Process Mining Data science in Action

2014

├── 2 – 1 – Lecture 1.1- Data Science and Big Data (17 min.).mp4
├── 2 – 2 – Lecture 1.2- Different Types of Process Mining (21 min.).mp4
├── 2 – 3 – Lecture 1.3- How Process Mining Relates to Data Mining (20 min.).mp4
├── 2 – 4 – Lecture 1.4- Learning Decision Trees (27 min.).mp4
├── 2 – 5 – Lecture 1.5- Applying Decision Trees (21 min.).mp4
├── 2 – 6 – Lecture 1.6- Association Rule Learning (18 min.).mp4
├── 2 – 7 – Lecture 1.7- Cluster Analysis (13 min.).mp4
├── 2 – 8 – Lecture 1.8- Evaluating Mining Results (15 min.).mp4
├── 3 – 1 – Lecture 2.1- Event Logs and Process Models (14 min.).mp4
├── 3 – 2 – Lecture 2.2- Petri Nets (1-2) (16 min.).mp4
├── 3 – 3 – Lecture 2.3- Petri Nets (2-2) (18 min.).mp4
├── 3 – 4 – Lecture 2.4- Transition Systems and Petri Net Properties (21 min.).mp4
├── 3 – 5 – Lecture 2.5- Workflow Nets and Soundness (17 min.).mp4
├── 3 – 6 – Lecture 2.6- Alpha Algorithm- A Process Discovery Algorithm (25 min.).mp4
├── 3 – 7 – Lecture 2.7- Alpha Algorithm- Limitations (23 min.).mp4
├── 3 – 8 – Lecture 2.8- Introducing ProM and Disco (25 min.).mp4
├── 4 – 1 – Lecture 3.1- Four Quality Criteria For Process Discovery (19 min.).mp4
├── 4 – 2 – Lecture 3.2- On The Representational Bias of Process Mining (17 min.).mp4
├── 4 – 3 – Lecture 3.3- Business Process Model and Notation (BPMN) (15 min.).mp4
├── 4 – 4 – Lecture 3.4- Dependency Graphs and Causal Nets (21 min.).mp4
├── 4 – 5 – Lecture 3.5- Learning Dependency Graphs (21 min.).mp4
├── 4 – 6 – Lecture 3.6- Learning Causal nets and Annotating Them (18 min.).mp4
├── 4 – 7 – Lecture 3.7- Learning Transition Systems (15 min.).mp4
├── 4 – 8 – Lecture 3.8- Using Regions to Discover Concurrency (18 min.).mp4
├── 5 – 1 – Lecture 4.1- Two-Phase Process Discovery And Its Limitations (15 min.).mp4
├── 5 – 2 – Lecture 4.2- Alternative Process Discovery Techniques (23 min.).mp4
├── 5 – 3 – Lecture 4.3- Introduction to Conformance Checking (12 min.).mp4
├── 5 – 4 – Lecture 4.4- Conformance Checking Using Causal Footprints (10 min.).mp4
├── 5 – 5 – Lecture 4.5- Conformance Checking Using Token-Based Replay (15 min.).mp4
├── 5 – 6 – Lecture 4.6- Token Based Replay- Some Examples (15 min.).mp4
├── 5 – 7 – Lecture 4.7- Aligning Observed and Modeled Behavior (18 min.).mp4
├── 5 – 8 – Lecture 4.8- Exploring Event Data (21 min.).mp4
├── 6 – 1 – Lecture 5.1- About the Last Two Weeks of This Course (10 min.).mp4
├── 6 – 2 – Lecture 5.2- Mining Decision Points (17 min.).mp4
├── 6 – 3 – Lecture 5.3- Discovering Data Aware Petri Nets (12 min.).mp4
├── 6 – 4 – Lecture 5.4- Mining Bottlenecks (11 min.).mp4
├── 6 – 5 – Lecture 5.5- Mining Social Networks (17 min.).mp4
├── 6 – 6 – Lecture 5.6- Organizational Mining (9 min.).mp4
├── 6 – 7 – Lecture 5.7- Combining Different Perspectives (13 min.).mp4
├── 6 – 8 – Lecture 5.8- Comparative Process Mining Using Process Cubes (13 min.).mp4
├── 6 – 9 – Lecture 5.9- Refined Process Mining Framework (11 min.).mp4
├── 7 – 1 – Lecture 6.1- Operational Support- Detect, Predict and Recommend (17 min.).mp4
├── 7 – 2 – Lecture 6.2- Getting the Right Event Data (17 min.).mp4
├── 7 – 3 – Lecture 6.3- Guidelines for Logging (10 min.).mp4
├── 7 – 4 – Lecture 6.4- Process Mining Software (16 min.).mp4
├── 7 – 5 – Lecture 6.5- How to Conduct a Process Mining Project (11 min.).mp4
├── 7 – 6 – Lecture 6.6- Mining Lasagna Processes (6 min.).mp4
├── 7 – 7 – Lecture 6.7- Mining Spaghetti Processes (8 min.).mp4
├── 7 – 8 – Lecture 6.8- Process Models as Maps (12 min.).mp4
└── 7 – 9 – Lecture 6.9- Data Science in Action (9 min.).mp4