Finding concurrency problems in software can be very hard because the problems may not appear until all of the components have been integrated together and just the right conditions occur. In production, these problems may even seem to be random. In software, these problems have been well researched, however research in Business Process Modeling is still relatively new, and the people who are experts on business processes may not be familiar with all the knowledge contained within the realm of software engineering.;In order to create these guidelines, a pattern analysis framework is described which amalgamates existing pattern research and re-categorizes it by smells instead of solutions as patterns are typically organized. It also synthesizes new information by revealing new relationships between different research sources.;This thesis adapts well known solutions to common data concurrency problems from software engineering, to guidelines business process modelers can use to detect where potential problems may hide and suggest possible solutions. In software engineering terms, the thesis takes the literature on data concurrency patterns and principles and combines it to form a catalog of process model smells (like symptoms) with references back to how the problems should be dealt with.
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