Please use this identifier to cite or link to this item: https://doi.org/10.1145/2502081.2503836
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dc.titleSummary abstract for the 1st ACM international workshop on personal data meets distributed multimedia
dc.contributor.authorSingh, V.K.
dc.contributor.authorChua, T.-S.
dc.contributor.authorPentland, A.
dc.contributor.authorJain, R.
dc.date.accessioned2014-07-04T03:15:27Z
dc.date.available2014-07-04T03:15:27Z
dc.date.issued2013
dc.identifier.citationSingh, V.K.,Chua, T.-S.,Pentland, A.,Jain, R. (2013). Summary abstract for the 1st ACM international workshop on personal data meets distributed multimedia. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference : 1105-1106. ScholarBank@NUS Repository. <a href="https://doi.org/10.1145/2502081.2503836" target="_blank">https://doi.org/10.1145/2502081.2503836</a>
dc.identifier.isbn9781450324045
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78364
dc.description.abstractMultimedia data are now created at a macro, public scale as well as individual personal scale. While distributed multimedia streams (e.g. images, microblogs, and sensor readings) have recently been combined to understand multiple spatio-temporal phenomena like epidemic spreads, seasonal patterns, and political situations; personal data (via mobile sensors, quantified-self technologies) are now being used to identify user behavior, intent, affect, social connections, health, gaze, and interest level in real time. An effective combination of the two types of data can revolutionize multiple applications ranging from healthcare, to mobility, to product recommendation, to content delivery. Building systems at this intersection can lead to better orchestrated media systems that may also improve users' social, emotional and physical wellbeing. For example, users trapped in risky hurricane situations can receive personalized evacuation instructions based on their health, mobility parameters, and distance to nearest shelter. This workshop bring together researchers interested in exploring novel techniques that combine multiple streams at different scales (macro and micro) to understand and react to each user's needs. Copyright © 2013 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/2502081.2503836
dc.sourceScopus
dc.subjectDistributed multimedia
dc.subjectPersonal data
dc.subjectReality mining
dc.subjectSensor networks
dc.subjectSituation recognition
dc.subjectSocial networks
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/2502081.2503836
dc.description.sourcetitleMM 2013 - Proceedings of the 2013 ACM Multimedia Conference
dc.description.page1105-1106
dc.identifier.isiutNOT_IN_WOS
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