UnconstrainedMiner : efficient discovery of generalized declarative process models

Process discovery techniques derive a process model from observed behavior (e.g., event logs). In case of less structured processes, declarative models have notable advantages over procedural models. A declarative model consists of a set of temporal constraints over the activities in the event log. In this paper, we address three limitations of current discovery techniques: their unclear semantics of declarative constraints for business processes, their non-performative discovery of constraints, and their potential identification of vacuous constraints. We implemented our contributions as a declarative discovery algorithm for the Declare language. Our evaluations on a real-life event log indicate that it outperforms state of the art techniques by several orders of magnitude

Leveraging super-scalarity and parallelism to provide fast Declare mining without restrictions

UnconstrainedMiner is a tool for fast and accurate mining Declare constraints from models without imposing any assumptions about the model. Declare models impose constraints instead of explicitly stating event orders. Constraints can impose various choices and ordering of events; constraints typically have understandable names, but for details, refer to [5]. Current state-of-the-art mining tends to fail due to a computational explosion, and employ ¿ltering to reduce this. Our tool is not intended to provide user-readable models, but instead to provide all constraints satis¿ed by a model. This allows post-processing to weed out uninteresting constraints, potentially obtaining better resulting models than making ¿ltering beforehand out of necessity. Any post-processing (and complexity-reducing ¿ltering) possible with existing miners is also possible with the UnconstrainedMiner; our miner just allows more intelligent post-processing due to having more information available, such as interactive ¿ltering of models. In our demonstration, we show how the new miner can handle large event logs in short time, and how the resulting output can be imported into Excel for further processing. Our intended audience is researchers interested in Declare mining and users interested in abstract characterization of relationships between events. We explicitly do not target end-users who wish to see a Declare model for a particular log (but we are happy to demonstrate the miner on other concrete data)

Leveraging super-scalarity and parallelism to provide fast Declare mining without restrictions

UnconstrainedMiner is a tool for fast and accurate mining Declare constraints from models without imposing any assumptions about the model. Declare models impose constraints instead of explicitly stating event orders. Constraints can impose various choices and ordering of events; constraints typically have understandable names, but for details, refer to [5]. Current state-of-the-art mining tends to fail due to a computational explosion, and employ ¿ltering to reduce this. Our tool is not intended to provide user-readable models, but instead to provide all constraints satis¿ed by a model. This allows post-processing to weed out uninteresting constraints, potentially obtaining better resulting models than making ¿ltering beforehand out of necessity. Any post-processing (and complexity-reducing ¿ltering) possible with existing miners is also possible with the UnconstrainedMiner; our miner just allows more intelligent post-processing due to having more information available, such as interactive ¿ltering of models. In our demonstration, we show how the new miner can handle large event logs in short time, and how the resulting output can be imported into Excel for further processing. Our intended audience is researchers interested in Declare mining and users interested in abstract characterization of relationships between events. We explicitly do not target end-users who wish to see a Declare model for a particular log (but we are happy to demonstrate the miner on other concrete data)

On the discovery of declarative control flows for artful processes

Artful processes are those processes in which the experience, intuition, and knowledge of the actors are the key factors in determining the decision making. They are typically carried out by the "knowledge workers," such as professors, managers, and researchers. They are often scarcely formalized or completely unknown a priori. Throughout this article, we discuss how we addressed the challenge of discovering declarative control flows in the context of artful processes. To this extent, we devised and implemented a two-phase algorithm, named MINERful. The first phase builds a knowledge base, where statistical information extracted from logs is represented. During the second phase, queries are evaluated on that knowledge base, in order to infer the constraints that constitute the discovered process. After outlining the overall approach and offering insight on the adopted process modeling language, we describe in detail our discovery technique. Thereupon, we analyze its performances, both from a theoretical and an experimental perspective. A user-driven evaluation of the quality of results is also reported on the basis of a real case study. Finally, a study on the fitness of discovered models with respect to synthetic and real logs is presented