A pro-active and dynamic prediction assistance using BaranC framework

dc.contributor.authorHashemi, Mohammad
dc.contributor.authorHerbert, John
dc.contributor.funderHigher Education Authorityen
dc.date.accessioned2016-11-17T13:28:41Z
dc.date.available2016-11-17T13:28:41Z
dc.date.issued2016
dc.description.abstractMonitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic; it is dynamic both in responding to the current context, and also in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.en
dc.description.sponsorshipHigher Education Authority (Telecommunications Graduate Initiative Program)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationHashemi, M. and Herbert, J. (2016) ‘A pro-active and dynamic prediction assistance using BaranC framework’, Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16), Austin, Texas, 14-22 May. New York, USA: ACM, pp. 269-270. doi: 10.1145/2897073.2897759en
dc.identifier.doi10.1145/2897073.2897759
dc.identifier.endpage270en
dc.identifier.isbn978-1-4503-4178-3
dc.identifier.startpage269en
dc.identifier.urihttps://hdl.handle.net/10468/3283
dc.language.isoenen
dc.publisherAssociation for Computing Machineryen
dc.relation.ispartofProceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16)
dc.relation.urihttp://mobilesoftconf.org/2016/
dc.rights© 2016, the Authors. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16) http://doi.acm.org/10.1145/2897073.2897759en
dc.subjectForecastingen
dc.subjectSoftware engineeringen
dc.subjectApp predictionsen
dc.subjectContext dataen
dc.subjectDynamic predictionen
dc.subjectLevel of satisfactionen
dc.subjectUser assistantsen
dc.subjectUser behaviouren
dc.subjectUser interactionen
dc.subjectUser modelingen
dc.titleA pro-active and dynamic prediction assistance using BaranC frameworken
dc.typeConference itemen
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