Abstract
Context-awareness for big data applications is different from that of traditional applications in that it is getting challenging to obtain the contexts from big data due to the complexity, velocity, variety, and other aspects of big data, especially big video data. The awareness of contexts in big data is more difficult, and should be more in-depth than that of classical applications. Therefore, in this paper, we propose an in-depth context-awareness framework for a pervasive video cloud in order to obtain underlying contexts in big video data. In this framework, we propose an approach that combines the historical view with the current view to obtain meaningful in-depth contexts, where deep learning techniques are used to obtain raw context data. We have conducted initial evaluations to show the effectiveness of the proposed approach in terms of performance and also the accuracy of obtaining the contexts. The evaluation results show that the proposed approach is effective for real-time context-awareness in a pervasive video cloud.
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