Automated preprocessing of environmental data

In this article we discuss automated preprocessing of environmental data for further use. Environmental data is by default heterogeneous, as it may consist of data from sources such as weather stations, weather radars, chemical sensors, acoustic sensors, and off-line laboratory analysis. When integrating data from such heterogeneous sources, it needs to be processed in a context dependent manner. In addition, there is no single generic processing method; rather, several atomic methods need to be applied and in an appropriate sequence. Furthermore, the problem is complicated by the requirements set by the intended use of the data. The requirements influence not only the set of applicable methods but also the application sequence. In this article, we study automation of the selection and sequencing of preprocessing methods based on the user requirements. As the main contribution, we propose here the use of characterizations and a reachability algorithm to solve the selection and sequencing problem. In this article, we present the algorithm and argue for its correctness. We also discuss, how the algorithm is implemented as a cloud service, and illustrate the use of the service with simple case studies