As part of an evaluation process of user experience realism in a Virtual Reality (VR) system, we focus in this paper on one of the core characteristics of vision: the relationship between contrast and luminance. The experiment aims at validating in VR reaction time predictions given by Rea and Ouellette's model. The subjects have to distinguish, as fast as they can, a target object from an uniform background. Our results did not match the predictions of the model. Our subjects showed higher performance in performing the task than expected. At low level of contrast, our subjects could easily perceive a target they should not have been able to see at all. This is explained by the size of the visual field surrounding the target: at low level of visibility, the larger the surrounding, the easier perception the is. We conclude that the Rea and Ouellette's model could be applied in VR if a specific visual field size factor was added.

Big data management is often characterized by three Vs: Volume, Velocity and Variety. While traditional batch-oriented systems such as MapReduce are able to scale-out and process very large volumes of data in parallel, they also introduce some significant latency. In this paper, we focus on the second V (Velocity) of the Big Data triad; We present a case-study where we use a popular open-source stream processing engine (Storm) to perform real-time integration and trend detection on Twitter and Bitly streams. We describe our trend detection solution below and experimentally demonstrate that our architecture can effectively process data in real-time - even for high-velocity streams.