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[Coursera] Process Mining: Data science in Action [2014, ENG]
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Process Mining: Data science in Action
#777 Год выпуска: 2014 Производитель: Coursera Сайт производителя: 13:23:25 Тип раздаваемого материала: Видеоурок Язык: Английский Описание: наука данных является профессия будущего , потому что организации , которые не могут использовать ( большие) данные в умный способ не выживет . Это не достаточно, чтобы сосредоточиться на хранении данных и анализа данных . Ученый данные также необходимо связать данные в процессе анализа. добыча Процесс ликвидирует разрыв между традиционным анализом на основе модели процесса (например, моделирования и других методов управления бизнес-процессами ) и методов анализа, ориентированных на данные , такие как машинного обучения и интеллектуального анализа данных . 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 a 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. Содержание ├── 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 Файлы примеров: отсутствуют Формат видео: MP4 Видео : AVC, 960x540 (16:9), 29.970 (29970/1000) fps, ~173 Kbps avg, 0.011 bit/pixel Аудио: 44.1 KHz, AAC LC, 2 ch, ~128.0 Kbps Скриншоты |
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