From event streams to process models and back: Challenges and opportunities
The domains of complex event processing (CEP) and business process management (BPM) have different origins but for many aspects draw on similar concepts. While specific combinations of BPM and CEP have attracted research attention, resulting in solutions to specific problems, we attempt to take a broad view at the opportunities and challenges involved. We first illustrate these by a detailed example from the logistics domain. We then propose a mapping of this area into four quadrants -two
... nts drawing from CEP to create or extend process models and two quadrants starting $ This paper is an outcome of discussions and collaborations that were initiated at the Dagstuhl seminar 16341 on "Integrating Process-Oriented and Event-Based Systems" from a process model to address how it can guide CEP. Existing literature is reviewed and specific challenges and opportunities are indicated for each of these quadrants. Based on this mapping, we identify challenges and opportunities that recur across quadrants and can be considered as the core issues of this combination. We suggest that addressing these issues in a generic manner would form a sound basis for future applications and advance this area significantly. Keywords: complex event processing, event-based systems, business processes, event-driven business process management and can be executed by a process engine  , also called Business Process Management System (BPMS). The data perspective defines the data elements, e.g., the order, delivery note, customer id, required for the execution of the process together with their scope, their visibility and how they are passed on between activities. The resource perspective clarifies who takes part in which activities, how work is assigned to resources, and other organizational issues such as authorization. Given a (business) process model, one can construct its traces, that is, the possible executions of one process instance or case from beginning to end or the (temporally ordered) sequence of all events in one log belonging to one case. Based on these notions, we can define formal criteria of correctness, most famously, soundness  . Such criteria describe acceptable behaviors in terms of general properties such as deadlock freedom, proper termination, or bounded resource consumption. Existing analysis techniques can verify the correctness of a process model with respect to such criteria  . During runtime, process monitoring is a mechanism for providing accurate information of the status of business process instances  . This information can be used to provide feedback to a customer  or to generate combined metrics such as the number of processes carried out per hour  . Typically, there exists a gap between process modeling and its execution: while the process model specifies correct process behavior, the enacted behavior may differ. Process mining  promotes the understanding of the actually observed process behavior. Process mining refers to a set of techniques that analyze (mostly historical) event logs in order to derive a model of the process that created these logs. Generally, one assumes that each event has a timestamp and belongs to one case (process instance). Process mining enables discovery of a process model from a log, measuring the conformance of a log to a model, enhancement of a process model based on logs, and predictions of process properties [17, 18] . The feasibility of these tasks depends on the quality of the data, that is, completeness and correctness of the observed events, and properties are given for each event. Often, the application of pre-processing techniques is required to improve the quality of a log, and to bridge the gap between the activities in a process model with events of a log. Complex event processing and event-based systems Complex event processing (CEP) is a core function of event-based systems (EBS). CEP provides means for applications to react to happenings in form of 5 Pre-print copy of the manuscript published in Information Systems identified by doi: https://doi.