A pro-active and dynamic prediction assistance using BaranC framework

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dc.contributor.author Hashemi, Mohammad
dc.contributor.author Herbert, John
dc.date.accessioned 2016-11-17T13:28:41Z
dc.date.available 2016-11-17T13:28:41Z
dc.date.issued 2016
dc.identifier.citation Hashemi, 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: 10.1145/2897073.2897759 en
dc.identifier.startpage 269 en
dc.identifier.endpage 270 en
dc.identifier.isbn 978-1-4503-4178-3
dc.identifier.uri http://hdl.handle.net/10468/3283
dc.identifier.doi 10.1145/2897073.2897759
dc.description.abstract Monitoring 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.sponsorship Higher Education Authority (Telecommunications Graduate Initiative Program) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Association for Computing Machinery en
dc.relation.ispartof Proceedings of the International Conference on Mobile Software Engineering and Systems (MOBILESoft '16)
dc.relation.uri http://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/10.1145/2897073.2897759 en
dc.subject Forecasting en
dc.subject Software engineering en
dc.subject App predictions en
dc.subject Context data en
dc.subject Dynamic prediction en
dc.subject Level of satisfaction en
dc.subject User assistants en
dc.subject User behaviour en
dc.subject User interaction en
dc.subject User modeling en
dc.title A pro-active and dynamic prediction assistance using BaranC framework en
dc.type Conference item en
dc.internal.authorcontactother John Herbert, Computer Science, University College Cork, Cork, Ireland. T: +353-21-490-3000 E: j.herbert@cs.ucc.ie en
dc.internal.availability Full text available en
dc.description.version Accepted Version en
dc.contributor.funder Higher Education Authority en
dc.description.status Peer reviewed en
dc.internal.IRISemailaddress j.herbert@cs.ucc.ie


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