Discovering Characteristics of Stochastic Collections of Process Models

Abstract

Process models in organizational collections are typically created by the same team and using the same conventions. As such, these models share many characteristic features like size range, type and frequency of errors. In most cases merely small samples of these collections are available due to e.g. the sensitive information they contain. Because of their sizes, these samples may not provide an accurate representation of the characteristics of the originating collection. This paper deals with the problem of constructing collections of process models from small samples of a collection, with the purpose to estimate the characteristics of this collection. Given a small sample of process models drawn from a real-life collection, we mine a set of generation parameters that we use to generate arbitrarily-large collections that feature the same characteristics of the original collection. In this way we can estimate the characteristics of the original collection on the generated collections. We extensively evaluate the quality of our technique on various sample datasets drawn from both research and industry.