The paper concerns mining data lacking the uniform structure. The
data are collected from a riumber of objects during repeated measurenients, all of which are tagged by a corresponding time. No attribute-valued machine learning algorithm can be applied directly on such data since the number of measurements is not fixed but it varies. The available data háve to be transformed and preprocessed in such a way that a uniform type of Information is obtained about all the considered objects. This can be achieved, e.g., by aggregation. But this process can introduce anachronistic variables, i.e., variables containing Information which cannot be available at the moment when a prediction is needed. The paper suggests and tests a method how to preprocess the considered type of data without falling into a trap of introducing anachronistic attributes. The method is illustrated on a čase study baaed on STULONG data.