Efficient Process Model Discovery Using Maximal Pattern Mining

In recent years, process mining has become one of the most important and promising areas of research in the field of business process management as it helps businesses understand, analyze, and improve their business processes. In particular, several proposed techniques and algorithms have been proposed to discover and construct process models from workflow execution logs (i.e., event logs). With the existing techniques, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces seen in the log. In this paper, we propose a novel technique for process discovery using Maximal Pattern Mining (MPM) where we construct patterns based on the whole sequence of events seen on the traces—ensuring the soundness of the mined models. Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Our evaluation shows that it consistently achieves better precision, replay fitness and efficiency than the existing techniques

Discovering block-structured process models from incomplete event logs

One of the main challenges in process mining is to discover a process model describing observed behaviour in the best possible manner. Since event logs only contain example behaviour and one cannot assume to have seen all possible process executions, process discovery techniques need to be able to handle incompleteness. In this paper, we study the effects of such incomplete logs on process discovery. We analyse the impact of incompleteness of logs on behavioural relations, which are abstractions often used by process discovery techniques. We introduce probabilistic behavioural relations that are less sensitive to incompleteness, and exploit these relations to provide a more robust process discovery algorithm. We prove this algorithm to be able to rediscover a model of the original system. Furthermore, we show in experiments that our approach even rediscovers models from incomplete event logs that are much smaller than required by other process discovery algorithms. © 2014 Springer International Publishing

Workflow Mining: Current Status and Future Directions

Current workflow management systems require the explicit design of the workflows that express the business process of an organization. This process design is very time consuming and error prone. Considerable work has been done to develop heuristics to mine event-data logs to produce a process model that can support the workflow design process. However, all the existing heuristic-based mining algorithms have their limitations. To achieve more insight into these limitations the starting point in this paper is the α-algorithm [3] for which it is proved under which conditions and process constructs the algorithm works. After presentation of the α-algorithm, a classification is given of the process constructs that are difficult to handle for this type of algorithms. Then, for some constructs (i.e. short loops) it is illustrated in which way the α-algorithm can be extended so that it can correctly discover these constructs