Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to ...the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. In this paper different similarity measures are presented and evaluated based on a pilot of an empirical study. In particular the different measures are compared with the study results. It turns out that the quality of the different measures differ significantly.
During the last years a new generation of process-aware information systems has emerged, which enables process model configurations at buildtime as well as process instance changes during runtime. ...Respective model adaptations result in a large number of model variants that are derived from the same process model, but slightly differ in structure. Generally, such model variants are expensive to configure and maintain. In this paper we address two scenarios for learning from process model adaptations and for discovering a reference model out of which the variants can be configured with minimum efforts. The first one is characterized by a reference process model and a collection of related process variants. The goal is to improve the original reference process model such that it fits better to the variant models. The second scenario comprises a collection of process variants, while the original reference model is unknown; i.e., the goal is to “merge” these variants into a new reference process model. We suggest two algorithms that are applicable in both scenarios, but have their pros and cons. We provide a systematic comparison of the two algorithms and further contrast them with conventional process mining techniques. Comparison results indicate good performance of our algorithms and also show that specific techniques are needed for learning from process configurations and adaptations. Finally, we provide results from a case study in automotive industry in which we successfully applied our algorithms.
Data provenance allows scientists to validate their model as well as to investigate the origin of an unexpected value. Furthermore, it can be used as a replication recipe for output data products. ...However, capturing provenance requires enormous effort by scientists in terms of time and training. First, they need to design the workflow of the scientific model, i.e., workflow provenance, which requires both time and training. However, in practice, scientists may not document any workflow provenance before the model execution due to the lack of time and training. Second, they need to capture provenance while the model is running, i.e., fine-grained data provenance. Explicit documentation of fine-grained provenance is not feasible because of the massive storage consumption by provenance data in the applications, including those from the geoscience domain where data are continuously arriving and are processed. In this paper, we propose an inference-based framework, which provides both workflow and fine-grained data provenance at a minimal cost in terms of time, training, and disk consumption. Our proposed framework is applicable to any given scientific model, and is capable of handling different model dynamics, such as variation in the processing time as well as input data products arrival pattern. Our evaluation of the framework in a real use case with geospatial data shows that the proposed framework is relevant and suitable for scientists in geoscientific domain.
For various applications there is the need to compare the similarity between two process models. For example, given the as-is and to-be models of a particular business process, we would like to know ...how much they differ from each other and how we can efficiently transform the as-is to the to-be model; or given a running process instance and its original process schema, we might be interested in the deviations between them (e.g. due to ad-hoc changes at instance level). Respective considerations can be useful, for example, to minimize the efforts for propagating the schema changes to other process instances as well. All these scenarios require a method to measure the similarity or distance between two process models based on the efforts for transforming the one into the other. In this paper, we provide an approach using digital logic to evaluate the distance and similarity between two process models based on high-level change operations (e.g. to add, delete or move activities). In this way, we can not only guarantee that model transformation results in a sound process model, but also ensure that related efforts are minimized.
Some physical objects are influenced by business workflows and are observed by sensors. Since both sensor infrastructures and business workflows must deal with imprecise information, the correlation ...of sensor data and business workflow data related to physical objects might be used a-posteriori to determine the source of the imprecision. In this paper, an information theory based approach is presented to distinguish sensor infrastructure errors from inhomogeneous business workflows. This approach can be applied on detecting imprecisions in the sensor infrastructure, like e.g. sensor errors or changes of the sensor infrastructure deployment.
Recently, a new generation of adaptive Process-Aware Information Systems (PAISs) has emerged, which enables structural process changes during runtime. Such flexibility, in turn, leads to a large ...number of process variants derived from the same model, but differing in structure. Generally, such variants are expensive to configure and maintain. This paper provides a heuristic search algorithm which fosters learning from past process changes by mining process variants. The algorithm discovers a reference model based on which the need for future process configuration and adaptation can be reduced. It additionally provides the flexibility to control the process evolution procedure, i.e., we can control to what degree the discovered reference model differs from the original one. As benefit, we cannot only control the effort for updating the reference model, but also gain the flexibility to perform only the most important adaptations of the current reference model. Our mining algorithm is implemented and evaluated by a simulation using more than 7000 process models. Simulation results indicate strong performance and scalability of our algorithm even when facing large-sized process models.
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