Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 2 - Process Modeling and AnalysisWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 11 - Analyzing Lasagna ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 8 - Mining Additional PerspectivesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 2 - Process Modeling and AnalysisWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 11 - Analyzing Lasagna ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 8 - Mining Additional PerspectivesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 12 - Analyzing Spaghetti ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Event Logs: What kind of data does process mining require?Wil van der Aalst
The starting point for process mining is an event log. How to get this data in a format suitable for process mining? This slide show will explain this.
Each event in such a log refers to an activity (i.e., a well-defined step in some process) and is related to a particular case (i.e., a process instance). The events belonging to a case are ordered and can be seen as one "run" of the process. Event logs may store additional information about events. In fact, whenever possible, process-mining techniques use extra information such as the resource (i.e., person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g., the size of an order).
Process Mining 2.0: From Insights to ActionsMarlon Dumas
Keynote talk at the workshop on Artificial Intelligence for Enterprise Process Transformation in conjunction with the PAKDD'2021 conference. The talk focuses on the move from process mining as a descriptive analytics approach, to process mining as a predictive and prescriptive analytics technology for automated process improvement.
This presentation introduces the Process Mining as the cutting-edge data analytics approach for discovering the real processes by analyzing the event logs, detecting the bottlenecks, and generating recommendations for enhancing the business performance.
Process Mining: Data Science in Action - Wil van der Aalst, TU/e, DSC/e, HSEYandex
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 to find a connection 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 can include: analyzing treatment processes in hospitals, improving customer service processes in multinational companies, understanding browsing behavior of customers on a booking site, analyzing failures of a baggage handling system, or improving user interface of the X-ray machine. What all of these applications have in common is the need to relate dynamic behavior to process models. Not only does process mining provide a bridge between data mining and business process management, but 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.
Process Mining and AI for Continuous Process ImprovementMarlon Dumas
Talk delivered at BPM Day Rio Grande do Sul on 11 November 2021.
Abstract.
Process mining is a technology that marries methods from business process management and from data science, to support operational excellence and digital transformation. Process mining tools can transform data extracted from enterprise systems, into visualizations and reports that allow managers to improve organizational performance along different dimensions, such as efficiency, quality, and compliance. In this talk, we will give an overview of the capabilities of process mining tools, and we will illustrate the benefits of process mining via several case studies in the fields of insurance, manufacturing, and IT service management.
Process Mining and Predictive Process Monitoring in ApromoreMarlon Dumas
Seminar delivered at University of Hasselt on 14 May 2019. The seminar covers the research efforts underpinning Apromore's automated process discovery, conformance checking, log delta analysis, and predictive process monitoring plugins.
Analítica de datos e inteligencia artificial para procesos de negociosMarlon Dumas
Charla sobre minería de procesos e inteligencia operacional, dictada como parte de la reunión inaugural de la Red de Gerentes de Procesos de Bogotá el 10 de julio del 2019 en la Pontificia Universidad Javeriana.
Data mining Course
Chapter 1
Definition of Data Mining
Data Mining as an Interdisciplinary field
The process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
Learning Accurate Business Process Simulation Models from Event Logs via Auto...Marlon Dumas
Paper presentation at the International Conference on Advanced Information Systems Engineering (CAiSE).
This paper presents an approach to automatically discover business process simulation models from event logs by combining process mining and deep learning techniques.
Paper available at: https://link.springer.com/chapter/10.1007/978-3-031-07472-1_4
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
This Slide Deck was presented at the annual international conference of itSMF Slovensko on May, 6th. in Bratislava. It gives an introduction into Process Mining as a new useful approach to discover real life processes in IT Service Management end everywhere else where processes are driven by tools providing log file information.
Many thanks to Anne Rozinat http://fluxicon.com for the graphs and information she provided to itSMF Austria. Many thanks to Celonis for providing a demo application.
Please recognize the further links and recommendations at the end of the presentation.
Process Mining: Understanding and Improving Desire Lines in Big DataWil van der Aalst
We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data”
in honour of doctor honoris causa Wil van der Aalst.
Wednesday May 30th - 10.00 a.m. - 12 a.m.,
Hasselt University, campus Diepenbeek (Agoralaan, building D) - auditorium H5
The Faculty of Business Economics of Hasselt University is pleased to invite you to the lecture
“Process Mining: Understanding and Improving Desire Lines in Big Data”.
This lecture is organised to honour prof. dr. Wil van der Aalst, on whom the degree of ‘doctor honoris causa’ will be conferred by Hasselt University, Faculty of Business Economics (promotor prof. Koen Vanhoof). Professor van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT).His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Many of his ideas have influenced researchers, software developers and standardization committees working on process support.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process Mining - Chapter 12 - Analyzing Spaghetti ProcessesWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Event Logs: What kind of data does process mining require?Wil van der Aalst
The starting point for process mining is an event log. How to get this data in a format suitable for process mining? This slide show will explain this.
Each event in such a log refers to an activity (i.e., a well-defined step in some process) and is related to a particular case (i.e., a process instance). The events belonging to a case are ordered and can be seen as one "run" of the process. Event logs may store additional information about events. In fact, whenever possible, process-mining techniques use extra information such as the resource (i.e., person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g., the size of an order).
Process Mining 2.0: From Insights to ActionsMarlon Dumas
Keynote talk at the workshop on Artificial Intelligence for Enterprise Process Transformation in conjunction with the PAKDD'2021 conference. The talk focuses on the move from process mining as a descriptive analytics approach, to process mining as a predictive and prescriptive analytics technology for automated process improvement.
This presentation introduces the Process Mining as the cutting-edge data analytics approach for discovering the real processes by analyzing the event logs, detecting the bottlenecks, and generating recommendations for enhancing the business performance.
Process Mining: Data Science in Action - Wil van der Aalst, TU/e, DSC/e, HSEYandex
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 to find a connection 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 can include: analyzing treatment processes in hospitals, improving customer service processes in multinational companies, understanding browsing behavior of customers on a booking site, analyzing failures of a baggage handling system, or improving user interface of the X-ray machine. What all of these applications have in common is the need to relate dynamic behavior to process models. Not only does process mining provide a bridge between data mining and business process management, but 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.
Process Mining and AI for Continuous Process ImprovementMarlon Dumas
Talk delivered at BPM Day Rio Grande do Sul on 11 November 2021.
Abstract.
Process mining is a technology that marries methods from business process management and from data science, to support operational excellence and digital transformation. Process mining tools can transform data extracted from enterprise systems, into visualizations and reports that allow managers to improve organizational performance along different dimensions, such as efficiency, quality, and compliance. In this talk, we will give an overview of the capabilities of process mining tools, and we will illustrate the benefits of process mining via several case studies in the fields of insurance, manufacturing, and IT service management.
Process Mining and Predictive Process Monitoring in ApromoreMarlon Dumas
Seminar delivered at University of Hasselt on 14 May 2019. The seminar covers the research efforts underpinning Apromore's automated process discovery, conformance checking, log delta analysis, and predictive process monitoring plugins.
Analítica de datos e inteligencia artificial para procesos de negociosMarlon Dumas
Charla sobre minería de procesos e inteligencia operacional, dictada como parte de la reunión inaugural de la Red de Gerentes de Procesos de Bogotá el 10 de julio del 2019 en la Pontificia Universidad Javeriana.
Data mining Course
Chapter 1
Definition of Data Mining
Data Mining as an Interdisciplinary field
The process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
Learning Accurate Business Process Simulation Models from Event Logs via Auto...Marlon Dumas
Paper presentation at the International Conference on Advanced Information Systems Engineering (CAiSE).
This paper presents an approach to automatically discover business process simulation models from event logs by combining process mining and deep learning techniques.
Paper available at: https://link.springer.com/chapter/10.1007/978-3-031-07472-1_4
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
This Slide Deck was presented at the annual international conference of itSMF Slovensko on May, 6th. in Bratislava. It gives an introduction into Process Mining as a new useful approach to discover real life processes in IT Service Management end everywhere else where processes are driven by tools providing log file information.
Many thanks to Anne Rozinat http://fluxicon.com for the graphs and information she provided to itSMF Austria. Many thanks to Celonis for providing a demo application.
Please recognize the further links and recommendations at the end of the presentation.
Process Mining: Understanding and Improving Desire Lines in Big DataWil van der Aalst
We are pleased to announce the lecture: “Process Mining: Understanding and Improving Desire Lines in Big Data”
in honour of doctor honoris causa Wil van der Aalst.
Wednesday May 30th - 10.00 a.m. - 12 a.m.,
Hasselt University, campus Diepenbeek (Agoralaan, building D) - auditorium H5
The Faculty of Business Economics of Hasselt University is pleased to invite you to the lecture
“Process Mining: Understanding and Improving Desire Lines in Big Data”.
This lecture is organised to honour prof. dr. Wil van der Aalst, on whom the degree of ‘doctor honoris causa’ will be conferred by Hasselt University, Faculty of Business Economics (promotor prof. Koen Vanhoof). Professor van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT).His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Many of his ideas have influenced researchers, software developers and standardization committees working on process support.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Process mining in business process managementRamez Al-Fayez
An overview of Process mining in Business Process Management ...
References :
- Jan Claes, Geert Poels, Process Mining and the ProM Framework: An Exploratory Survey, Business Process Management Conference Workshops, LNBIP 132, p. 187-198, 2012. http://janclaes.info/paper.php?paper=pubbpi2012
Building Information Model (BIM) based process miningStijn van Schaijk
Master Thesis research into BIM based process mining. Enabling knowledge reassurance and fact-based problem discovery within the Architecture, Engineering, Construction and Facility Management Industry.
Process Mining - Chapter 13 - Cartography and NavigationWil van der Aalst
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Slides supporting the book "Process Mining: Discovery, Conformance, and Enhancement of Business Processes" by Wil van der Aalst. See also http://springer.com/978-3-642-19344-6 (ISBN 978-3-642-19344-6) and the website http://www.processmining.org/book/start providing sample logs.
Discovering Petri Nets: Evidence-Based Business Process ManagementWil van der Aalst
Invited Talk for the Carl Adam Petri Memorial Symposium, February 2010, Berlin, Germany
Carl Adam Petri was one of the most influential computer scientists of our time. This symposium commemorated the life and work of Petri. See http://www2.informatik.hu-berlin.de/top/lehre/petriweb/.
Keynote Gartner Business Process Management Summit, February 2009, London Wil van der Aalst
Executive Keynote Gartner Business Process Management Summit
23 – 25 February 2009, London. Title "Process Mining: Beyond Business Intelligence" by Prof. dr. ir. Wil van der Aalst, Professor of Information Systems, Technische Universiteit Eindhoven.
This is something completely NEW, something people said wasn’t possible, that the data wasn’t there to allow systems that really could map out a process; they were wrong. Data is now everywhere; it is accessible, there is an abundance of data and it can provide you with insights you could never find just in interviews. The goal is to get away from workflow systems that are divorced from reality and from how people really work.
Today’s tools oversimplify reality when what you need is a view as close to the real world as possible. Since the 1990s such process tools have been a disappointment; they haven’t covered the true lifecycle. Process mining is a new step which involves seeing how processes are really being executed and using this as an input to allow the design and improvement of processes.
This presentation was given by Dirk Fahland at the International Conference on Business Process Management 2011 (BPM'11) in Clermont-Ferrand, France on 31st August 2011.
Keynote for the Yahoo! Frontend Developer's Summit 2008 held at the Yahoo! campus in Sunnyvale, CA. Looks at lessons from programming from the past and applies to web developer's today.
We at Revolution Analytics are often asked “What is the best way to learn R?” While acknowledging that there may be as many effective learning styles as there are people we have identified three factors that greatly facilitate learning R. For a quick start:
- Find a way of orienting yourself in the open source R world
- Have a definite application area in mind
- Set an initial goal of doing something useful and then build on it
In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R. We will:
- Provide an orientation to R’s data mining resources
- Show how to use the "point and click" open source data mining GUI, rattle, to perform the basic data mining functions of exploring and visualizing data, building classification models on training data sets, and using these models to classify new data.
- Show the simple R commands to accomplish these same tasks without the GUI
- Demonstrate how to build on these fundamental skills to gain further competence in R
- Move away from using small test data sets and show with the same level of skill one could analyze some fairly large data sets with RevoScaleR
Data scientists and analysts using other statistical software as well as students who are new to data mining should come away with a plan for getting started with R.
Process Mining: BPM on Steroids (CPOs@BPM&O 2019 Keynote)Wil van der Aalst
In seinem Vortrag am ersten Veranstaltungstags des CPOs@BPM&O wird Prof. van der Aalst von der RWTH Aachen vorschlagen, das Beste aus beiden Welten zu kombinieren: Hybridprozessmodelle zu entdecken, die formale und informelle Elemente enthalten. Die entdeckten Modelle erlauben formale Argumente, offenbaren aber auch Informationen, die nicht in gängigen formalen Modellen erfasst werden können. Die nächste Welle kommerzieller Process-Mining-Tools wird solche Hybridmodelle verwenden.
In seiner Keynote wird Prof. van der Aalst auch auf seine Zusammenarbeit mit der Industrie eingehen. Er führte Process Mining in über 150 Organisationen an, leitete die Entwicklung des Open-Source-Tools ProM und beeinflusste die über 20 verfügbaren kommerziellen Process Mining-Tools.
Everything You Always Wanted To Know About Petri Nets, But Were Afraid To AskWil van der Aalst
A short tutorial on Petri nets at BPM 2019 in Vienna. Business Process Management (BPM), Process Mining (PM), Workflow Management (WFM), and other approaches aimed at improving processes depend on process models. Business Process Model and Notation (BPMN), Event-driven Process Chains (EPCs), and UML activity diagrams all build on Petri nets and have semantics involving ‘playing the token game’. In addition, process analysis approaches ranging from verification and simulation to process discovery and compliance checking often depend on Petri net theory. For the casual user, there is no need to understand the underlying foundations. However, BPM/PM/WFM researchers and ‘process experts’ working in industry need to understand these foundational results. Unfortunately, the results of 50 years of Petri net research are not easy to digest. This tutorial paper provides, therefore, an entry point into the wonderful world of Petri nets.
Process Mining In Today’s Platforms Economy: Opportunities and Challenges (WI...Wil van der Aalst
Process mining is rapidly becoming a standard way to analyze performance and compliance problems based on event data. Currently, there are more than 30 commercial process-mining tools based on the research by prof. Van der Aalst and his team. The primary enabler for process mining is the increasing digitization of society and business. Tech companies such as Uber, Airbnb, Amazon, Booking, and Alibaba and were able to grow extremely fast due to the digital platforms they provide. Smart homes, production facilities, and energy networks also build on platforms recording the actual behavior or people and machines. All digital platforms have in common that they record event data at an unprecedented level. This allows for all forms of process mining (process discovery, conformance checking, prediction, etc.). Particularly interesting are comparative process mining techniques, i.e., comparing variants of the same process for different groups of customers, periods, locations, etc. However, there are also challenges related to confidentiality and other aspects of responsible data science. In his talk, Wil van der Aalst (“the godfather of process mining”) reflects on the capabilities and limitations of today’s process mining tools and the opportunities and challenges provided by digital platforms.
A Decade of Business Process Management Conferences: Reflections on a Develop...Wil van der Aalst
The Business Process Management (BPM) conference series celebrates its tenth anniversary. This is a nice opportunity to reflect on a decade of BPM research. This talk will describe the history of the conference series through the prism of typical BPM use cases and six key BPM concerns: Process Modeling Languages, Process Enactment Infrastructures, Process Model Analysis, Process Mining, Process Flexibility, and Process Reuse. Although BPM has matured as a research discipline, there are still various important problems that remain open. Moreover, despite the broad interest in BPM, there is significant room for improvement when it comes to the the adoption of state-of-the-art results by software vendors, consultants, and end-users. The BPM discipline should not shy away from the key challenges and set clear targets for the next decade.
Keynote BPM 2012: http://bpm2012.ut.ee/
Prof.dr.ir. Wil van der Aalst is a full professor of Information Systems at the Technische Universiteit Eindhoven (TU/e). Currently he is also an adjunct professor at Queensland University of Technology (QUT) working within the BPM group there. His research interests include workflow management, process mining, Petri nets, business process management, process modeling, and process analysis. Wil van der Aalst has published more than 150 journal papers, 17 books (as author or editor), 300 refereed conference/workshop publications, and 50 book chapters. Many of his papers are highly cited (he has an H-index of more than 92 according to Google Scholar, making him the European computer scientist with the highest H-index) and his ideas have influenced researchers, software developers, and standardization committees working on process support. He has been a co-chair of many conferences including the Business Process Management conference, the International Conference on Cooperative Information Systems, the International conference on the Application and Theory of Petri Nets, and the IEEE International Conference on Services Computing. He is also editor/member of the editorial board of several journals, including the Distributed and Parallel Databases, the International Journal of Business Process Integration and Management, the International Journal on Enterprise Modelling and Information Systems Architectures, Computers in Industry, Business & Information Systems Engineering, IEEE Transactions on Services Computing, Lecture Notes in Business Information Processing, and Transactions on Petri Nets and Other Models of Concurrency. In 2012, he received the degree of doctor honoris causa from Hasselt University. He is also a member of the Royal Holland Society of Sciences and Humanities (Koninklijke Hollandsche Maatschappij der Wetenschappen) and the Academy of Europe (Academia Europaea).
Service Interaction: Patterns, Formalization, and AnalysisWil van der Aalst
Invited Lecture at the 9th International School on Formal Methods for the Design of Computer, Communication and Software Systems: Web Services (SFM-09:WS), Bertinoro, Italy, June 1-6, 2009.
Keynote IEEE Symposium Series on Computational Intelligence (SSCI 2011)/IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), April 2011, Paris, France
Keynote at 18th International Conference on Cooperative Information Systems (...Wil van der Aalst
The Software as a Service (SaaS) paradigm is particularly interesting for situations where many organizations need to support similar processes. For example, municipalities, courts, rental agencies, etc. support highly similar processes. However, despite these similarities, there is also the need to allow for local variations in a controlled manner. Therefore, cloud infrastructures should provide configurable services such that products and processes can be customized while sharing commonalities. Configurable and executable process models are essential to realize such infrastructures. This will finally transform reference models from "paper tigers" (reference modeling a la SAP, ARIS, etc.) into an "executable reality". Moreover, "configurable services in the cloud" enable cross-organizational process mining. This way, organizations can learn from each other and improve their processes.
Unveiling the Secrets How Does Generative AI Work.pdfSam H
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
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Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
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It is a sample of an interview for a business english class for pre-intermediate and intermediate english students with emphasis on the speking ability.
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
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Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
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LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
2. Overview
Chapter 1
Introduction
Part I: Preliminaries
Chapter 2 Chapter 3
Process Modeling and Data Mining
Analysis
Part II: From Event Logs to Process Models
Chapter 4 Chapter 5 Chapter 6
Getting the Data Process Discovery: An Advanced Process
Introduction Discovery Techniques
Part III: Beyond Process Discovery
Chapter 7 Chapter 8 Chapter 9
Conformance Mining Additional Operational Support
Checking Perspectives
Part IV: Putting Process Mining to Work
Chapter 10 Chapter 11 Chapter 12
Tool Support Analyzing “Lasagna Analyzing “Spaghetti
Processes” Processes”
Part V: Reflection
Chapter 13 Chapter 14
Cartography and Epilogue
Navigation
PAGE 1
3. Process discovery
supports/
“world” business
controls
processes software
people machines system
components
organizations records
events, e.g.,
messages,
specifies transactions,
models
configures etc.
analyzes
implements
analyzes
discovery
(process) event
conformance
model logs
enhancement
PAGE 2
4. Challenge
“able to replay event log” “Occam’s razor”
fitness simplicity
process
discovery
generalization precision
“not overfitting the log” “not underfitting the log”
PAGE 3
5. Observing a stable process infinitely long
frequent all behavior
behavior trace in (including noise)
event log
PAGE 4
10. Characteristics of process discovery
algorithms
• Representational bias
− Inability to represent concurrency
− Inability to deal with (arbitrary) loops
− Inability to represent silent actions
− Inability to represent duplicate actions
− Inability to model OR-splits/joins
− Inability to represent non-free-choice behavior
− Inability to represent hierarchy
• Ability to deal with noise
• Completeness notion assumed
• Approach used (direct algorithmic approaches, two-
phase approaches, computational intelligence
approaches, partial approaches, etc.) PAGE 9
11. Examples
• Algorithmic techniques
• Alpha miner
• Alpha+, Alpha++, Alpha#
• FSM miner
• Fuzzy miner
• Heuristic miner
• Multi phase miner
• Genetic process mining
• Single/duplicate tasks
• Distributed GM
• Region-based process mining
• State-based regions
• Language based regions
• Classical approaches not dealing with concurrency
• Inductive inference (Mark Gold, Dana Angluin et al.)
• Sequence mining
PAGE 10
12. Heuristic mining
• To deal with noise and incompleteness.
• To have a better representational bias than the α
algorithm (AND/XOR/OR/skip).
• Uses C-nets.
b
check
policy
a c e
register check close
claim damage case
d
consult
expert
PAGE 11
17. Lower threshold (2 direct successions and
a dependency of at least 0.7)
5(0.83)
b
11(0.92) 11(0.92)
a c e
11(0.92) 11(0.92)
13(0.93) 13(0.93)
d
4(0.80)
PAGE 16
18. Higher threshold (5 direct successions
and a dependency of at least 0.9)
b
11(0.92) 11(0.92)
a c e
11(0.92) 11(0.92)
13(0.93) 13(0.93)
d
PAGE 17
19. Learning splits and joins
5
20 b 20
21
5 20 20 5
20 20 20 20
a c e
40 20 21 20 40
13
13
13 13
13 13
d
4 17
4
4
PAGE 18
20. Alternative visualization
5
20 b 20
21
5 20 20 5
20 20 20 20
a c e
40 20 21 20 40
13
13
13 13
13 13
d b
4 17
4
4
AND AND
a c e
d
PAGE 19
21. Characteristics of heuristic mining
• Can deal with noise and therefore quite robust.
• Improved representational bias.
• Split and join rules are only considered locally
(therefore most of the discovered model are not
sound and require repair actions).
PAGE 20
22. Genetic process mining
create initial
population
event log mutation
next generation
compute
fitness
elitism
termination
tournament children
crossover
select best parents
individual
“dead” individuals
PAGE 21
23. Design decisions
• Representation of individuals
• Initialization
• Fitness function
• Selection strategy (tournament and elitism)
• Crossover create initial
population
• Mutation event log mutation
next generation
compute
fitness
elitism
termination
tournament children
crossover
select best parents
individual
“dead” individuals
PAGE 22
24. Example: crossover
b b
examine examine
thoroughly thoroughly
g g
pay pay
c c
compensation compensation
a e a e
examine examine
start register casually decide end start register casually decide end
request request
h h
d d
reject reject
check ticket request check ticket request
f f
reinitiate reinitiate
request request
b b
examine examine
thoroughly thoroughly
g g
pay pay
c c
compensation compensation
a e a e
examine examine
start register casually decide end start register casually decide end
request request
h h
d d
reject reject
check ticket request check ticket request
f f
reinitiate
reinitiate
request
request
PAGE 23
25. Example: mutation
remove place
b b
examine examine
thoroughly thoroughly
g g
pay pay
c c
compensation compensation
a e a e
examine examine
start register casually decide end start register casually decide end
request request
h h
d d
reject reject
check ticket request check ticket request
f f
reinitiate reinitiate
request
added arc request
PAGE 24
26. Characteristics of genetic
process mining
• Requires a lot of computing power.
• Can be distributed easily.
• Can deal with noise, infrequent behavior, duplicate tasks,
invisible tasks, etc.
• Allows for incremental improvement and combinations
with other approaches (heuristics post-optimization, etc.).
PAGE 25
27. Region-based mining
• Two types of regions theory:
− State-based regions
− Language-based regions
• All about discovering places (like in the α algorithm)!
a1 b1
a2 b2
... p(A,B) ...
am bn
A={a1,a2, … am} B={b1,b2, … bn}
PAGE 26
28. State-based regions
Two steps:
1.Discover a transition system (different abstractions
are possible)
2.Convert transition system into an “equivalent” Petri
net.
PAGE 27
29. Step 1: learning a transition system
current state
trace: abcdcdcde faghhhi
past future
past and future
• past, future, past+future
• sequence, multiset, set abstraction
• limited horizon to abstract further
• filtering e.g. based on transaction type, names, etc.
• labels based on activity name or other features
PAGE 28
30. Past without abstraction (full sequence)
c d
‹a,b›
‹a,b,c› ‹a,b,c,d›
b
a e d
‹› ‹a› ‹a,e› ‹a,e,d›
c
b d
‹a,c›
‹a,c,b› ‹a,c,b,d›
PAGE 29
31. Future without abstraction
a b ‹c,d›
‹a,b,c,d› ‹b,c,d› c
a e d
‹a,e,d› ‹e,d› ‹d › ‹›
b
a c
‹b,d›
‹a,c,b,d› ‹c,b,d›
PAGE 30
32. Past with multiset abstraction
[a,e]
d
[a,d,e]
e [a,b]
a b
[] [a]
c c
b d
[a,c] [a,b,c] [a,b,c,d]
PAGE 31
33. Only last event matters for state
‹e›
e d
a b
‹ b› d
‹› ‹a › c b ‹d›
c d
‹c›
PAGE 32
34. Step 2: constructing a Petri net using
regions
a = enter
b d b = enter
a e c = exit
d = exit
f d e = do not cross
e f = do not cross
e
f c
a
R
a c
e f
pR
b d
PAGE 33
35. Example
d
e
[a,e] [a,d,e]
[ a,b]
a b
[] [a] c
c
b d
[a,c] [a,b,c] [a,b,c,d]
b
a p1 e p3 d
start end
p2 c p4
PAGE 34
36. Language based regions
f c1
a1 b1
e c d
pR
a2 b2
X Y
Region R = (X,Y,c) corresponding to place pR: X = {a1,a2,c1} =
transitions producing a token for pR, Y = {b1,b2,c1} = transitions
consuming a token from pR, and c is the initial marking of pR. PAGE 35
37. Based idea: enough tokens should be
present when consuming
A place is feasible if it
can be added without
f c1 disabling any of the
traces in the event log.
a1 b1
e c d
pR
a2 b2
X Y
PAGE 36
41. Characteristics of region-based mining
• Can be used to discover more complex control-flow
structures.
• Classical approaches need to be adapted
(overfitting!).
• Representational bias can be parameterized (e.g.,
free-choice nets, label splitting, etc.).
• Problems dealing with noise.
PAGE 40
43. Evaluating the discovered process
Fitness: Is the event log
possible according to the
model?
Precision: Is the model Generalization: Is the model
not underfitting (allow for not overfitting (only allow for
too much)? the “accidental” examples)?
Structure: Is this the
simplest model (Occam's
Razor)?
PAGE 42