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2011 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Process Mining

Discovery, Conformance and Enhancement of Business Processes

verfasst von: Wil M. P. van der Aalst

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

More and more information about business processes is recorded by information systems in the form of so-called “event logs”. Despite the omnipresence of such data, most organizations diagnose problems based on fiction rather than facts. Process mining is an emerging discipline based on process model-driven approaches and data mining. It not only allows organizations to fully benefit from the information stored in their systems, but it can also be used to check the conformance of processes, detect bottlenecks, and predict execution problems.

Wil van der Aalst delivers the first book on process mining. It aims to be self-contained while covering the entire process mining spectrum from process discovery to operational support. In Part I, the author provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Part II focuses on process discovery as the most important process mining task. Part III moves beyond discovering the control flow of processes and highlights conformance checking, and organizational and time perspectives. Part IV guides the reader in successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM. Finally, Part V takes a step back, reflecting on the material presented and the key open challenges.

Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Information systems are becoming more and more intertwined with the operational processes they support. As a result, multitudes of events are recorded by today’s information systems. Nevertheless, organizations have problems extracting value from these data. The goal of process mining is to use event data to extract process-related information, e.g., to automatically discover a process model by observing events recorded by some enterprise system. To show the importance of process mining, this chapter discusses the spectacular growth of event data and links this to the limitations of classical approaches to business process management. To explain the basic concepts, a small example is used. Finally, it is shown that process mining can play an important role in realizing the promises made by contemporary management trends such as SOX and Six Sigma.
Wil M. P. van der Aalst

Preliminaries

Frontmatter
Chapter 2. Process Modeling and Analysis
Abstract
The plethora of process modeling notations available today illustrates the relevance of process modeling. Some organizations may use only informal process models to structure discussions and to document procedures. However, organizations that operate at a higher BPM maturity level use models that can be analyzed and used to enact operational processes. Today, most process models are made by hand and are not based on a rigorous analysis of existing process data. This chapter serves two purposes. On the one hand, preliminaries are presented that will be used in later chapters. For example, various process modeling notations are introduced and some analysis techniques are reviewed. On the other hand, the chapter reveals the limitations of classical approaches, thus motivating the need for process mining.
Wil M. P. van der Aalst
Chapter 3. Data Mining
Abstract
Process mining builds on two pillars: (a) process modeling and analysis (as described in Chap. 2) and (b) data mining. This chapter introduces some basic data mining approaches and structures the field. The motivation for doing so is twofold. On the one hand, some process mining techniques build on classical data mining techniques, e.g., discovery and enhancement approaches focusing on data and resources. On the other hand, ideas originating from the data mining field will be used for the evaluation of process mining results. For example, one can adopt various data mining approaches to measure the quality of the discovered or enhanced process models. Existing data mining techniques are of little use for control-flow discovery, conformance checking, and other process mining tasks. Nevertheless, a basic understanding of data mining is most helpful for fully understanding the process mining techniques presented in subsequent chapters.
Wil M. P. van der Aalst

From Event Logs to Process Models

Frontmatter
Chapter 4. Getting the Data
Abstract
Process mining is impossible without proper event logs. This chapter describes the information that should be present in such event logs. Depending on the process mining technique used, these requirements may vary. The challenge is to extract such data from a variety of data sources, e.g., databases, flat files, message logs, transaction logs, ERP systems, and document management systems. When merging and extracting data, both syntax and semantics play an important role. Moreover, depending on the questions one seeks to answer, different views on the available data are needed.
Wil M. P. van der Aalst
Chapter 5. Process Discovery: An Introduction
Abstract
Process discovery is one of the most challenging process mining tasks. Based on an event log, a process model is constructed thus capturing the behavior seen in the log. This chapter introduces the topic using the rather naïve α-algorithm. This algorithm nicely illustrates some of the general ideas used by many process mining algorithms and helps to understand the notion of process discovery. Moreover, the α-algorithm serves as a stepping stone for discussing challenges related to process discovery.
Wil M. P. van der Aalst
Chapter 6. Advanced Process Discovery Techniques
Abstract
The α-algorithm nicely illustrates some of the main ideas behind process discovery. However, this simple algorithm is unable to manage the trade-offs involving the four quality dimensions described in Chap. 5 (fitness, simplicity, precision, and generalization). To successfully apply process mining in practice, one needs to deal with noise and incompleteness. This chapter focuses on more advanced process discovery techniques. The goal is not to present one particular technique in detail, but to provide an overview of the most relevant approaches. This will assist the reader in selecting the appropriate process discovery technique. Moreover, insights into the strengths and weaknesses of the various approaches support the correct interpretation and effective use of the discovered models.
Wil M. P. van der Aalst

Beyond Process Discovery

Frontmatter
Chapter 7. Conformance Checking
Abstract
After covering control-flow discovery in depth in Part II, this chapter looks at the situation in which both a process model and an event log are given. The model may have been constructed by hand or may have been discovered. Moreover, the model may be normative or descriptive. Conformance checking relates events in the event log to activities in the process model and compares both. The goal is to find commonalities and discrepancies between the modeled behavior and the observed behavior. Conformance checking is relevant for business alignment and auditing. For example, the event log can be replayed on top of the process model to find undesirable deviations suggesting fraud or inefficiencies. Moreover, conformance checking techniques can also be used for measuring the performance of process discovery algorithms and to repair models that are not aligned well with reality.
Wil M. P. van der Aalst
Chapter 8. Mining Additional Perspectives
Abstract
Whereas the main focus of process discovery is on the control-flow perspective, event logs may contain a wealth of information relating to other perspectives such as the organizational perspective, the case perspective, and the time perspective. Therefore, we now shift our attention to these other perspectives. Organizational mining can be used to get insight into typical work patterns, organizational structures, and social networks. Timestamps and frequencies of activities can be used to identify bottlenecks and diagnose other performance related problems. Case data can be used to better understand decision-making and analyze differences among cases. Moreover, the different perspectives can be merged into a single model providing an integrated view on the process. Such an integrated model can be used for “what if” analysis using simulation.
Wil M. P. van der Aalst
Chapter 9. Operational Support
Abstract
Most process-mining techniques work on “post mortem” event data, i.e., they analyze events that belong to cases that have already completed. Obviously, it is not possible to influence the execution of “post mortem” cases. Moreover, cases that are still in the pipeline cannot be guided on the basis of “post mortem” event data only. Today, however, many data sources are updated in (near) real-time and sufficient computing power is available to analyze events when they occur. Therefore, process mining should not be restricted to off-line analysis and can also be used for online operational support. This chapter broadens the scope of process mining to include online decision support. For example, for a running case the remaining flow time can be predicted and suitable actions can be recommended to minimize costs.
Wil M. P. van der Aalst

Putting Process Mining to Work

Frontmatter
Chapter 10. Tool Support
Abstract
Many vendors offer Business Intelligence (BI) software products. Unfortunately, most of these products are data-centric and focus on rather simplistic forms of analysis. As shown in the preceding chapters, process-centric, truly “intelligent” BI is possible due to advances in process mining. For example, ProM is an open-source process mining tool supporting all of the techniques mentioned in this book. Process discovery, conformance checking, social network analysis, organizational mining, decision mining, history-based prediction and recommendation, etc. are all supported by ProM. Recently, several software vendors started adding process mining capabilities to their products. This chapter provides an overview of the market for BI products. Moreover, ProM and other tools supporting process mining are described in more detail.
Wil M. P. van der Aalst
Chapter 11. Analyzing “Lasagna Processes”
Abstract
Lasagna processes are relatively structured and the cases flowing through such processes are handled in a controlled manner. Therefore, it is possible to apply all of the process mining techniques presented in the preceding chapters. This chapter characterizes Lasagna processes and discusses typical use cases for process mining. Moreover, the different stages of a process mining project for improving a Lasagna process are described. The resulting life-cycle model guides users of process mining tools like ProM. Moreover, different application scenarios are discussed.
Wil M. P. van der Aalst
Chapter 12. Analyzing “Spaghetti Processes”
Abstract
Spaghetti processes are the counterpart of Lasagna processes. Because Spaghetti processes are less structured, only a subset of the process mining techniques described in this book are applicable. For instance, it makes no sense to aim at operational support activities if there is too much variability. Nevertheless, process mining can help to realize dramatic process improvements by uncovering key problems.
Wil M. P. van der Aalst

Reflection

Frontmatter
Chapter 13. Cartography and Navigation
Abstract
Process models can be seen as the “maps” describing the operational processes of organizations. Similarly, information systems can be looked at as “navigation systems” guiding the flow of work in organizations. Unfortunately, many organizations fail in creating and maintaining accurate business process maps. Often process models are outdated and have little to do with reality. Moreover, most information systems fail to provide the functionality offered by today’s navigation systems. For instance, workers are not guided by the information system and need to work behind the system’s back to get things done. Moreover, useful information such as the “estimated arrival time” of a running case is not provided. Process mining can help to overcome some of these problems.
Wil M. P. van der Aalst
Chapter 14. Epilogue
Abstract
To conclude this book, we summarize the main reasons for using process mining. Process mining can be seen as the “missing link” between data mining and traditional model-driven BPM. Although mature process mining techniques and tools are available, several challenges remain to further improve the applicability of the techniques presented in the preceding chapters. Therefore, we list the most important challenges. Finally, we encourage the reader to start using process mining today. For organizations that store event data in some form, the threshold to get started is really low.
Wil M. P. van der Aalst
Backmatter
Metadaten
Titel
Process Mining
verfasst von
Wil M. P. van der Aalst
Copyright-Jahr
2011
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-642-19345-3
Print ISBN
978-3-642-19344-6
DOI
https://doi.org/10.1007/978-3-642-19345-3

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