Relaxed context-aware machine learning midddleware (RCAMM) for Android

Abstract

Context Aware Computing is a promising approach of developing mobile applications that provide experiences and services in a manner that is fine-tuned based on the user's preferences. Applications such as Google Now, Apple Siri learn the User's activities from context related information and subsequently provide suggestions to the users in real-time. However, in almost all cases, application developers have to develop the same set of mechanisms to consume the context information and storing it in an appropriate form rather than focusing on the parts of the application that consume the context information. This approach results in the repetition of the same task and multiple copies of data. This paper presents our work detailing the development of a middleware that handles context information collection and its storage. The work provides a framework that allows the developers to easily implement context aware applications that consume the services provided by the middleware. Applications will only have to react to context data (past and present) while the middleware takes care of everything else such as the background service for context information collection and storage, thus reducing the redundancy, increasing adaptability and flexibility, and simultaneously supporting developers in rapid prototyping of context-aware applications. Thus the paper presents our work towards building sustainable Android Framework which follows the principle of Reformat, Reduce, Regenerate, Reuse and Repurpose.