The two most prominent process mining tasks are process discovery (i.e., learning a process model from an event log) and conformance checking (i.e., diagnosing and quantifying differences between observed and modeled behavior). The increasing availability of event data makes these tasks highly relevant for process analysis and improvement. Therefore, process mining is considered to be one of the key technologies for Business Process Management (BPM). In recent years, we have applied process mining in over 100 organizations. However, as event logs and process models grow, process mining becomes more challenging. Therefore, we propose a fully generic approach to decompose process mining problems into smaller problems that can be analyzed more efficiently. As shown, process discovery and conformance checking can be done per process fragment and the results can be aggregated. This has advantages in terms of efficiency and diagnostics.

• eis-decomp-bigdata-passages.pdf

Process discovery (discovering a process model from example behavior recorded in an event log) is one of the most challenging tasks in process mining. Discovery approaches need to deal with competing quality criteria such as fitness, simplicity, precision, and generalization. Moreover, event logs may contain low frequent behavior and tend to be far from complete (i.e., typically only a fraction of the possible behavior is recorded). At the same time, models need to have formal semantics in order to reason about their quality. These complications explain why dozens of process discovery approaches have been proposed in recent years. Most of these approaches are time-consuming and/or produce poor quality models. In fact, simply checking the quality of a model is already computationally challenging.

This talk shows that process mining problems can be decomposed into a set of smaller problems after determining the so-called causal structure. Given a causal structure, we partition the activities over a collection of passages. Conformance checking and discovery can be done per passage. The decomposition of the process mining problems has two advantages. First of all, the problem can be distributed over a network of computers. Second, due to the exponential nature of most process mining algorithms, decomposition can significantly reduce computation time (even on a single computer). As a result, conformance checking and process discovery can be done much more efficiently.

• petri-nets-passages-2012-wvda.pdf

The amounts of data that (embedded) systems are recording are rapidly increasing. The explosion of data happens in a pace that is unprecedented and in our networked world of today the trend is even accelerating. Companies have transactional data with trillions of bytes of event data generated by systems in the field, production systems, customers and suppliers. Sensors in smart devices generate unparalleled amounts of sensor data. Social media sites and mobile phones have allowed billions of individuals globally to create their own enormous trails of data. Process mining is a relative young research discipline that sits between machine learning and data mining on the one hand and process modeling and analysis on the other. The challenge in process mining is to discover, monitor and improve real processes (i.e. not assumed processes) by extracting knowledge from event logs readily available in today’s systems. Event logs can be used to conduct three types of process mining. The first and most prominent is discovery. A discovery technique takes an event log and produces a model without using a priori information. The second type is conformance where an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. The third type is enhancement, where the idea is to extend or improve an existing process model using information about the actual process recorded in an event log. These techniques can be used to discover how systems are being used in the field. Analysis can be explorative (finding out what really happens without analysing a particular problem) or targeted at understanding and solving a particular problem (e.g. system failures). Process mining has been used to diagnose problems in a broad range of organisations.

• bc2012es-process-mining-wvda.pdf

• nba-norea-process-mining-vdaalst2012.pdf

• decomposed_process_mining.pptx

• overview-ais-2012.pdf

• coopis2012-genetic-mining-balancing-perspectives.pdf

• Event log used in workshop

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