Increasing Recall of Process Model Matching by Improved Activity Label Matching

Klinkmüller, Christopher; Weber, Ingo; Mendling, Jan; Leopold, Henrik; Ludwig, André

doi:10.1007/978-3-642-40176-3_17

Cited by 54 publications

(43 citation statements)

References 11 publications

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“…Similarities can thus be identified relatively easily, which increases the support of patterns. BP models, instead, are more flexible in their "components" (the tasks): task labels are free text, and identifying "types" of tasks is a hard problem in itself [7]. For instance, the tasks "Pay bill", "Pay" and "Send money" can all be seen as instances of a common task type "Payment."…”

Section: Discussion and Analogy With Bpmmentioning

confidence: 99%

Crowd-Based Mining of Reusable Process Model Patterns

Rodríguez

Daniel

Casati

2014

Lecture Notes in Computer Science

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Abstract. Process mining is a domain where computers undoubtedly outperform humans. It is a mathematically complex and computationally demanding problem, and event logs are at too low a level of abstraction to be intelligible in large scale to humans. We demonstrate that if instead the data to mine from are models (not logs), datasets are small (in the order of dozens rather than thousands or millions), and the knowledge to be discovered is complex (reusable model patterns), humans outperform computers. We design, implement, run, and test a crowd-based pattern mining approach and demonstrate its viability compared to automated mining. We specifically mine mashup model patterns (we use them to provide interactive recommendations inside a mashup tool) and explain the analogies with mining business process models. The problem is relevant in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices or organizational conventions, from which modelers can learn and benefit when designing own models.

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Section: Discussion and Analogy With Bpmmentioning

confidence: 99%

Crowd-Based Mining of Reusable Process Model Patterns

Rodríguez

Daniel

Casati

2014

Lecture Notes in Computer Science

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“…Process model techniques are more advanced when it comes to the usage of external knowledge. Especially in recent years, more sophisticated techniques that use linguistic information have evolved [71,66]. Other model-based techniques -as, e.g., in [106] -leverage different semantic information as, e.g., roles, resources or data objects, which can be seen as external knowledge in this area.…”

Section: Approaches Working On Process Modelsmentioning

confidence: 99%

Matching of events and activities

Baier

Solti

Mendling

et al. 2015

Proceedings of the 30th Annual ACM Symposium on Applied Computing

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I like things matching. I have an upright bass, a drum kit and a grand piano that's the same color.-Penn Jillette A B S T R A C T Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during process execution. Aiming at a better process understanding and improvement, this event data can be used to analyze processes using process mining techniques. Process models can be automatically discovered and the execution can be checked for conformance to specified behavior. Moreover, existing process models can be enhanced and annotated with valuable information, for example for performance analysis. While the maturity of process mining algorithms is increasing and more tools are entering the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Mapping the recorded events to activities of a given process model is essential for conformance checking, annotation and understanding of process discovery results. Current approaches try to abstract from events in an automated way that does not capture the required domain knowledge to fit business activities. Such techniques can be a good way to quickly reduce complexity in process discovery. Yet, they fail to enable techniques like conformance checking or model annotation, and potentially create misleading process discovery results by not using the known business terminology.In this thesis, we develop approaches that abstract an event log to the same level that is needed by the business. Typically, this abstraction level is defined by a given process model. Thus, the goal of this thesis is to match events from an event log to activities in a given process model. To accomplish this goal, behavioral and linguistic aspects of process models and event logs as well as domain knowledge captured in existing process documentation are taken into account to build semiautomatic matching approaches. The approaches establish a pre-processing for every available process mining technique that produces or annotates a process model, thereby reducing the manual effort for process analysts. While each of the presented approaches can be used in isolation, we also introduce a general framework for the integration of different matching approaches.The approaches have been evaluated in case studies with industry and using a large industry process model collection and simulated event logs. The evaluation demonstrates the effectiveness and efficiency of the approaches and their robustness towards nonconforming execution logs. • Thomas Baier, Andreas Rogge-Solti, Mathias Weske, and Jan Mendling. Matching of events and activities -an approach based on behavioral constraint satisfaction. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 1225-1230. ACM, 2015.• Thomas Baier, Claudio Di Ciccio, Jan Mendling, and Mathias Weske. Matching of events and activities -an approach using declarative modeling constraints. Other important roles i...

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“…The result is a measure of similarity between two process models. Klinkmuller, Weber, Mendling, Leopold, and Ludwig [112] present an alternative matching algorithm that applies more rigor to the label matching. They start with activity matching based on bag-of-word's similarity.…”

Section: Similarity Modeling and Redesignmentioning

confidence: 99%