A Framework for Self-managing Database Support and Parallel Computing for Assistive Systems

There is no doubt that assistive systems are and will be a great part of our everyday lives. Thus, it is not suprising that in recent years researchers all over the world have been putting a lot of effort into their development. One of the most challenging problems usually is the handling of enormous amounts of data, which often has been collected by numerous sensors. This data is the basis of models, e.g. for prediction of movement, which has been derived by statistical methods, e.g. machine learning. However, due to the massive amounts of data, conventional statistical tools suffer from performance issues. In this paper, we would like to introduce and discuss a framework that combines the popular, statistical development tool R, database technology and the widely known MapReduce framework. Our main focus is placed on user-friendliness, meaning that the user does not have to change anything in his R-script, but still benefits from parallel computation and the in- and output power of databases.