Process Mining: Data science in Action

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About this course: 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 …

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Who is this class for: This course is aimed at both students and professionals. A basic understanding of logic, sets, and statistics (at the undergraduate level) is assumed. Basic computer skills are required to use the software provided with the course (but no programming experience is needed). Participants are also expected to have an interest in process modeling and data mining but no specific prior knowledge is assumed as these concepts are introduced in the course.

Created by:  Eindhoven University of Technology
  • Taught by:  Wil van der Aalst, Professor dr.ir.

    Department of Mathematics & Computer Science
Level Intermediate Commitment 6 weeks of study, 3 to 5 hours/week of material + self study Language English Hardware Req Laptop or computer with 1+ GB memory, able to run Java tools. How To Pass Pass all graded assignments to complete the course. User Ratings 4.7 stars Average User Rating 4.7See what learners said Coursework

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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.

Syllabus


WEEK 1


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.


18 videos, 7 readings, 1 practice quiz expand


  1. Reading: Welcome to Process Mining: Data Science in Action
  2. Video: Course Background and Practical Information
  3. Reading: The Forum is your (Extended) Classroom
  4. Reading: Process Mining: Data Science in Action Getting Started!
  5. Video: 1.1: Data Science and Big Data
  6. Video: 1.2: Different Types of Process Mining
  7. Video: 1.3: How Process Mining Relates to Data Mining
  8. Video: 1.4: Learning Decision Trees
  9. Video: 1.5: Applying Decision Trees
  10. Video: 1.6: Association Rule Learning
  11. Reading: [Extra] The data used in the lectures
  12. Reading: How is Process Mining Different from Data Mining?
  13. Video: 1.7: Cluster Analysis
  14. Video: 1.8: Evaluating Mining Results
  15. Reading: Quick Note Regarding Quizzes in this Course
  16. Slideshow: Process Mining Software
  17. Video: Introducing Fluxicon & Disco
  18. Reading: Real-life Process Mining Session
  19. Video: Real Life Session 01: The Demo Scenario (7 min.)
  20. Video: Real Life Session 02: Process Discovery and Simplification (11 min.)
  21. Video: Real Life Session 03: Statistics, Cases and Variants (8 min.)
  22. Video: Real Life Session 04: Bottleneck Analysis (7 min.)
  23. Video: Real Life Session 05: Compliance Analysis (6 min.)
  24. Video: Real Life Session 06: Tip 1 - Keep Copies of your Analyses (4 min.)
  25. Video: Real Life Session 07: Tip 2 - Take Different Views on your Process (7 min.)
  26. Video: Real Life Session 08: Tip 3 - Exporting Results (4 min.)
  27. Practice Quiz: Real-life Process Mining Session Quiz (Not for points)

Graded: Quiz 1

WEEK 2


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.


8 videos, 1 reading expand


  1. Reading: Using Event Data to Tear Down the Towers of Babel in Process Management
  2. Video: 2.1: Event Logs and Process Models
  3. Video: 2.2: Petri Nets (1/2)
  4. Video: 2.3: Petri Nets (2/2)
  5. Video: 2.4: Transition Systems and Petri Net Properties
  6. Video: 2.5: Workflow Nets and Soundness
  7. Video: 2.6: Alpha Algorithm: A Process Discovery Algorithm
  8. Video: 2.7: Alpha Algorithm: Limitations
  9. Video: 2.8: Introducing ProM and Disco

Graded: Quiz 2
Graded: Tool Quiz

WEEK 3


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.


8 videos, 1 reading expand


  1. Video: 3.1: Four Quality Criteria For Process Discovery
  2. Video: 3.2: On The Representational Bias of Process Mining
  3. Video: 3.3: Business Process Model and Notation (BPMN)
  4. Reading: Process Mining in the Large: Smart Data Scientists Are Important Than Big Computers!!
  5. Video: 3.4: Dependency Graphs and Causal Nets
  6. Video: 3.5: Learning Dependency Graphs
  7. Video: 3.6: Learning Causal nets and Annotating Them
  8. Video: 3.7: Learning Transition Systems
  9. Video: 3.8: Using Regions to Discover Concurrency

Graded: Quiz 3

WEEK 4


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.


8 videos, 1 reading expand


  1. Video: 4.1: Two-Phase Process Discovery And Its Limitations
  2. Video: 4.2: Alternative Process Discovery Techniques
  3. Reading: Conformance Checking: Positive and Negative Deviants
  4. Video: 4.3: Introduction to Conformance Checking
  5. Video: 4.4: Conformance Checking Using Causal Footprints
  6. Video: 4.5: Conformance Checking Using Token-Based Replay
  7. Video: 4.6: Token Based Replay: Some Examples
  8. Video: 4.7: Aligning Observed and Modeled Behavior
  9. Video: 4.8: Exploring Event Data

Graded: Quiz 4
Graded: Applying Process Mining on Real Data

WEEK 5


Enrichment of Process Models
In this module we focus on enriching process models. We can for instance add the data aspect to process models, show bottlenecks on the process model and analyse the social aspects of the process.


9 videos, 1 reading expand


  1. Video: 5.1: About the Last Two Weeks of This Course
  2. Video: 5.2: Mining Decision Points
  3. Video: 5.3: Discovering Data Aware Petri Nets
  4. Reading: Holistic Process Mining: Integrating Different Perspectives
  5. Video: 5.4: Mining Bottlenecks
  6. Video: 5.5: Mining Social Networks
  7. Video: 5.6: Organizational Mining
  8. Video: 5.7: Combining Different Perspectives
  9. Video: 5.8: Comparative Process Mining Using Process Cubes
  10. Video: 5.9: Refined Process Mining Framework

Graded: Quiz 5

WEEK 6


Operational Support and Conclusion
In this final module we discuss how process mining can be applied on running processes. We also address how to get the (right) event data, process mining software, and how to get from data to results.


9 videos, 2 readings expand


  1. Video: 6.1: Operational Support: Detect, Predict and Recommend
  2. Reading: Process models are like maps: Which one is best depends on the questions that need to be answered!
  3. Video: 6.2: Getting the Right Event Data
  4. Video: 6.3: Guidelines for Logging
  5. Video: 6.4: Process Mining Software
  6. Reading: Overview: Process Mining Software
  7. Slideshow: Process Mining Software
  8. Video: 6.5: How to Conduct a Process Mining Project
  9. Video: 6.6: Mining Lasagna Processes
  10. Video: 6.7: Mining Spaghetti Processes
  11. Video: 6.8: Process Models as Maps
  12. Video: 6.9: Data Science in Action

Graded: Quiz 6
Graded: Final Quiz
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