Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative processmodels gained plenty of traction lately as they have proven to provide a good fit for the problem ...
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Flexible systems and services require a solid approach for modeling and enacting dynamic behavior. Declarative processmodels gained plenty of traction lately as they have proven to provide a good fit for the problem at hand, i.e. visualizing and executing flexible business processes. These models are based on constraints that impose behavioral restrictions on process behavior. Essentially, a declarative model is a set of constraints defined over the set of activities in a process. While allowing for very flexible process specifications, a major downside is that the combination of constraints can lead to behavioral restrictions not explicitly visible when reading a model. These restrictions, so-called hidden dependencies, make the models much more difficult to understand. This paper presents a technique for discovering hidden dependencies and making them explicit by means of dependency structures. Experiments with novice process modelers demonstrate that the proposed technique lowers the cognitive effort necessary to comprehend a constraint-basedprocess model. (C) 2018 Elsevier Ltd. All rights reserved.
process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predict...
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process-aware information systems (PAISs) are increasingly used to provide flexible support for business processes. The support given through a PAIS is greatly enhanced when it is able to provide accurate time predictions which is typically a very challenging task. Predictions should be (1) multi-dimensional and (2) not based on a single process instance. Furthermore, the prediction system should be able to (3) adapt to changing circumstances and (4) deal with multi-perspective declarative languages (e.g., models which consider time, resource, data and control flow perspectives). In this work, a novel approach for generating time predictions considering the aforementioned characteristics is proposed. For this, first, a multi-perspective constraint-based language is used to model the scenario. Thereafter, an optimized enactment plan (representing a potential execution alternative) is generated from such a model considering the current execution state of the process instances. Finally, predictions are performed by evaluating a desired function over this enactment plan. To evaluate the applicability of our approach in practical settings we apply it to a real process scenario. Despite the high complexity of the considered problems, results indicate that our approach produces a satisfactory number of good predictions in a reasonable time.
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