The "Human Sensor: " Bridging Between Human Data and Services

Data from ‘human sensors’ is increasingly easy to collect. Yet how may systems be designed that put it to use? This chapter discusses this question in three steps. First, we describe how the increasing ubiquity of digital systems is facilitating the creation of streams of human data. We characterise these data sources according to their purpose, obtrusiveness, structure, and hierarchy. Then, we address the kinds of systems that are already reaping the benefits of these data sources; they are broadly categorised as recommendation, retrieval, and behaviour-mediating systems. Finally, we describe a case study of potential systems that may be built to support urban travellers by leveraging the data that travellers themselves create while navigating their city. The chapter concludes with three open research challenges, related to understanding the context of data creation, the systems that are designed to use this data, and how to best architect a bridge between the two.

[1]  Jodi Forlizzi,et al.  Know thyself: monitoring and reflecting on facets of one's life , 2010, CHI Extended Abstracts.

[2]  Filip Radlinski,et al.  Active exploration for learning rankings from clickthrough data , 2007, KDD '07.

[3]  Jimmy J. Lin,et al.  WTF: the who to follow service at Twitter , 2013, WWW.

[4]  Sune Lehmann,et al.  Understanding the Demographics of Twitter Users , 2011, ICWSM.

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

[6]  Victor Soto,et al.  Robust Land Use Characterization of Urban Landscapes using Cell Phone Data , 2011 .

[7]  Cecilia Mascolo,et al.  Where Online Friends Meet: Social Communities in Location-Based Networks , 2012, ICWSM.

[8]  Ramesh Govindan,et al.  Medusa: a programming framework for crowd-sensing applications , 2012, MobiSys '12.

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

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

[11]  Josep Blat,et al.  Digital Footprinting: Uncovering Tourists with User-Generated Content , 2008, IEEE Pervasive Computing.

[12]  B. J. Fogg,et al.  Persuasive technology: using computers to change what we think and do , 2002, UBIQ.

[13]  Cecilia Mascolo,et al.  METIS: Exploring mobile phone sensing offloading for efficiently supporting social sensing applications , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[14]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.

[15]  Alon Y. Halevy,et al.  Crowdsourcing systems on the World-Wide Web , 2011, Commun. ACM.

[16]  Licia Capra,et al.  Pervasive and Mobile Computing ( ) – Pervasive and Mobile Computing Individuals among Commuters: Building Personalised Transport Information Services from Fare Collection Systems , 2022 .

[17]  Alan Borning,et al.  OneBusAway: results from providing real-time arrival information for public transit , 2010, CHI.

[18]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[19]  Xavier Amatriain,et al.  Mining large streams of user data for personalized recommendations , 2013, SKDD.

[20]  D. Boyd,et al.  Six Provocations for Big Data , 2011 .

[21]  Daniele Quercia,et al.  The Hidden Image of the City: Sensing Community Well-Being from Urban Mobility , 2012, Pervasive.

[22]  James A. Landay,et al.  UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits , 2009, CHI.

[23]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[24]  Cecilia Mascolo,et al.  Collecting big datasets of human activity one checkin at a time , 2012, HotPlanet '12.

[25]  Daniele Quercia,et al.  Tracking "gross community happiness" from tweets , 2012, CSCW.

[26]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[27]  Tefko Saracevic,et al.  RELEVANCE: A review of and a framework for the thinking on the notion in information science , 1997, J. Am. Soc. Inf. Sci..

[28]  Andrew Hogue,et al.  Learning to rank for spatiotemporal search , 2013, WSDM.

[29]  Deborah Estrin,et al.  Using mobile phones to determine transportation modes , 2010, TOSN.

[30]  Ryen W. White,et al.  Web-scale pharmacovigilance: listening to signals from the crowd , 2013, J. Am. Medical Informatics Assoc..

[31]  Licia Capra,et al.  Temporal diversity in recommender systems , 2010, SIGIR.

[32]  M. Hansen,et al.  Participatory Sensing , 2019, Internet of Things.

[33]  Daniele Quercia,et al.  The Social World of Twitter: Topics, Geography, and Emotions , 2012, ICWSM.

[34]  Deepak Ganesan,et al.  mCrowd: a platform for mobile crowdsourcing , 2009, SenSys '09.

[35]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[36]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[37]  Licia Capra,et al.  Mining mobility data to minimise travellers' spending on public transport , 2011, KDD.

[38]  Nuria Oliver,et al.  Sensing and predicting the pulse of the city through shared bicycling , 2009, IJCAI 2009.

[39]  Licia Capra,et al.  Mining Public Transport Usage for Personalised Intelligent Transport Systems , 2010, 2010 IEEE International Conference on Data Mining.

[40]  Lisa Amini,et al.  Challenges and results in city-scale sensing , 2011, 2011 IEEE SENSORS Proceedings.

[41]  George MacKerron,et al.  Happiness and environmental quality , 2012 .

[42]  Alejandro Bellogín,et al.  Time feature selection for identifying active household members , 2012, CIKM '12.

[43]  Sushil Jajodia,et al.  Who is tweeting on Twitter: human, bot, or cyborg? , 2010, ACSAC '10.

[44]  Mo Li,et al.  How Long to Wait? Predicting Bus Arrival Time With Mobile Phone Based Participatory Sensing , 2012, IEEE Transactions on Mobile Computing.

[45]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[46]  Soong Moon Kang,et al.  Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows , 2010, PloS one.

[47]  John Krumm,et al.  Learning Time-Based Presence Probabilities , 2011, Pervasive.

[48]  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.

[49]  Yvonne Rogers,et al.  How to nudge in Situ: designing lambent devices to deliver salient information in supermarkets , 2011, UbiComp '11.

[50]  Licia Capra,et al.  How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives , 2011, UbiComp '11.

[51]  Ling Bao,et al.  A context-aware experience sampling tool , 2003, CHI Extended Abstracts.

[52]  Predrag V. Klasnja,et al.  Mind the theoretical gap: interpreting, using, and developing behavioral theory in HCI research , 2013, CHI.

[53]  S. Strogatz,et al.  Redrawing the Map of Great Britain from a Network of Human Interactions , 2010, PloS one.

[54]  David W. McDonald,et al.  Theory-driven design strategies for technologies that support behavior change in everyday life , 2009, CHI.

[55]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[56]  Mirco Musolesi,et al.  Urban sensing systems: opportunistic or participatory? , 2008, HotMobile '08.

[57]  John Krumm,et al.  Route Prediction from Trip Observations , 2008 .

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

[59]  Phil Blythe,et al.  Understanding behaviour through smartcard data analysis , 2007 .

[60]  J. Ayers,et al.  Seasonality in seeking mental health information on Google. , 2013, American journal of preventive medicine.

[61]  Xing Xie,et al.  Where to find my next passenger , 2011, UbiComp '11.