À propos de ce cours
4.8
414 notes
105 avis
Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. Data science is the profession of the future, because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis. The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining. Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems). Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking site, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine. All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as "data science in action". The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data. Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains. This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design. Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments. The course covers the three main types of process mining. 1. The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. An example is the Alpha-algorithm that takes an event log and produces a process model (a Petri net) explaining the behavior recorded in the log. 2. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. Conformance checking can be used to check if reality, as recorded in the log, conforms to the model and vice versa. 3. The third type of process mining is enhancement. Here, the idea is to extend or improve an existing process model using information about the actual process recorded in some event log. Whereas conformance checking measures the alignment between model and reality, this third type of process mining aims at changing or extending the a-priori model. An example is the extension of a process model with performance information, e.g., showing bottlenecks. Process mining techniques can be used in an offline, but also online setting. The latter is known as operational support. An example is the detection of non-conformance at the moment the deviation actually takes place. Another example is time prediction for running cases, i.e., given a partially executed case the remaining processing time is estimated based on historic information of similar cases. Process mining provides not only a bridge between data mining and business process management; it also helps to address the classical divide between "business" and "IT". Evidence-based business process management based on process mining helps to create a common ground for business process improvement and information systems development. The course uses many examples using real-life event logs to illustrate the concepts and algorithms. After taking this course, one is able to run process mining projects and have a good understanding of the Business Process Intelligence field. After taking this course you should: - have a good understanding of Business Process Intelligence techniques (in particular process mining), - understand the role of Big Data in today’s society, - be able to relate process mining techniques to other analysis techniques such as simulation, business intelligence, data mining, machine learning, and verification, - be able to apply basic process discovery techniques to learn a process model from an event log (both manually and using tools), - be able to apply basic conformance checking techniques to compare event logs and process models (both manually and using tools), - be able to extend a process model with information extracted from the event log (e.g., show bottlenecks), - have a good understanding of the data needed to start a process mining project, - be able to characterize the questions that can be answered based on such event data, - explain how process mining can also be used for operational support (prediction and recommendation), and - be able to conduct process mining projects in a structured manner....
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Calendar

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Intermediate Level

Niveau intermédiaire

Clock

Approx. 27 hours to complete

Recommandé : 6 weeks of study, 3 to 5 hours/week of material + self study...
Comment Dots

English

Sous-titres : English...

Compétences que vous acquerrez

Petri NetProcess ModelingProcess MiningData Mining
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Calendar

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Intermediate Level

Niveau intermédiaire

Clock

Approx. 27 hours to complete

Recommandé : 6 weeks of study, 3 to 5 hours/week of material + self study...
Comment Dots

English

Sous-titres : English...

Programme du cours : ce que vous apprendrez dans ce cours

Week
1
Clock
6 heures pour terminer

Introduction and Data Mining

This first module contains general course information (syllabus, grading information) as well as the first lectures introducing data mining and process mining....
Reading
18 vidéos (Total 228 min), 7 lectures, 2 quiz
Video18 vidéos
1.1: Data Science and Big Data16 min
1.2: Different Types of Process Mining21 min
1.3: How Process Mining Relates to Data Mining20 min
1.4: Learning Decision Trees26 min
1.5: Applying Decision Trees20 min
1.6: Association Rule Learning18 min
1.7: Cluster Analysis13 min
1.8: Evaluating Mining Results14 min
Introducing Fluxicon & Disco10 min
Real Life Session 01: The Demo Scenario (7 min.)7 min
Real Life Session 02: Process Discovery and Simplification (11 min.)10 min
Real Life Session 03: Statistics, Cases and Variants (8 min.)7 min
Real Life Session 04: Bottleneck Analysis (7 min.)7 min
Real Life Session 05: Compliance Analysis (6 min.)5 min
Real Life Session 06: Tip 1 - Keep Copies of your Analyses (4 min.)4 min
Real Life Session 07: Tip 2 - Take Different Views on your Process (7 min.)7 min
Real Life Session 08: Tip 3 - Exporting Results (4 min.)4 min
Reading7 lectures
Welcome to Process Mining: Data Science in Action10 min
The Forum is your (Extended) Classroom10 min
Process Mining: Data Science in Action Getting Started!10 min
[Extra] The data used in the lectures10 min
How is Process Mining Different from Data Mining?10 min
Quick Note Regarding Quizzes in this Course10 min
Real-life Process Mining Session10 min
Quiz2 exercices pour s'entraîner
Quiz 120 min
Real-life Process Mining Session Quiz (Not for points)12 min
Week
2
Clock
4 heures pour terminer

Process Models and Process Discovery

In this module we introduce process models and the key feature of process mining: discovering process models from event data....
Reading
8 vidéos (Total 159 min), 1 lecture, 2 quiz
Video8 vidéos
2.2: Petri Nets (1/2)16 min
2.3: Petri Nets (2/2)17 min
2.4: Transition Systems and Petri Net Properties20 min
2.5: Workflow Nets and Soundness16 min
2.6: Alpha Algorithm: A Process Discovery Algorithm25 min
2.7: Alpha Algorithm: Limitations23 min
2.8: Introducing ProM and Disco25 min
Reading1 lecture
Using Event Data to Tear Down the Towers of Babel in Process Management10 min
Quiz2 exercices pour s'entraîner
Quiz 220 min
Tool Quiz40 min
Week
3
Clock
3 heures pour terminer

Different Types of Process Models

Now that you know the basics of process mining, it is time to dive a little bit deeper and show you other ways of discovering a process model from event data....
Reading
8 vidéos (Total 145 min), 1 lecture, 1 quiz
Video8 vidéos
3.2: On The Representational Bias of Process Mining16 min
3.3: Business Process Model and Notation (BPMN)15 min
3.4: Dependency Graphs and Causal Nets21 min
3.5: Learning Dependency Graphs21 min
3.6: Learning Causal nets and Annotating Them18 min
3.7: Learning Transition Systems14 min
3.8: Using Regions to Discover Concurrency18 min
Reading1 lecture
Process Mining in the Large: Smart Data Scientists Are More Important Than Big Computers!!10 min
Quiz1 exercice pour s'entraîner
Quiz 320 min
Week
4
Clock
5 heures pour terminer

Process Discovery Techniques and Conformance Checking

In this module we conclude process discovery by discussing alternative approaches. We also introduce how to check the conformance of the event data and the process model....
Reading
8 vidéos (Total 129 min), 1 lecture, 2 quiz
Video8 vidéos
4.2: Alternative Process Discovery Techniques22 min
4.3: Introduction to Conformance Checking11 min
4.4: Conformance Checking Using Causal Footprints10 min
4.5: Conformance Checking Using Token-Based Replay14 min
4.6: Token Based Replay: Some Examples15 min
4.7: Aligning Observed and Modeled Behavior18 min
4.8: Exploring Event Data20 min
Reading1 lecture
Conformance Checking: Positive and Negative Deviants10 min
Quiz1 exercice pour s'entraîner
Quiz 420 min
4.8
Briefcase

83%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par ATMay 13th 2018

Very interesting course, explained in a understandable way and rich of high level topics. Essential for anyone who likes statistics and process analysis. Many congratulations for it!

par ECJul 31st 2017

Great course. Professor Wil van der Aalst delivers great lectures, very clear and deep in general with good examples. I really enjoyed the course from the beginning to the end.

Enseignant

Wil van der Aalst

Professor dr.ir.
Department of Mathematics & Computer Science

À propos de Eindhoven University of Technology

Eindhoven University of Technology (TU/e) is a research-driven, design-oriented university of technology with a strong international focus. The university was founded in 1956, and has around 8,500 students and 3,000 staff. TU/e has defined strategic areas focusing on the societal challenges in Energy, Health and Smart Mobility. The Brainport Eindhoven region is one of world’s smartest; it won the title Intelligent Community of the Year 2011....

Foire Aux Questions

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