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Process Mining: Data science in Action
  • Level Professional
  • المدة 22 ساعات hours
  • الطبع بواسطة Eindhoven University of Technology
  • Offered by

عن

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.

الوحدات

Course Introduction and Overview

    • 1 Videos
    • 2 Readings
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1 Videos

  • Course Background and Practical Information

2 Readings

  • Welcome to Process Mining: Data Science in Action
  • The Forum is your (Extended) Classroom

Data and Process Mining

    • 3 Videos
    • 1 Readings
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3 Videos

  • 1.1: Data Science and Big Data
  • 1.2: Different Types of Process Mining
  • 1.3: How Process Mining Relates to Data Mining

1 Readings

  • Process Mining: Data Science in Action Getting Started!

Decision Trees

    • 3 Videos
    • 1 Readings
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3 Videos

  • 1.4: Learning Decision Trees
  • 1.5: Applying Decision Trees
  • 1.6: Association Rule Learning

1 Readings

  • [Extra] The data used in the lectures

Clustering and Association Rule Learning

    • 2 Videos
    • 1 Readings
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2 Videos

  • 1.7: Cluster Analysis
  • 1.8: Evaluating Mining Results

1 Readings

  • How is Process Mining Different from Data Mining?

Review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Quiz 1

1 Readings

  • Quick Note Regarding Quizzes in this Course

(OPTIONAL, NOT FOR POINTS) Real-life Process Mining Session

    • 9 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Real-life Process Mining Session Quiz (Not for points)

9 Videos

  • Introducing Fluxicon & Disco
  • Real Life Session 01: The Demo Scenario (7 min.)
  • Real Life Session 02: Process Discovery and Simplification (11 min.)
  • Real Life Session 03: Statistics, Cases and Variants (8 min.)
  • Real Life Session 04: Bottleneck Analysis (7 min.)
  • Real Life Session 05: Compliance Analysis (6 min.)
  • Real Life Session 06: Tip 1 - Keep Copies of your Analyses (4 min.)
  • Real Life Session 07: Tip 2 - Take Different Views on your Process (7 min.)
  • Real Life Session 08: Tip 3 - Exporting Results (4 min.)

1 Readings

  • Real-life Process Mining Session

Event Logs and Process Models

    • 1 Videos
    • 1 Readings
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1 Videos

  • 2.1: Event Logs and Process Models

1 Readings

  • Using Event Data to Tear Down the Towers of Babel in Process Management

Petri Nets

    • 2 Videos
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2 Videos

  • 2.2: Petri Nets (1/2)
  • 2.3: Petri Nets (2/2)

Transition Systems, Petri & Workflow Nets, and Soundness

    • 2 Videos
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2 Videos

  • 2.4: Transition Systems and Petri Net Properties
  • 2.5: Workflow Nets and Soundness

Alpha Algorithm

    • 2 Videos
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2 Videos

  • 2.6: Alpha Algorithm: A Process Discovery Algorithm
  • 2.7: Alpha Algorithm: Limitations

Intro to ProM and Disco

    • 1 Videos
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1 Videos

  • 2.8: Introducing ProM and Disco

Review

    • 1 Assignment
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1 Assignment

  • Quiz 2

Tool Quiz

    • 1 Assignment
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1 Assignment

  • Tool Quiz

Quality and Representational Bias

    • 3 Videos
    • 1 Readings
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3 Videos

  • 3.1: Four Quality Criteria For Process Discovery
  • 3.2: On The Representational Bias of Process Mining
  • 3.3: Business Process Model and Notation (BPMN)

1 Readings

  • Process Mining in the Large: Smart Data Scientists Are More Important Than Big Computers!!

Dependency Graphs and Causal Nets

    • 3 Videos
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3 Videos

  • 3.4: Dependency Graphs and Causal Nets
  • 3.5: Learning Dependency Graphs
  • 3.6: Learning Causal nets and Annotating Them

Transition Systems and Concurrency

    • 2 Videos
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2 Videos

  • 3.7: Learning Transition Systems
  • 3.8: Using Regions to Discover Concurrency

Review

    • 1 Assignment
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1 Assignment

  • Quiz 3

Process Discovery

    • 2 Videos
    • 1 Readings
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2 Videos

  • 4.1: Two-Phase Process Discovery And Its Limitations
  • 4.2: Alternative Process Discovery Techniques

1 Readings

  • Conformance Checking: Positive and Negative Deviants

Conformance Checking

    • 5 Videos
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5 Videos

  • 4.3: Introduction to Conformance Checking
  • 4.4: Conformance Checking Using Causal Footprints
  • 4.5: Conformance Checking Using Token-Based Replay
  • 4.6: Token Based Replay: Some Examples
  • 4.7: Aligning Observed and Modeled Behavior

Exploring Event Data

    • 1 Videos
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1 Videos

  • 4.8: Exploring Event Data

Review

    • 1 Assignment
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1 Assignment

  • Quiz 4

Peer Assignment

    • 1 PeerReview
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1 Peer Review

  • Applying Process Mining on Real Data

Decision Point Analysis

    • 3 Videos
    • 1 Readings
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3 Videos

  • 5.1: About the Last Two Weeks of This Course
  • 5.2: Mining Decision Points
  • 5.3: Discovering Data Aware Petri Nets

1 Readings

  • Holistic Process Mining: Integrating Different Perspectives

Mining: Bottlenecks, Social Networks, and Organizational

    • 3 Videos
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3 Videos

  • 5.4: Mining Bottlenecks
  • 5.5: Mining Social Networks
  • 5.6: Organizational Mining

Combining and Comparative Mining Perspectives

    • 3 Videos
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3 Videos

  • 5.7: Combining Different Perspectives
  • 5.8: Comparative Process Mining Using Process Cubes
  • 5.9: Refined Process Mining Framework

Review

    • 1 Assignment
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1 Assignment

  • Quiz 5

Operational Support

    • 1 Videos
    • 1 Readings
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1 Videos

  • 6.1: Operational Support: Detect, Predict and Recommend

1 Readings

  • Process models are like maps: Which one is best depends on the questions that need to be answered!

Getting Event Data and Process Mining Software Overview

    • 3 Videos
    • 1 Readings
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3 Videos

  • 6.2: Getting the Right Event Data
  • 6.3: Guidelines for Logging
  • 6.4: Process Mining Software

1 Readings

  • Overview: Process Mining Software

Conducting a Process Mining Project

    • 3 Videos
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3 Videos

  • 6.5: How to Conduct a Process Mining Project
  • 6.6: Mining Lasagna Processes
  • 6.7: Mining Spaghetti Processes

Conclusion

    • 2 Videos
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2 Videos

  • 6.8: Process Models as Maps
  • 6.9: Data Science in Action

Review

    • 1 Assignment
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1 Assignment

  • Quiz 6

Final Quiz

    • 1 Assignment
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1 Assignment

  • Final Quiz

Auto Summary

Unlock the potential of data science with our comprehensive course, "Process Mining: Data Science in Action." Tailored for professionals in the Data Science and AI domains, this course bridges the gap between model-based process analysis and data-centric techniques. Led by expert instructors, you'll dive into the world of process mining through real-life data sets and user-friendly software. In this course, you'll explore the three main types of process mining: discovery, conformance, and enhancement. Learn to automatically generate process models from raw event data, compare these models with actual event logs, and enhance them with performance insights. Applications span diverse fields, from improving hospital treatment processes to optimizing customer service in multinationals. Over the duration of approximately 1320 minutes, you'll gain practical skills to directly apply theory in various domains. The course starts with an overview of event data technologies and focuses on process mining as a bridge between data mining and business process modeling. Ideal for those at an introductory level, it includes practical assignments to solidify your understanding. By the end of this course, you will: - Understand Business Process Intelligence techniques and the role of Big Data. - Relate process mining to other analysis techniques like simulation, business intelligence, and machine learning. - Apply process discovery techniques to learn process models manually and with tools. - Conduct conformance checking and enhance process models with event log information. - Use process mining for operational support, including prediction and recommendation. Available through Coursera with a Starter subscription option, this professional-level course is perfect for data scientists, business analysts, and IT professionals seeking to enhance their skills in process mining and business process management. Join us to master the art of data science in action and drive impactful improvements in your organization.

Wil van der Aalst
Instructor

Wil van der Aalst