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A pro-active and dynamic prediction assistance using BaranC framework

Published: 14 May 2016 Publication History

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.

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Cited By

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  • (2018)Association rules mining analysis of app usage based on mobile traffic flow data2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367651(55-60)Online publication date: Mar-2018

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  1. A pro-active and dynamic prediction assistance using BaranC framework

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    cover image ACM Conferences
    MOBILESoft '16: Proceedings of the International Conference on Mobile Software Engineering and Systems
    May 2016
    326 pages
    ISBN:9781450341783
    DOI:10.1145/2897073
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 14 May 2016

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    • (2018)Association rules mining analysis of app usage based on mobile traffic flow data2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA)10.1109/ICBDA.2018.8367651(55-60)Online publication date: Mar-2018

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