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Decision support systems (DSS) that rely on time-sensitive information are demanding on the integration of computational models. Scientific models are commonly developed and tested with offline data coming from files and databases, but in a real-time DSS models have to deal with low-latency data streams, transmission faults and other imperfections. In practice, models need to process data from multiple data streams and various formats and require mechanisms to deal with delayed, missing and out-of-order data. It is desirable to handle data adaption, fault tolerance and other bookkeeping in a robust framework and allow domain experts to implement computational models in a mathematical language such as R, MATLAB or Fortran. We present a platform that allows modellers to deploy R scripts and execute then in a distributed environment with online data. The platform is written in Java, dynamically sets up R …