligent Systems for Activity Recognition, 2(1), 3:1-3:27.
  36. Fay, N., Garrod, S., and Carletta, J. (2000). Group discussion as interactive dialogue or as serial monologue: The influence of group size. Psychological Science, English, 481-486+.
  37. Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779-782.
  38. Griffiths, T. L. and Steyvers, M. (2004). Finding scientific topics. Proc. of the National Academy of Sciences (PNAS USA), 101 Suppl 1, 5228-5235.
  39. Guo, F., Hanneke, S., Fu, W., and Xing, E. P. (2007). Recovering temporally rewiring networks: A model-based approach. In Proc. of the International Conference on Machine Learning (ICML), Oregan, USA.
  40. Hamid, R., Maddi, S., Johnson, A., Bobick, A., Essa, I., and Isbell, C. (2005). Unsupervised activity discovery and characterization from event-streams. In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI), Edinburgh, Scotland.
  41. Hamid, R., Maddi, S., Bobick, A. F., and Essa, I. A. (2007). Structure from statistics -unsupervised activity analysis using suffix trees. In IEEE International Conference on Computer Vision (ICCV), pages 1-8, Rio de Janeiro, Brazil.
  42. Heinrich, G. (2008). Parameter estimation for text analysis,. Technical report, University of Leipzig.
  43. Hidalgo, C. A. and Rodriguez-Sickert, C. (2008). The dynamics of a mobile phone network. Physica A, 387, 3017-3024.
  44. Hightower, J., Consolvo, S., Lamarca, A., Smith, I., and Hughes, J. (2005). Learning and recognizing the places we go. In Ubiquitous Computing (UbiComp), pages 159-176, Tokyo, Japan.
  45. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI), pages 289-296, Stockholm, Sweden.
  46. Huckfeldt, R. and Sprague, J. (1991). Discussant Effects on Vote Choice: Intimacy, Structure and Interdependence. The Journal of Politics, 53, 122-158.
  47. Hunter, D. R. and Handcock, M. S. (2006). Inference in curved exponential family models for networks. Journal of Computational and Graphical Statistics, 15, 565-583.
  48. Huynh, T., Fritz, M., and Schiele, B. (2008). Discovery of activity patterns using topic models. In Ubiquitous computing (UbiComp), pages 10-19, Seoul, Korea.
  49. Intille, S. S., Larson, K., Tapia, E. M., Beaudin, J. S., Kaushik, P., Nawyn, J., and Rockinson, R. (2006). Using a live-in laboratory for ubiquitous computing research. In Pervasive Computing, pages 349-365.
  50. Jebara, T. (2008). http://www.sensenetworks.com. Sense Networks.
  51. Justeson, J. S. and Katz, S. M. (1995). Technical terminology: some linguistic properties and an algorithm for identification in text. Natural Language Engineering, 1.
  52. Kiukkonen, N., Blom, J., Dousse, O., Gatica-Perez, D., and Laurila, J. (2010). Towards rich mobile phone datasets: Lausanne data collection campaign. In Proc. ACM Int. Conf. on Pervasive Services (ICPS), Berlin, Germany.
  53. Krumm, J. and Horvitz, E. (2006). Predestination: Inferring destinations from partial trajectories. In Ubiquitous Computing (UbiComp), California, USA.
  54. Laerhoven, K. V., Borazio, M., Kilian, D., and Schiele, B. (2008). Sustained logging and discrim- ination of sleep postures with low-level, wrist-worn sensors. In IEEE International Symposium on Wearable Computers (ISWC), pages 69-76, Pittsburgh, Pennsylvania, USA.
  55. Larson, K. and Intille, S. (2002). http://architecture.mit.edu/house n/data/placelab/placelab.htm. Placelab website.
  56. Lazer, D., Rubineau, B., Katz, N., Chetkovich, C., and Neblo, M. A. (2008). Networks and political attitudes: Structure, influence, and co-evolution. Working paper series, Harvard University.
  57. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., and Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721-723.
  58. Letchner, J., Fox, D., and LaMarca, A. (2005). Large-scale localization from wireless signal strength. In National Conference on Artificial Intelligence (AAAI), pages 15-20, Pittsburgh, Pennsylvania, USA.
  59. Liao, L., Fox, D., and Kautz, H. (2006). Location-based activity recognition. In Advances in Neural Information Processing Systems (NIPS), pages 787-794, Vancouver, Canada.
  60. Loecher, M. and Jebara, T. (2009). CitySense: Multiscale space time clustering of gps points and trajectories. In Proc. of the Joint Statistical Meeting.
  61. Logan, B., Healey, J., Philipose, M., Tapia, E. M., and Intille, S. S. (2007). A long-term evaluation of sensing modalities for activity recognition. In Ubiquitous Computing (UbiComp), volume 4717 of Lecture Notes in Computer Science, pages 483-500. Springer.
  62. Lu, H., Pan, W., Lane, N. D., Choudhury, T., and Campbell, A. T. (2009). Soundsense: scalable sound sensing for people-centric applications on mobile phones. In Proc. of the 7th International Conference on Mobile Systems, Applications, and Services (MobiSys), pages 165-178, New York, NY, USA.
  63. Madan, A. and Pentland, A. (2006). Vibefones: Socially aware mobile phones. In IEEE International Symposium on Wearable Computers (ISWC), Montreux, Switzerland.
  64. Madan, A., Cebrián, M., Lazer, D., and Pentland, A. (2010a). Social sensing for epidemiological behavior change. In Ubiquitous Computing (UbiComp), pages 291-300, Copenhagen, Denmark.
  65. Madan, A., Moturu, S., Lazer, D., and Pentland, A. (2010b). Social sensing: Obesity, unhealthy eating and exercise in face-to-face networks. In Proc. of ACM Wireless Health, San Diego, USA.
  66. Madan, A., Farrahi, K., Gatica-Perez, D., and Pentland, A. (2011). Pervasive sensing to model political opinions in face-to-face networks. In Pervasive, San Francisco, USA.
  67. Mccallum, A., Corrada-emmanuel, A., and Wang, X. (2004). The author-recipient-topic model for topic and role discovery in social networks: Experiments with enron and academic email. Technical report, University of Massachusetts Amherst.
  68. McCowan, I., Gatica-Perez, D., Bengio, S., Lathoud, G., Barnard, M., and Zhang, D. (2005). Auto- matic analysis of multimodal group actions in meetings. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 27(3), 305-317.
  69. Miluzzo, E., Lane, N. D., Eisenman, S. B., and Campbell, A. T. (2007). Cenceme: injecting sensing presence into social networking applications. In Proc. of the 2nd European conference on Smart sensing and context, EuroSSC'07, pages 1-28, Berlin, Heidelberg. Springer-Verlag.
  70. Monay, F. and Gatica-Perez, D. (2007). Modeling semantic aspects for cross-media image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 29(10), 1802-1817.
  71. Nallapati, R. and Cohen, W. (2008). Link-plsa-lda: A new unsupervised model for topics and influence in blogs. In International AAAI Conference on Weblogs and Social Media (ICWSM), Seattle, Washington, USA.
  72. Niebles, J., Wang, H., and Fei-Fei, L. (2008). Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision (IJCV), 79, 299-318.
  73. Olguin, D., Waber, B., Kim, T., Mohan, A., Ara, K., and Pentland, A. (2009). Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 39(1), 43-55.
  74. Olguin Olguin, D., Gloor, P., and Pentland, A. (2009). Wearable sensors for pervasive healthcare management. PCT Healthcare.
  75. Otsason, V., Varshavsky, A., Lamarca, A., and de Lara, E. (2005). Accurate gsm indoor localization. In Ubiquitous Computing (UbiComp), pages 141-158. Springer Berlin / Heidelberg.
  76. Patel, S., Kientz, J., Hayes, G., Bhat, S., and Abowd, G. (2006). Farther than you may think: An empirical investigation of the proximity of users to their mobile phones. In Proc. of Ubicomp, pages 123-140, Orange County, California, USA.
  77. Patterson, D., Liao, L., Fox, D., and Kautz, H. (2003). Inferring high-level behavior from low-level sensors. In Ubiquitous Computing (UbiComp), pages 73-89, Seattle, USA.
  78. Pritchard, J. K., Stephens, M., and Donnelly, P. (2000). Inference of Population Structure Using Multilocus Genotype Data. Genetics, 155(2), 945-959.
  79. Quelhas, P., Monay, F., Odobez, J.-M., Gatica-Perez, D., and Tuytelaars, T. (2007). A thousand words in a scene. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 29(9), 1575-89.
  80. Reddy, S., Burke, J., Estrin, D., Hansen, M., and Strivastava, M. (2008). Using mobile phones to determine transportation mode. In IEEE International Symposium on Wearable Computers (ISWC), Pittsburgh, Pennsylvania, USA.
  81. Robins, G., Snijders, T., Wang, P., Handcock, M., and Pattison, P. (2007). Recent developments in exponential random graph (p*) models for social networks. Social Networks, 29(2), 192-215.
  82. Rosen-Zvi, M., Griffiths, T., Steyvers, M., and Smyth, P. (2004). The author-topic model for authors and documents. In Proc. of the Conference on Uncertainty in Artificial Intelligence (UAI), pages 487- 494, Arlington, USA.
  83. Smadja, F. (1993). Retrieving collocations from text: Xtract. Computational Linguistics, 19.
  84. Sohn, T., Varshavsky, A., Lamarca, A., Chen, M., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W., and de Lara, E. (2006). Mobility detection using everyday gsm traces. In Ubiquitous Computing (UbiComp), pages 212-224, California, USA.
  85. Steyvers, M., Smyth, P., Rosen-Zvi, M., and Griffiths, T. (2004). Probabilistic author-topic models for information discovery. In International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), pages 306-315, New York, USA.
  86. Tapia, E. M., Intille, S. S., and Larson, K. (2004). Activity recognition in the home setting using simple and ubiquitous sensors. In A. Ferscha and F. Mattern, editors, Pervasive Computing, volume 3001 of Lecture Notes in Computer Science, pages 158-175. Springer Berlin / Heidelberg.
  87. Tapia, E. M., Intille, S. S., Haskell, W., Larson, K., Wright, J., King, A., and Friedman, R. (2007). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart monitor. In IEEE International Symposium on Wearable Computers (ISWC), Boston, USA.
  88. Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. (2006). Hierarchical dirichlet processes. Journal of the American Statistical Association, 101(476), 1566-1581.
  89. Teh, Y. W., Newman, D., and Welling, M. (2007). A collapsed variational Bayesian inference algo- rithm for latent Dirichlet allocation. In Advances in Neural Information Processing Systems (NIPS), Vancouver, Canada.
  90. Wallach, H. (2006). Topic modeling: beyond bag-of-words. In Proc. of the International Conference on Machine Learning (ICML), Pittsburgh, Pennsylvania, USA.
  91. Wang, P., Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. (2009a). Understanding the spreading patterns of mobile phone viruses. Science, 324(5930), 1071-1076.
  92. Wang, X., Mohanty, N., and Mccallum, A. (2005). Group and topic discovery from relations and text. In International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD) Workshop on Link Discovery, Chicago, USA.
  93. Wang, X., McCallum, A., and Wei, X. (2007). Topical n-grams: Phrase and topic discovery, with an application to information retrieval. In IEEE International Conference on Data Mining (ICDM), pages 697-702, Washington, USA.
  94. Wang, X., Ma, X., and Grimson, W. E. L. (2009b). Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 31(3), 539-555.
  95. Wesolowski, A. and Eagle, N. (2010). Parameterizing the dynamics of slums. In AAAI Spring Symposium on Artificial Intelligence for Development (AI-D).
  96. Wojek, C., Nickel, K., and Stiefelhagen, R. (2006). Activity recognition and room-level tracking in an office environment. In IEEE International Conf. on Multisensor Fusion and Integration for Intelligent Systems, pages 25-30.
  97. Wren, C., Ivanov, Y., Kaur, I., Leigh, D., Westhues, J., Wren, C. R., Ivanov, Y. A., Kaur, I., Leigh, D., and Westhues, J. (2007). Socialmotion: Measuring the hidden social life of a building. In International Symposium on Location-and Context-Awareness (LoCA 2007), pages 85-102.
  98. Wyatt, D., Choudhury, T., and Kautz, H. (2007). Capturing spontaneous conversation and social dynamics: A privacy sensitive data collection effort. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, USA.
  99. Wyatt, D., Choudhury, T., and Bilmes, J. (2010). Discovering long range properties of social net- works with multi-valued time-inhomogeneous models. In National Conference on Artificial Intelligence (AAAI), Atlanta, Georgia, USA.
  100. Yano, T., Cohen, W. W., and Smith, N. A. (2009). Predicting response to political blog posts with topic models. In North American Chapter of the Association for Computational Linguistics -Human Language Technologies (NAACL HLT), pages 477-485. Curriculum Vitae [1] Probabilistic Mining of Socio-Geographic Routines from Mobile Phone Data, K. Farrahi and D. Gatica-Perez, IEEE Journal of Special Topics in Signal Processing, Vol. 4, No. 4, pp.746-755, Aug. 2010.
  101. Discovering Routines from Large-Scale Human Locations using Hierarchical Bayesian Mod- els, K. Farrahi and D. Gatica-Perez, ACM Transactions on Intelligent Systems and Tech- nology, Special Issue on Intelligent Systems for Activity Recognition, Vol. 2, No. 1, Jan. 2011. Publications -Conferences
  102. Pervasive Sensing to Model Political Opinions in Face-to-Face Networks, A. Madan, K. Farrahi, D. Gatica-Perez, and A. Pentland, Pervasive, San Francisco, USA, June 2011.
  103. Mining Human Location-Routines using a Multi-Level Topic Model, K. Farrahi and D. Gatica-Perez, Socialcom Symposium on Social Intelligence and Network- ing (Socialcom SIN-10), Minneapolis, USA, Aug. 2010.
  104. Learning and Predicting Multimodal Daily Life Patterns from Cell Phones, K. Farrahi and D. Gatica-Perez, ICMI-MLMI, Cambridge, USA, Nov. 2009. (Accepted for Oral Presentation as well as Doctoral Symposium poster) .
  105. Discovering Human Routines from Cell Phone Data with Topic Models, K. Farrahi and D. Gatica-Perez, 12th IEEE International Symposium on Wearable Com- puters (ISWC), Pittsburgh, USA, 2008. (Accepted for Oral Presentation)
  106. What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data, K. Farrahi and D. Gatica-Perez, ACM International Conference on Multimedia (ACM MM), Vancouver, Canada, 2008.
  107. Daily Routine Classification from Mobile Phone Data, K. Farrahi and D. Gatica-Perez, 5th Joint Workshop on Machine Learning and Multimodal Interaction (MLMI), Utrecht, Netherlands, 2008. (Accepted for Oral Presentation)
  108. Robust Video Transmission using Forward Error Correction over 3G WCDMA Networks, K. Farrahi and T.A. Gulliver, IEEE WoWCaS, Vancouver, Canada, May 2004. (Accepted for Oral Presentation)
  109. Projects April -June Internship, Massachusetts Institute of Technology (MIT). Visiting The Human Dynamics Lab led by Prof. Alex Pentland at the MIT Media Lab. Under- standing the underlying behaviors in human opinion change from Reality Mining data. 1999 -Robot Design and Manufacture, University of Toronto. Designed and built a successful computer-controlled pancake maker. Responsible for the mechani- cal component of the robot. Worked closely with the person in charge of the electrical component. 2001 -4th Year Undergraduate Thesis Project, University of Toronto. Implemented and analyzed a phase interpolator circuit for high-speed multiphase clock generation. Languages English (native), French (intermediate), Persian (advanced)

Related papers

Probabilistic mining of socio-geographic routines from mobile phone data
Katayoun Farrahi

Selected Topics in Signal …, 2010

There is relatively little work on the investigation of large-scale human data in terms of multimodality for human activity discovery. In this paper we suggest that human interaction data, or human proximity, obtained by mobile phone Bluetooth sensor data, can be integrated with human location data, obtained by mobile cell tower connections, to mine meaningful details about human activities from large and noisy datasets. We propose a model, called bag of multimodal behavior, that integrates the modeling of variations of location over multiple time-scales, and the modeling of interaction types from proximity. Our representation is simple yet robust to characterize real-life human behavior sensed from mobile phones, which are devices capable of capturing large-scale data known to be noisy and incomplete. We use an unsupervised approach, based on probabilistic topic models, to discover latent human activities in terms of the joint interaction and location behaviors of 97 individuals over the course of approximately a 10 month period using data from MIT's Reality Mining project. Some of the human activities discovered with our multimodal data representation include "going out from 7pm-midnight alone" and "working from 11am-5pm with 3-5 other people", further finding that this activity dominantly occurs on specific days of the week. Our methodology also finds dominant work patterns occurring on other days of the week. We further demonstrate the feasibility of the topic modeling framework to discover human routines to predict missing multimodal phone data on specific times of the day. ) 2

View PDFchevron_right
Mining Human Mobility Data to Discover Locations and Habits
Brais Cancela

Communications in Computer and Information Science, 2020

Many aspects of life are associated with places of human mobility patterns and nowadays we are facing an increase in the pervasiveness of mobile devices these individuals carry. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way. Therefore, detecting significant places and the frequency of movements between them is fundamental to understand human behavior. In this paper, we propose a method for discovering user habits without any a priori or external knowledge by introducing a densitybased clustering for spatio-temporal data to identify meaningful places and by applying a Gaussian Mixture Model (GMM) over the set of meaningful places to identify the representations of individual habits. To evaluate the proposed method we use two real-world datasets. One dataset contains high-density GPS data and the other one contains GSM mobile phone data in a coarse representation. The results show that the proposed method is suitable for this task as many unique habits were identified. This can be used for understanding users' behavior and to draw their characterizing profiles having a panorama of the mobility patterns from the data.

View PDFchevron_right
Discovering locations and habits from human mobility data
Thiago Andrade

Annals of Telecommunications, 2020

Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals' visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual's habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.

View PDFchevron_right
Activity-aware map: Identifying human daily activity pattern using mobile phone data
Teerayut Horanont

Human Behavior …, 2010

Being able to understand dynamics of human mobility is essential for urban planning and transportation management. Besides geographic space, in this paper, we characterize mobility in a profilebased space (activity-aware map) that describes most probable activity associated with a specific area of space. This, in turn, allows us to capture the individual daily activity pattern and analyze the correlations among different people's work area's profile. Based on a large mobile phone data of nearly one million records of the users in the central Metro-Boston area, we find a strong correlation in daily activity patterns within the group of people who share a common work area's profile. In addition, within the group itself, the similarity in activity patterns decreases as their work places become apart.

View PDFchevron_right
Social Event Detection in Massive Mobile Phone Data Using Probabilistic Location Inference
Frédéric Morlot

2011

The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved