Process mining as the superglue between data science and enterprise computing

Process mining provides new ways to utilize the abundance of data in enterprises. Suddenly many organizations realize that survival is not possible without exploiting available data intelligently. A new profession is emerging: the data scientist. Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available today. Process mining will be an integral part of the data scientist's toolbox. Also enterprise computing will need to focus on process innovation through the intelligent use of event data. In his talk Wil van der Aalst will focus on challenges related to process mining in the large , i.e., dealing with many processes, many actors, many data sources, and huge amounts of data at the same time. By adequately addressing these challenges (e.g., using process cubes) we get a new kind of superglue that will impact the future of enterprise computing

Business process management: a comprehensive survey

Business Process Management (BPM) research resulted in a plethora of methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. This survey aims to structure these results and provide an overview of the state-of-the-art in BPM. In BPM the concept of a process model is fundamental. Process models may be used to configure information systems, but may also be used to analyze, understand, and improve the processes they describe. Hence, the introduction of BPM technology has both managerial and technical ramifications and may enable significant productivity improvements, cost savings, and flow-time reductions. The practical relevance of BPM and rapid developments over the last decade justify a comprehensive survey

Business process simulation survival guide

Abstract Simulation provides a flexible approach to analyzing business processes. Through simulation experiments various what if questions can be answered and redesign alternatives can be compared with respect to key performance indicators. This chapter introduces simulation as an analysis tool for business process management. After describing the characteristics of business simulation models, the phases of a simulation project, the generation of random variables, and the analysis of simulation results, we discuss 15 risks, i.e., potential pitfalls jeopardizing the correctness and value of business process simulation. For example, the behavior of resources is often modeled in a rather nai¨ve manner resulting in unreliable simulation models. Whereas traditional simulation approaches rely on hand-made models, we advocate the use of process mining techniques for creating more reliable simulation models based on real event data. Moreover, simulation can be turned into a powerful tool for operational decision making by using real-time process data

Business process configuration in the cloud: how to support and analyze multi-tenant processes?

Lion's share of cloud research has been focusing on performance related problems. However, cloud computing will also change the way in which business processes are managed and supported, e.g., more and more organizations will be sharing common processes. In the classical setting, where product software is used, different organizations can make ad-hoc customizations to let the system fit their needs. This is undesirable, especially when multiple organizations share a cloud infrastructure. Configurable process models enable the sharing of common processes among different organizations in a controlled manner. This paper discusses challenges and opportunities related to business process configuration. Causal nets (C-nets) are proposed as a new formalism to deal with these challenges, e.g., merging variants into a configurable model is supported by a simple union operator. C-nets also provide a good representational bias for process mining, i.e., process discovery and conformance checking based on event logs. In the context of cloud computing, we focus on the application of C-nets to cross-organizational process mining

Process cubes:slicing, dicing, rolling up and drilling down event data for process mining

\u3cp\u3eRecent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.\u3c/p\u3

A general divide and conquer approach for process mining

Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining - an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data ( Big Data ) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems

Putting Petri nets to work in Industry

Petri nets exist for over 30 years. Especially in the last decade Petri nets have been put into practive extensively. Thanks to several useful extensions and the availability of computer tools, Petri nets have become a mature tool for modelling and analysing industrial systems. This paper describes an approach based on a high-level Petri net model, i.e. an extended version of the classical Petri net model. This approach has been used to model and analyze a variety of systems in application domains ranging from logistics to office automation

Using process mining to bridge the gap between BI and BPM

Process mining techniques enable process-centric analytics through automated process discovery, conformance checking, and model enhancement