Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.

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The application of Petri-nets to workflow management

Workflow management systems will change the architecture of future information systems dramatically. The explicit representation of business procedures is one of the main issues when introducing a workflow management system. In this paper we focus on a class of Petri nets suitable for the representation, validation and verification of these procedures. We will show that the correctness of a procedure represented by such a Petri net can be verified by using standard Petri-net-based techniques. Based on this result we provide a comprehensive set of transformation rules which can be used to construct and modify correct procedures.

Verification of Workflow Nets

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.

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Business Process Management: A Comprehensive Survey

For many companies, business processes have become the focal point of attention. As a result, many tools have been developed for business process engineering and the actual deployment of business processes. Typical examples of these tools are Business Process Reengineering (BPR) tools, Enterprise Resource Planning (ERP) systems, and Workflow Management (WFM) systems. Some of the leading products, e.g. SAP R/3 (ERP/WFM) and ARIS (BPR), use Event-driven Process Chains (EPCs) to model business processes. Although the EPCs have become a widespread process modeling technique, they suffer from a serious drawback: neither the syntax nor the semantics of an EPC are well defined. In this paper, this problem is tackled by mapping EPCs (without connectors of type ∨) onto Petri nets. The Petri nets have formal semantics and provide an abundance of analysis techniques. As a result, the approach presented in this paper gives formal semantics to EPCs. Moreover, many analysis techniques are available for EPCs. To illustrate the approach, it is shown that the correctness of an EPC can be checked in polynomial time by using Petri-net-based analysis techniques.

Formalization and verification of event-driven process chains

Given an event log describing observed behaviour, process discovery aims to find a process model that ‘best’ describes this behaviour. A large variety of process discovery algorithms has been proposed. However, no existing algorithm returns a sound model in all cases (free of deadlocks and other anomalies), handles infrequent behaviour well and finishes quickly. We present a technique able to cope with infrequent behaviour and large event logs, while ensuring soundness. The technique has been implemented in ProM and we compare the technique with existing approaches in terms of quality and performance.

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Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour

Business processes provide a means of coordinating interactions between workers and organisations in a structured way. However the dynamic nature of the modern business environment means these processes are subject to a increasingly wide range of variations and must demonstrate flexible approaches to dealing with these variations if they are to remain viable. The challenge is to provide flexibility and offer process support at the same time. Many approaches have been proposed in literature and some of these approaches have been implemented in flexible workflow management systems. However, a comprehensive overview of the various approaches has been missing. In this paper, we take a deeper look into the various ways in which flexibility can be achieved and we propose an extensive taxonomy of flexibility. This taxonomy is subsequently used to evaluate a selection of systems and to discuss how the various forms of flexibility fit together.

Process flexibility: A survey of contemporary approaches

Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but still have problems discovering and visualizing less structured ones. Unfortunately, process mining is most interesting in domains requiring flexibility. A typical example would be the treatment process in a hospital where it is vital that people can deviate to deal with changing circumstances. Here it is useful to provide insights into the actual processes but at the same time there is a lot of diversity leading to complex models that are difficult to interpret. This paper presents an approach using trace clustering, i.e., the event log is split into homogeneous subsets and for each subset a process model is created. We demonstrate that our approach, based on log profiles, can improve process mining results in real flexible environments. To illustrate this we present a real-life case study.

Trace Clustering in Process Mining

Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality.

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Wmp Wil van der Aalst

Papers

Business Process Management: A Comprehensive Survey

Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour

Process flexibility: A survey of contemporary approaches

Trace Clustering in Process Mining

XES, XESame, and ProM 6