Abstract. Modern information systems allow us to track, often in great detail, the execution of processes within companies. Consider for example luggage handling in airports, manufacturing processes of products and goods, or processes related to service provision, all of these processes generate traces of valuable event data. Such event data are typically stored in a company’s information system and describe the execution of the process at hand. In recent years, the field of process mining has emerged. Process mining techniques aim to translate the data captured during the process execution, i.e. the event data, into actionable insights. As such, we identify three main process mining types of analysis, i.e. process discovery, conformance checking and process enhancement. In process discovery, we aim to discover a process model, i.e. a formal behavioral description, which describes the process as captured by the event data. In conformance checking, we aim to assess to what degree the event data is in correspondence with a given reference model, i.e. a model describing how the process ought to be executed. Finally, within process enhancement, the main goal is to improve the view of the process, i.e. by enhancing process models on the basis of facts derived from event data.
 
Recent developments in information technology allow us to capture data at increasing rates, yielding enormous volumes of data, both in terms of size and velocity. In the context of process mining, this relates to the advent of real-time, online, streams of events that result in data sets that are no longer efficiently analyzable by commodity hardware. Such types of data pose both opportunities and challenges. On the one hand, it allows us to get actionable insights into the process, at the moment it is being
executed. On the other hand, conventional process mining techniques do not allow us to gain these insights, as they are not designed to cope with such a new type of data. As a consequence, new methods, techniques and tools are needed to allow us to apply process mining techniques and analyses on streams of event data of arbitrary size.
 

In this presentation, we explore process mining techniques that are able to handle online, streaming event data. The premise of streaming event data, is the fact that we assume the stream of events under consideration to be of infinite size. As such, efficient techniques to temporarily store and use relevant recent subsets of event data are needed. In particular, we focus on a few basic examples of applying process discovery in an online setting. Furthermore, applications and recent breakthroughs in the area of online conformance checking will be discussed as well.

Speaker: Sebastiaan van Zelst, Scientist at Fraunhofer FIT and RWTH Aachen University