Large Language Models for Business Process Management: Opportunities and Challenges

Large language models are deep learning models with a large number of parameters. The models made noticeable progress on a large number of tasks, and as a consequence allowing them to serve as valuable and versatile tools for a diverse range of applications. Their capabilities also offer opportunities for business process management, however, these opportunities have not yet been systematically investigated. In this paper, we address this research problem by foregrounding various management tasks of the BPM lifecycle. We investigate six research directions highlighting problems that need to be addressed when using large language models, including usage guidelines for practitioners

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads

Towards a data-driven framework for measuring process performance

Studies have shown that the focus of Business Process Management (BPM) mainly lies on process discovery and process implementation & execution. In contrast, process analysis, i.e., the measurement of process performance, has been mostly neglected in the field of process science so far. However, in order to be viable in the long run, a process' performance has to be made evaluable. To enable this kind of analysis, the suggested approach in this idea paper builds upon the well-established notion of devil's quadrangle. The quadrangle depicts the process performance according to four dimensions (time, cost, quality and flexibility), thus allowing for a meaningful assessment of the process. In the course of this paper, a framework for the measurement of each dimension is proposed, based on the analysis of process execution data. A trailing example is provided that reflects the expressed concepts on a tangible realistic scenario

Towards a multi-parametric visualisation approach for business process analytics

Visualisation is an integral part of many scientific areas and is reportedly an important tool for learning and teaching. One reason for this is the picture superior effect. Nevertheless, little research endeavour has been carried out so far to effectively apply visualisation principles to the emerging field of business process analytics. In this paper a novel multi-parametric visualisation approach is proposed in such a context. General visualisation principles are used to create, evaluate, and improve the approach in the design process. They are drawn from a wide range of fields, and are synthesised from theory and empirical evidence