A case in case-based reasoning is a contextualized piece of experience, which can be represented in various forms. Traditional approaches can be classified into three main categories: feature vector representations , structured representations , and textual representations . More sophisticated approaches make use of hierarchical representations or generalized cases. For particular tasks such as design and planning highly specific representations have been developed.

Monitoring the state of currently running processes and reacting to ad-hoc situations during runtime is a key challenge in Business Process Management (BPM). This is especially the case in cyber-physical environments that are characterized by high context sensitivity. MAPE-K control loops are widely used for self-management in these environments and describe four phases for approaching this challenge: Monitor, Analyze, Plan, and Execute. In this paper, we present an architectural solution as well as implementation proposals for using MAPE-K control loops for adaptive workflow management in smart factories. We use Complex Event Processing (CEP) techniques and the process execution states of a Workflow Management System (WfMS) in the monitoring phase. In addition, we apply automated planning techniques to resolve detected exceptional situations and to continue process execution. The experimental evaluation with a physical smart factory shows the potential of the developed approach that is able to detect failures by using IoT sensor data and to resolve them autonomously in near real time with considerable results.

Collaborative, long-term processes with changing requirements occur in several domains. In design and manufacturing, we observed the following particular characteristics: the process models can be huge and contain longsome decisions while the time to market is tight. Furthermore, different types of products have to be considered. In this paper, we present an approach of new, flexible workflow technology that provides a solution for this: The ongoing workflows can be adapted by means of late-planning and ad-hoc changes. A suspension mechanism allows the workflow designers to modify parts of a workflow while the remainder of the workflow can continue to be executed. A case-based reasoning approach supports the reuse of past experience for this.

The Internet of Things (IoT) has been shown to be very valuable for Business Process Management (BPM), for example, to better track and control process executions. While IoT actuators can automatically trigger actions, IoT sensors can monitor the changes in the environment and the humans involved in the processes. These sensors produce large amounts of discrete and continuous data streams, which hold the key to understanding the quality of the executed processes. However, to enable this understanding, it is needed to have a joint representation of the data generated by the process engine executing the process, and the data generated by the IoT sensors. In this paper, we present an extension of the event log standard format XES called DataStream. DataStream enables the connection of IoT data to process events, preserving the full context required for data analysis, even when scenarios or hardware artifacts are rapidly changing. The DataStream extension is designed based on a set of goals and evaluated by creating two datasets for real-world scenarios from the transportation/logistics and manufacturing domains.

We briefly examine case-based planning starting with the seminal work of Hammond. Derivational analogy represents an important shift of technical emphasis that helped mature the techniques. The choice of abstraction level is equally important. We conclude by discussing theoretical underpinnings and by providing some pointers to current directions.