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Long-life application

Situation detection in a context-aware all-in-one application

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Abstract

Nowadays, mobile devices host many applications that are directly downloaded and installed from mobile application stores. The existence of such a large amount of apps for a myriad of purposes imposes a huge overhead on users, who are in charge of selecting, installing, and executing the appropriate apps, as well as deleting them when no longer needed. Moreover, these applications have mostly neglected to take into account the user’s context, as they propose static non-evolving scenarios of use. The proposed long-life application provides a new way to respond to the user’s needs on the fly. It evolves at run time (by including/ excluding business functionalities, updating the interaction mode, and migrating executions) according to the user’s needs. While he/she moves in his/her surroundings, the app detects the occurring events and builds contextually-described situations. So, this work aims to offer a new type of mobile application able to detect, formulate and understand the users’ context and react accordingly. Therefore, in this paper, we present the overall approach to build a long-life application and we focus on context detection and formulation aspects.

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Acknowledgments

This work was supported by the the embassy of France in Spain and by the TIN2013-46238-C4-(1/4)-R, FEDER/ TIN2016-78011-C4-(2/3)-R (AEI/FEDER, UE), and DGA-FSE

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Authors and Affiliations

  1. LIUPPA, University of Pau and Adour Country, Pau, France

    Riadh Karchoud, Philippe Roose & Marc Dalmau

  2. Computer Science Faculty, University of the Basque Country, Paseo Manuel de Lardizabal 1, 20018, San Sebastián, Spain

    Arantza Illarramendi

  3. Department of Computer Science and Systems Engineering, University of Zaragoza, I3A, Maria de Luna, 1, E-50018, Zaragoza, Spain

    Sergio Ilarri

Authors
  1. Riadh Karchoud
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  2. Arantza Illarramendi
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  3. Sergio Ilarri
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  4. Philippe Roose
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  5. Marc Dalmau
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Corresponding author

Correspondence to Riadh Karchoud.

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Karchoud, R., Illarramendi, A., Ilarri, S. et al. Long-life application. Pers Ubiquit Comput 21, 1025–1037 (2017). https://doi.org/10.1007/s00779-017-1077-2

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  • DOI: https://doi.org/10.1007/s00779-017-1077-2

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