A Survey on Mobile Data Uses

Mobile devices leave an unprecedented volume and variety of digital traces of human beings. In this paper, the authors propose an overview of multiple uses of mobile data published in the scientific literature. The organization of the survey follows a typology built on two criteria: interaction level and focus of analysis. Crossing these two dimensions would suggest 8 research areas. Only 4 of them are actually covered by the collected pieces of work. They are discussed in turn showing off the main characteristics of them. Finally, the discussion of the 4 remaining areas highlights new research areas with a special focus on the possibility to use mobile data to influence individual users towards efficient collective behaviors. To conclude, current and future research avenues suggest that mobile devices and their underlying data are likely to be employed in many domains and may be used not only to observe human life but also to influence it.

[1]  Jeff Axup,et al.  Usability of a mobile, group communication prototype while rendezvousing , 2005, Proceedings of the 2005 International Symposium on Collaborative Technologies and Systems, 2005..

[2]  Sajal K. Das,et al.  Mobile social networking middleware: A survey , 2013, Pervasive Mob. Comput..

[3]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[4]  Alex Pentland,et al.  Social Sensors for Automatic Data Collection , 2008, AMCIS.

[5]  Christos Faloutsos,et al.  Mobile call graphs: beyond power-law and lognormal distributions , 2008, KDD.

[6]  Jihoon Kim,et al.  Concept lattices for visualizing and generating user profiles for context-aware service recommendations , 2009, Expert Syst. Appl..

[7]  Anna Monreale,et al.  WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.

[8]  Martin Raubal,et al.  Correlating mobile phone usage and travel behavior - A case study of Harbin, China , 2012, Comput. Environ. Urban Syst..

[9]  Jorng-Tzong Horng,et al.  Personal paging area design based on mobile's moving behaviors , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[10]  Nitya Narasimhan,et al.  Using location for personalized POI recommendations in mobile environments , 2006, International Symposium on Applications and the Internet (SAINT'06).

[11]  Hong-Yuan Mark Liao,et al.  Personalized travel recommendation by mining people attributes from community-contributed photos , 2011, ACM Multimedia.

[12]  Jari Saramäki,et al.  Small But Slow World: How Network Topology and Burstiness Slow Down Spreading , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Vincent S. Tseng,et al.  Efficient mining and prediction of user behavior patterns in mobile web systems , 2006, Inf. Softw. Technol..

[14]  Alex Pentland,et al.  Predicting Spending Behavior Using Socio-mobile Features , 2013, 2013 International Conference on Social Computing.

[15]  Wang-Chien Lee,et al.  Mining user similarity from semantic trajectories , 2010, LBSN '10.

[16]  Toon De Pessemier,et al.  A Hybrid Strategy for Privacy-Preserving Recommendations for Mobile Shopping , 2014, CBRecSys@RecSys.

[17]  Jae-Gil Lee,et al.  MoveMine: mining moving object databases , 2010, SIGMOD Conference.

[18]  Dino Pedreschi,et al.  Mobility data mining: discovering movement patterns from trajectory data , 2010, IWCTS '10.

[19]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[20]  Lionel M. Ni,et al.  SEER: Metropolitan-Scale Traffic Perception Based on Lossy Sensory Data , 2009, IEEE INFOCOM 2009.

[21]  Alex Pentland,et al.  Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives , 2011, HBU.

[22]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[23]  Alex Pentland,et al.  Situation fencing: making geo-fencing personal and dynamic , 2013, PDM '13.

[24]  Daniele Quercia,et al.  Recommending Social Events from Mobile Phone Location Data , 2010, 2010 IEEE International Conference on Data Mining.

[25]  Philip S. Yu,et al.  Music Recommendation Using Content and Context Information Mining , 2010, IEEE Intelligent Systems.

[26]  Katayoun Farrahi,et al.  A probabilistic approach to socio-geographic reality mining , 2011, ACMMR.

[27]  Daniel Gatica-Perez,et al.  A probabilistic approach to mining mobile phone data sequences , 2013, Personal and Ubiquitous Computing.

[28]  Yida Wang,et al.  Efficient mining of group patterns from user movement data , 2006, Data Knowl. Eng..

[29]  Ee-Peng Lim,et al.  Mining Mobile Group Patterns: A Trajectory-Based Approach , 2005, PAKDD.

[30]  Michael E. Theologou,et al.  Towards Anonymous Mobile Community services , 2009, J. Netw. Comput. Appl..

[31]  D. Lazer,et al.  Using reality mining to improve public health and medicine. , 2009, Studies in health technology and informatics.

[32]  Jason J. Jung.  Contextualized mobile recommendation service based on interactive social network discovered from mobile users , 2009, Expert Syst. Appl..

[33]  Ling Chen,et al.  A personal route prediction system based on trajectory data mining , 2011, Inf. Sci..

[34]  Wang-Chien Lee,et al.  A Framework for Personal Mobile Commerce Pattern Mining and Prediction , 2012, IEEE Transactions on Knowledge and Data Engineering.

[35]  Jianhua Ma,et al.  Survey on mobile social networking in proximity (MSNP): approaches, challenges and architecture , 2014, Wirel. Networks.

[36]  Ling Chen,et al.  A system for destination and future route prediction based on trajectory mining , 2010, Pervasive Mob. Comput..

[37]  Wang-Chien Lee,et al.  Semantic trajectory mining for location prediction , 2011, GIS.

[38]  Alex Pentland,et al.  Modeling Social Diffusion Phenomena using Reality Mining , 2009, AAAI Spring Symposium: Human Behavior Modeling.

[39]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[40]  Sung-Bae Cho,et al.  Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices , 2007, UIC.

[41]  Sridhar Balasubramanian,et al.  Mobile Marketing: A Synthesis and Prognosis , 2009 .

[42]  A. Persaud,et al.  Innovative mobile marketing via smartphones , 2012 .

[43]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[44]  Alladi Venkatesh,et al.  Mobile Marketing in the Retailing Environment: Current Insights and Future Research Avenues , 2010 .

[45]  Alex Pentland,et al.  Capturing Individual and Group Behavior with Wearable Sensors , 2009, AAAI Spring Symposium: Human Behavior Modeling.

[46]  David Taniar,et al.  On Mining Movement Pattern from Mobile Users , 2007, Int. J. Distributed Sens. Networks.

[47]  Jalal Kawash,et al.  Mobile virtual communities research: a synthesis of current trends and a look at future perspectives , 2007, Int. J. Web Based Communities.

[48]  Jan Marco Leimeister,et al.  Towards m-communities: the case of COSMOS healthcare , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[49]  Yao-Jen Chang,et al.  A General Architecture of Mobile Social Network Services , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[50]  Masanori Sugimoto,et al.  An Outdoor Recommendation System based on User Location History , 2005, ubiPCMM.

[51]  Isabelle Linden,et al.  From Mobile Data Towards Better Customer Knowledge: Proposals for an Information Framework , 2015, ANT/SEIT.

[52]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[53]  Alex Pentl,et al.  Reality Mining of Mobile Communications: Toward A New Deal On Data , 2009 .

[54]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[55]  Michael Koch,et al.  Mobile Communities - Extending Online Communities into the Real World , 2002, Mobile and Collaborative Business.

[56]  Özgür Ulusoy,et al.  A data mining approach for location prediction in mobile environments , 2005, Data Knowl. Eng..

[57]  Meng Hu,et al.  TrajPattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects , 2006, EDBT.

[58]  Enhong Chen,et al.  An effective approach for mining mobile user habits , 2010, CIKM.

[59]  Katayoun Farrahi,et al.  Probabilistic Mining of Socio-Geographic Routines From Mobile Phone Data , 2010, IEEE Journal of Selected Topics in Signal Processing.

[60]  Francesco Ricci,et al.  Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System , 2007, IEEE Intelligent Systems.

[61]  Daniel Gatica-Perez,et al.  What did you do today?: discovering daily routines from large-scale mobile data , 2008, ACM Multimedia.

[62]  Emilio Frazzoli,et al.  A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.

[63]  Hui Xiong,et al.  An energy-efficient mobile recommender system , 2010, KDD.

[64]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[65]  David Taniar,et al.  Mining Frequency Pattern from Mobile Users , 2004, KES.

[66]  Danaë Emma Beckford Stanton Fraser,et al.  Designing mobile technologies to support co-present collaboration , 2003, Personal and Ubiquitous Computing.

[67]  Abdelkader Adla.  Modeling Cooperative Decision Support Systems with Hybrid Agents , 2013, Int. J. Decis. Support Syst. Technol..

[68]  Vincent D. Blondel,et al.  Estimating Food Consumption and Poverty Indices with Mobile Phone Data , 2014, ArXiv.

[69]  Hui Zang,et al.  Mining call and mobility data to improve paging efficiency in cellular networks , 2007, MobiCom '07.

[70]  Pramod Sharma,et al.  A classification of U-commerce location based tourism applications: Centre for hospitality and tourism research - Victoria University , 2005, WWW 2005.

[71]  Torben Bach Pedersen,et al.  Privacy-Preserving Data Mining on Moving Object Trajectories , 2007, 2007 International Conference on Mobile Data Management.

[72]  Dietmar Jannach,et al.  Preface to the special issue on context-aware recommender systems , 2013, User Modeling and User-Adapted Interaction.

[73]  Soe-Tsyr Yuan,et al.  A recommendation mechanism for contextualized mobile advertising , 2003, Expert Syst. Appl..

[74]  Alex Pentland,et al.  Sensible Organizations: Changing Our Businesses and Work Styles through Sensor Data , 2008, J. Inf. Process..

[75]  Shuk Ying Ho,et al.  The attraction of personalized service for users in mobile commerce: an empirical study , 2002, SECO.

[76]  Alex Pentland,et al.  Predicting Personality Using Novel Mobile Phone-Based Metrics , 2013, SBP.

[77]  David Taniar,et al.  Mobile Data Mining by Location Dependencies , 2004, IDEAL.

[78]  Marta C. González,et al.  The path most travelled: Mining road usage patterns from massive call data , 2014, ArXiv.

[79]  N. Eagle,et al.  Network Diversity and Economic Development , 2010, Science.

[80]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[81]  Xing Xie,et al.  Mining interesting locations and travel sequences from GPS trajectories , 2009, WWW '09.

[82]  Vania Bogorny,et al.  A clustering-based approach for discovering interesting places in trajectories , 2008, SAC '08.

[83]  Nina D. Ziv,et al.  An Exploration on Mobile Social Networking: Dodgeball as a Case in Point , 2006, 2006 International Conference on Mobile Business.

[84]  Taly Sharon,et al.  Usage patterns of FriendZone: mobile location-based community services , 2004, MUM '04.

[85]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[86]  Dino Pedreschi,et al.  Mobility, Data Mining and Privacy - Geographic Knowledge Discovery , 2008, Mobility, Data Mining and Privacy.