While various types of process notations are used in industry, formal models such as Petri nets and temporal logics are more suitable for analysis purposes. Driven by questions from the other two research lines (Process Mining and PAIS Technology), particular models (e.g., WF-nets, WF-nets with data and resources, and declarative models) are used to answer questions related to correctness and performance. The main techniques that are used are model checking, structural techniques (e.g. invariants), and simulation.
Process mining techniques are used to extract process-related information from event logs, e.g., to automatically discover models, check conformance, and augment existing models with additional insights extracted from some event log. The main difference with Process Modeling/Analysis is that event logs play a central role (rather than predefined process models). One of the main challenges is to significantly improve the state-of-the-art in process discovery, e.g., we want to be able to deal with less structured processes and huge data sets (“Big Data”).
PAISs are used to manage and execute operational processes involving people, applications, and/or information sources. Examples are WFM (Workflow Management), BPM (Business Process Management), and ERP (Enterprise Resource Planning) systems. Increasingly, these systems are driven by models (connection to Process Modeling/Analysis) and produce high-quality event logs (connection to Process Mining). We are interested in the artifacts used and produced by these systems (i.e., models and logs) as these are essential for testing the techniques developed in the two other research lines.
The connections between the three research lines are illustrated by the diagram. Process models can be used to describe and analyze processes, but may also be used to specify, configure, or implement information systems. The left-hand-side of diagram shows some examples of design-time analysis: validation (i.e., testing whether the process behaves as expected), verification (i.e., establishing the correctness of a process definition), and traditional (i.e., non-log based) performance analysis (e.g., using simulation to evaluate the ability to meet requirements with respect to throughput times, service levels, and resource utilization).
Traditionally, most of AIS’s research focused on design-time analysis. However, more and more information about (business) processes is recorded by information systems in the form of so-called “event logs”. IT systems are becoming more and more intertwined with these processes, resulting in an “explosion” of available data that can be used for analysis purposes. The goal of process mining is to extract process-related information from event logs, e.g., to automatically discover a process model by observing events recorded by some information system. However, process mining is not limited to discovery and also includes conformance checking (investigating whether reality conforms to a given model and vice versa) and extension (augmenting an existing model with additional insights extracted from some event log).