Nico Herzberg

An Event Processing Platform for Business Process Management

The execution of business processes generates a lot of data comprising final process results as well as information about intermediate activities, both communicated as events. Automated process execution environments are centrally controlled by process engines that hold the connection between events and the processes they occure in. In contrast, in manual process execution environments, e.g., logistics, these events may not be correlated to the process they origin from. The correlation information is usually not present in the event but in so-called context data, which exists orthogonally to the corresponding process. However, in the areas of process monitoring and analysis, events need to be correlated to specific process instances. To close the gap between recorded events without process correlation and required events with process correlation, we propose a framework that enriches recorded events with context data to create events correlated to processes, so-called process events.

Monitoring of Business Processes with Complex Event Processing

Business process monitoring enables a fast and specific overview of the process executions in an enterprise. Traditionally, this kind of monitoring requires a coherent event log. Yet, in reality, execution information is often heterogeneous and distributed. In this paper, we present an approach that enables monitoring of business processes with execution data, independently of the structure and source of the event information. We achieve this by implementing an open source event processing platform combining existing techniques from complex event processing and business process management. Event processing includes transformation for abstraction as well as correlation to process instances and BPMN elements. Monitoring rules are automatically created from BPMN models and executed by the platform.

Embedding conformance checking in a process intelligence system in hospital environments

Process intelligence is an effective means to analyze and improve business processes in companies with high degree of automation. Hospitals are also facing high pressure to be profitable with ever decreasing available funds in a stressed healthcare sector, which calls for methods to enable process management and intelligent methods in their daily work. However, traditional process intelligence systems work with logs of execution data that is generated by workflow engines controlling the execution of a process. But the nature of the treatment processes requires the doctors to work with a high freedom of action, rendering workflow engines unusable in this context. In this paper, we introduce a novel method to conformance checking that computes fitness of individual activities in the setting of sparse process execution information, i.e., not all activities of a patients treatment are logged. We embed this method into a process intelligence approach for hospitals without workflow engines, enabling process monitoring and analysis.

Flexible Batch Configuration in Business Processes Based on Events

Organizations use business process management techniques to manage their core business processes more efficiently. A recent technique is the synchronization of multiple process instances by processing a set of activities as a batch – referred to as batch regions, e.g., the shipment of goods of several order processes at once. During process execution, events occur providing information about state changes of (a) the business process environment and (b) the business process itself. Thus, these events may influence batch processing. In this paper, we investigate how these events influence batch processing to enable flexible and improved batch region execution. Therefore, we introduce the concept of batch adjustments that are defined by rules following the Event-Condition-Action principle. Based on batch adjustment rules, relevant events are correlated at run-time to batch executions that fulfill the defined condition and are adjusted accordingly. We evaluate the concept by a real-world use case.

Improving business process intelligence by observing object state transitions

During the execution of business processes several events happen that are recorded in the companys information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events.

Implementation Framework for Production Case Management: Modeling and Execution

Nowadays, business process modeling and system-supported executions have become a commodity in many companies. Most systems, however, focus on modeling and execution of static, pre-defined processes with standards like the Business Process Model and Notation (BPMN). While these static process executions are applicable to a number of traditional processes like purchase orderings or back orderings, they fail at representing variant-rich, flexible processes. One solution for supporting flexible processes is Adaptive Case Management (ACM), where a case manager creates an individual execution path for each process instance, such as a doctor defining a clinical pathway for a specific patient. We found out, however, that both approaches are too strict, either supporting static process definitions with only a limited set of pre-defined flexibility or allowing maximum flexibility but requiring a highly skilled knowledge worker. To overcome this problem, we propose an implementation framework for Production Case Management (PCM) that combines concepts from traditional process management and adaptive case management. PCM combines the modeling of small, static process fragments with the execution flexibility of ACM.

Modeling and Monitoring Variability in Hospital Treatments: A Scenario Using CMMN

Healthcare faces the challenge to deliver high treatment quality and patient satisfaction while being cost efficient which is tackled by introducing clinical pathways to standardize the treatment processes. At the Jena University Hospital, the clinical pathway for living donor liver transplantation was modeled using Business Process Model and Notation. A survey based on that model investigates on the transferability of this pathway to other hospitals and lists the requirements for a general model including the need for flexibility caused by differences in treatments in various hospitals. In this paper, we show an approach to tackle the requirements for such a flexible process by using the Case Management Model and Notation standard. Further, we show how case monitoring and analysis can be established by using an approach combining event processing and case management. The holistic approach is exemplified by using a scenario of the evaluation of living liver donors.

Model-Driven Event Query Generation for Business Process Monitoring

While executing business processes, a variety of events is produced that is valuable for getting insights about the process execution. Specifically, these events can be processed by Complex Event Processing(CEP) engines to deliver a base for business process monitoring. Mobile, flexible, and distributed business processes challenge existing process monitoring techniques, especially if process execution is partially done manually. Thus, it is not trivial to decide where the required business process execution information can be found, how this information can be extracted, and to which point in the process it belongs to. Tackling these challenges, we present a model-driven approach to support the automated creation of CEP queries for process monitoring. For this purpose, we decompose a process model that includes monitoring information into its structural components. Those are transformed to CEP queries to monitor business process execution based on events. For illustration, we show an implementation for Business Process Model and Notation(BPMN) and describe possible applications.

Enabling Dynamic Decision Making in Business Processes with DMN

While executing business processes, regularly decisions need to be made such as which activities to execute next or what kind of resource to assign to a task. Such a decision-making process is often case-dependent and carried out under uncertainty, yet requiring compliance with organization’s service level agreements. In this paper, we address these challenges by presenting an approach for dynamic decision-making. It is able to automatically propose case-dependent decisions during process execution. Finally, we evaluate it with a use case that highlights the improvements of process executions based on our dynamic decision-making approach.

Improving Business Process Intelligence with Object State Transition Events

During the execution of business processes several events happen that are recorded in the companys information system. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the runtime of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process analysis in terms of a large event log.