Thesis Open Access

A model for contextual data sharing in smartphone applications

Pandit, Harshvardhan J.

Thesis supervisor(s)

O'Riordan, Adrian

The advent of smartphones as a computing device has resulted in a shift in focus towards the design and development of smartphone applications or apps, that allow the user to complete a wide range of tasks on their devices. The users depend on apps installed on their smartphones to access services such as emails, photos, music, browsing, messaging and telephony. However, the overall user experience is disjointed as users are required to use multiple apps to complete a task where each app requires the user to enter the same information as the apps cannot share the data contextually.

This thesis investigates how smartphone apps can perform contextual data sharing with an emphasis on practical integration into the existing platforms and app models. The identification of information and its associated context is necessary to create context definitions that allow different apps to identify the context of the shared data. An approach to model the Context Definitions using computer science concepts such as object-oriented data structures provides flexibility. A context datastore is defined to store and share contextual information between apps, which creates an independence between apps for acquiring information and provides compatibility with the existing security models on various platforms. The model allows apps to retrieve contextual data in a simple and efficient manner without interacting directly with the other apps.

This thesis explains the author's hypothesis of creating contextual services in apps based on the availability of contextual information on a smartphone device. An implementation of the model proving the hypothesis is presented on Android using native tools and technologies available on the platform. The demonstration aims to show the viability of the model through use cases, evaluations and performance metrics.

Finally, the author provides recommendation for developers in adoption of the model, and the efforts required to integrate the implementation into existing platforms and apps. Further research avenues are identified that define the future of research in this area.

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