Abstract : Scientists are confronted with significant data management problems due to the huge volume and high complexity of environmental data. An important aspect of environmental data management is that data, needed for a process, are not always in the adequate format. In this contribution, we analyze environmental data structure, and model this data using a semantic-based method. Using this model, we design and implement a framework based on Web services for transformation between massive environmental text-based data and relational databases. We present a mapping model for environmental data transformation to be used in the scenario devoted to the methodology for development of stochastic models for prediction of environmental parameters by application of Gaussian processes.
