The problem of automated discovery of process models from event logs has been intensively researched in the past two decades. Despite a rich field of proposals, state-of-the-art automated process discovery methods suffer from two recurrent deficiencies when applied to real-life logs: (i) they produce large and spaghetti-like models; and (ii) they produce models that either poorly fit the event log (low fitness) or over-generalize it (low precision). Striking a tradeoff between these quality dimensions in a robust and scalable manner has proved elusive. This paper presents an automated process discovery method, namely Split Miner, which produces simple process models with low branching complexity and consistently high and balanced fitness and precision, while achieving considerably faster execution times than stateof-the-art methods, measured on a benchmark covering twelve real-life event logs. Split Miner combines a novel approach to filter the directly-follows graph induced by an event log, with an approach to identify combinations of split gateways that accurately capture the concurrency, conflict, and causal relations between neighbors in the directly-follows graph. Split Miner is also the first automated process discovery method that is guaranteed to produce deadlock-free process models with concurrency, while not being restricted to producing block-structured process models.
A business process is often modeled using some kind of a directed flow graph, which we call a workflow graph. The Refined Process Structure Tree (RPST) is a technique for workflow graph parsing, i.e., for discovering the structure of a workflow graph, which has various applications. In this paper, we provide two improvements to the RPST. First, we propose an alternative way to compute the RPST that is simpler than the one developed originally. In particular, the computation reduces to constructing the tree of the triconnected components of a workflow graph in the special case when every node has at most one incoming or at most one outgoing edge. Such graphs occur frequently in applications. Secondly, we extend the applicability of the RPST. Originally, the RPST was applicable only to graphs with a single source and single sink such that the completed version of the graph is biconnected. We lift both restrictions. Therefore, the RPST is then applicable to arbitrary directed graphs such that every node is on a path from some source to some sink. This includes graphs with multiple sources and/or sinks and disconnected graphs.
This paper addresses the problem of transforming a process model with an arbitrary topology into an equivalent well-structured process model. While this problem has received significant attention, there is still no full characterization of the class of unstructured process models that can be transformed into well-structured ones, nor an automated method to structure any process model that belongs to this class. This paper fills this gap in the context of acyclic process models. The paper defines a necessary and sufficient condition for an unstructured process model to have an equivalent structured model under fully concurrent bisimulation, as well as a complete structuring method.
Bibliografische Information der Deutschen NationalbibliothekAbstract. Business process modeling is a creative task carried out by humans. Business analysts capture process knowledge in models. Process models are decompositions of processes into well recognized business tasks and their structuring by means of control flow. As outcome of a creative practice, models can be composed from tasks of different abstraction levels, i.e., low level tasks with a short and centralized lifecycles and general activities spanning over company departments. In this paper we propose to utilize process model control flow structure for the purpose of generalization of low level tasks to tasks of higher abstraction level. We use SPQR-tree hierarchical process model decomposition for identification of process model components-control flow structures with a self-contained logic suitable for abstraction. The approach allows the highest granularity as compared to existing techniques.
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