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.
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.
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.
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
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.
Introduction to Business Process Monitoring and Process MiningMarlon Dumas
Two-day course delivered at the Chinese Business Process Management (BPM) Summer School in Jinan, China, 23-24 August 2018. The course introduces a range of techniques, tools, and algorithms for process monitoring and mining.
Process Mining - Chapter 6 - Advanced Process Discovery_techniquesWil 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 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.
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.
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
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.
Introduction to Business Process Monitoring and Process MiningMarlon Dumas
Two-day course delivered at the Chinese Business Process Management (BPM) Summer School in Jinan, China, 23-24 August 2018. The course introduces a range of techniques, tools, and algorithms for process monitoring and mining.
Process Mining - Chapter 6 - Advanced Process Discovery_techniquesWil 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.
Power BI has in its DNA the goal of enabling everybody to experience their data any way, anywhere—in seconds and at global scale.
Power BI offers a set of capabilities that are uniquely enabled by its global and cloud nature:
The ability to harness data from Excel spreadsheets, on-premises data sources through the data gateway, big data, streaming data, and cloud services. It doesn’t matter what type of data you want or where it lives, Power BI allows you to connect to hundreds of data sources.
Out-of-the box SaaS content packs that deliver a curated experience with pre-built dashboards to get you up and running quickly. We have hundreds of ISVs building content packs to cater to the needs of millions of Power BI users.
Unmatched, unique ways for users to experience their data with speed and agility:
Live dashboards that maintain a real-time pulse on the business and provide critical insights.
Natural language query that enables users to simply and intuitively ask questions of their data, including through Cortana.
Custom visuals that bring data to life and surface intelligence hidden in the sea of data, with our community leveraging the Power BI visualization stack to create new ways to visualize data in a way that makes more sense. (Now available in the Office store.)
Integration of Power BI with the Microsoft stack. Power BI is part of larger ecosystem that integrates with services like Microsoft Teams, Office 365, and Dynamics 365. These services are aware of Power BI, are wired to Power BI, and enable you to use Power BI in the context of your work.
Anywhere access to insights. Whether in the office or on-the-go, Power BI provides anywhere access to insights with dashboards accessible via the desktop, on the web, or across mobile devices. Inside Excel, embedded—we have hundreds of ISVs embedding Power BI in their offerings.
Forfatterne til en ny bog om gevinstrealisering tager dig gennem en præsentation med inspiration og udvalgte værktøjer og metoder. Fokus er på gennemførelse af projektet eller programmet med særligt fokus på gevinster og fleksibilitet i leverancerne. Forandring handler nemlig grundlæggende om at ændre adfærd. Når vi lykkes med at ændre adfærd, er vi godt på vej til også at kunne realisere gevinsten af indsatsen.
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With Dynamics 365 Business Central, no longer have to store data in separate systems that don’t work together, it’s all integrated. Contact us for more details or visit us at https://www.inovarconsulting.co.in/.
Get an overview of Dataflows and how it integrates data lake and ETL technology directly into Power BI to enable anyone with Power Query skills.
Before diving into details we will go through the architecture and demonstrate the bigger picture for Dataflows in Power BI.
We will go through how you can create, customize and manage data within the Power BI experience in a simpler way. Part of this will also be to go through Common Data Models which contains the business entities across your organisation.
This will help your organisation simplifying modeling and is intended to prevent multiple definition for the same data.
45 mins presentation @ the MVP Cloud RoadShow on April 11, 2015
In preview and recently available in Canada, Power BI. Insights are hiding in your company's data - see the impact of bringing them into focus with Power BI. Let Power BI organize your data. See it all in one place and make better decisions. The metrics you need to run your business on a dashboard. Make confident decisions knowing everyone is on the same page.
Overview of Microsoft Appliances: Scaling SQL Server to Hundreds of TerabytesJames Serra
Learn how SQL Server can scale to HUNDREDS of terabytes for BI solutions. This session will focus on Fast Track Solutions and Appliances, Reference Architectures, and Parallel Data Warehousing (PDW). Included will be performance numbers and lessons learned on a PDW implementation and how a successful BI solution was built on top of it using SSAS.
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.
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.
Power BI has in its DNA the goal of enabling everybody to experience their data any way, anywhere—in seconds and at global scale.
Power BI offers a set of capabilities that are uniquely enabled by its global and cloud nature:
The ability to harness data from Excel spreadsheets, on-premises data sources through the data gateway, big data, streaming data, and cloud services. It doesn’t matter what type of data you want or where it lives, Power BI allows you to connect to hundreds of data sources.
Out-of-the box SaaS content packs that deliver a curated experience with pre-built dashboards to get you up and running quickly. We have hundreds of ISVs building content packs to cater to the needs of millions of Power BI users.
Unmatched, unique ways for users to experience their data with speed and agility:
Live dashboards that maintain a real-time pulse on the business and provide critical insights.
Natural language query that enables users to simply and intuitively ask questions of their data, including through Cortana.
Custom visuals that bring data to life and surface intelligence hidden in the sea of data, with our community leveraging the Power BI visualization stack to create new ways to visualize data in a way that makes more sense. (Now available in the Office store.)
Integration of Power BI with the Microsoft stack. Power BI is part of larger ecosystem that integrates with services like Microsoft Teams, Office 365, and Dynamics 365. These services are aware of Power BI, are wired to Power BI, and enable you to use Power BI in the context of your work.
Anywhere access to insights. Whether in the office or on-the-go, Power BI provides anywhere access to insights with dashboards accessible via the desktop, on the web, or across mobile devices. Inside Excel, embedded—we have hundreds of ISVs embedding Power BI in their offerings.
Forfatterne til en ny bog om gevinstrealisering tager dig gennem en præsentation med inspiration og udvalgte værktøjer og metoder. Fokus er på gennemførelse af projektet eller programmet med særligt fokus på gevinster og fleksibilitet i leverancerne. Forandring handler nemlig grundlæggende om at ændre adfærd. Når vi lykkes med at ændre adfærd, er vi godt på vej til også at kunne realisere gevinsten af indsatsen.
Microsoft Dynamics 365 Business Central | Inovar ConsultingInovar Tech
With Dynamics 365 Business Central, no longer have to store data in separate systems that don’t work together, it’s all integrated. Contact us for more details or visit us at https://www.inovarconsulting.co.in/.
Get an overview of Dataflows and how it integrates data lake and ETL technology directly into Power BI to enable anyone with Power Query skills.
Before diving into details we will go through the architecture and demonstrate the bigger picture for Dataflows in Power BI.
We will go through how you can create, customize and manage data within the Power BI experience in a simpler way. Part of this will also be to go through Common Data Models which contains the business entities across your organisation.
This will help your organisation simplifying modeling and is intended to prevent multiple definition for the same data.
45 mins presentation @ the MVP Cloud RoadShow on April 11, 2015
In preview and recently available in Canada, Power BI. Insights are hiding in your company's data - see the impact of bringing them into focus with Power BI. Let Power BI organize your data. See it all in one place and make better decisions. The metrics you need to run your business on a dashboard. Make confident decisions knowing everyone is on the same page.
Overview of Microsoft Appliances: Scaling SQL Server to Hundreds of TerabytesJames Serra
Learn how SQL Server can scale to HUNDREDS of terabytes for BI solutions. This session will focus on Fast Track Solutions and Appliances, Reference Architectures, and Parallel Data Warehousing (PDW). Included will be performance numbers and lessons learned on a PDW implementation and how a successful BI solution was built on top of it using SSAS.
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.
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.
SF Big Data Science Meetup 12.15.15
Scripts here: https://github.com/h2oai/h2o-meetups/tree/master/2015_12_15_MessyDataMeetup
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Mining Frequent Closed Graphs on Evolving Data StreamsAlbert Bifet
Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this talk we present a framework for studying graph pattern mining on time-varying streams and large datasets.
Arno candel scalabledatascienceanddeeplearningwithh2o_odsc_boston2015Sri Ambati
http://opendatascicon.com/schedule/scalable-data-science-and-deep-learning-with-h2o/
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Scalable Data Science and Deep Learning with H2Oodsc
The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
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/.
Los IPython Notebooks nos han proporcionado una sustancial mejora en la documentación del scripts, así como su inspección y una mayor re-utilización. Los IPython Notebooks también permiten acceder a distintos lenguajes de programación (Fortran, IDL, R, Shell,..) en un mismo script, lo que unido a su modo de acceso Web les hace ser un elemento ideal para el trabajo colaborativo (multi-lenguaje, multi-usuario, multi-plataforma, etc..) Os contaré qué tipo de cosas pueden hacerse con IPython Notebooks, desde desarrollo colaborativo de código multi-lenguaje, pasando por la reutilización de tutoriales, visualización interactiva de resultados, hasta la distribución de código más modular, y la publicación final de un experimento digital verificable y reproducible: el preámbulo de los papers ejecutables.
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.
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).
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 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.
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.
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.
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.
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It is crucial for the taxpayers to understand about the TDS Return Filing Due Date, so that they can fulfill your TDS obligations efficiently. Taxpayers can avoid penalties by sticking to the deadlines and by accurate filing of TDS. Timely filing of TDS will make sure about the availability of tax credits. You can also seek the professional guidance of experts like Legal Pillers for timely filing of the TDS Return.
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2. Let’s Play: Play-Out, Play-In, Replay
Big Data
Desire Lines
Process Mining
How Good is My Model?
Process Discovery
Conformance Checking
Food for Thought: Lasagna and Spaghetti
Google Maps and TomTom
How to Get Started?
Conclusion
PAGE 2
19. “All of the world's music
Big Data can be stored on a
$600 disk drive.”
“Enterprises globally
stored more than 7
exabytes
of new data on disk
drives in 2010, while
consumers stored
more
than 6 exabytes of
new data on devices
such as PCs and “Indeed, we are
notebooks.” generating so much
data today that it is
physically impossible to
store it all. Health care
providers, for instance,
discard 90 percent of
the data that they
generate.”
Source: “Big Data: The Next Frontier for Innovation, Competition, and
Productivity” McKinsey Global Institute, 2011.
PAGE 19
20. Hilbert and Lopez. The World's Technological Capacity to Store, Communicate, and
Compute Information. Science, 332(6025):60-65, 2011.
PAGE 20
28. Process Mining =
Event Data + Processes
Data Mining + Process Analysis
Machine Learning + Formal Methods
PAGE 28
29. Process Mining
supports/
“world” business
controls
processes software
people machines system
components
organizations records
events, e.g.,
messages,
specifies transactions,
models configures
analyzes etc.
implements
analyzes
discovery
(process) event
conformance
model logs
enhancement
31. Simplified event log
a = register request,
b = examine thoroughly,
c = examine casually,
d = check ticket,
e = decide,
f = reinitiate request,
g = pay compensation,
and h = reject request
PAGE 31
32. Process
discovery
b
examine
thoroughly
g
c1 c3 pay
c compensation
a examine
e
start register casually decide c5 end
request
h
c2 d c4 reject
check ticket request
f
reinitiate
request
PAGE 32
33. Conformance
checking
b case 7: e is
executed
examine without
thoroughly case 8: g or h
being g is missing
enabled
c1 c3 pay
c compensation
a examine
e
start register casually decide c5 end
request case 10: e h
is missing in
c2 d c4 reject
second
check ticket round request
f
reinitiate
request PAGE 33
34. Extension: Adding perspectives to
model based on event log
The event log can be used to
discover roles in the organization
(e.g., groups of people with similar
work patterns). These roles can be Performance information (e.g., the
used to relate individuals and average time between two
activities. subsequent activities) can be
extracted from the event log and
visualized on top of the model .
Role A: Role E: Role M:
Assistant Expert Manager
Decision rules (e.g., a decision tree
based on data known at the time a
Pete Sue Sara particular choice was made) can be
learned from the event log and used
Mike Sean to annotated decisions.
Ellen E
b
A
examine
thoroughly
A g
A M
c1 c3 pay
c compensation
a examine
e
A
start register casually
A decide c5 end
request
h
c2 d c4 M reject
check ticket request
f
reinitiate
PAGE 34
request
36. Four Competing Quality Criteria
“able to replay event log” “Occam’s razor”
fitness simplicity
process
discovery
generalization precision
“not overfitting the log” “not underfitting the log”
PAGE 36
37. Example: one log four models
b
examine
thoroughly
g
pay
c compensation
a examine e
start register casually decide end
# trace
request
h 455 acdeh
d reject
check ticket request 191 abdeg
f reinitiate
request 177 adceh
N1 : fitness = +, precision = +, generalization = +, simplicity = +
144 abdeh
111 acdeg
a c d e h
82 adceg
start register examine check decide reject end
request casually ticket request
56 adbeh
N2 : fitness = -, precision = +, generalization = -, simplicity = +
47 acdefdbeh
“able to replay event log” “Occam’s razor”
38 adbeg
examine check
thoroughly b d ticket g
fitness simplicity pay
compensation
33 acdefbdeh
a 14 acdefbdeg
start register examine
c end 11 acdefdbeg
request casually
e f
reinitiate h
process decide request reject
request
9 adcefcdeh
discovery N3 : fitness = +, precision = -, generalization = +, simplicity = + 8 adcefdbeh
5 adcefbdeg
a d c e g
3 acdefbdefdbeg
generalization precision register
request
check
ticket
examine
casually
decide pay
compensation
2 adcefdbeg
a c d e g 2 adcefbdefbdeg
“not overfitting the log” “not underfitting the log” register examine check decide pay
request casually ticket compensation 1 adcefdbefbdeh
a d c e h 1 adbefbdefdbeg
register check examine decide reject
request ticket casually request 1 adcefdbefcdefdbeg
a c d e h 1391
start end
register examine check decide reject
request casually ticket request
… (all 21 variants seen in the log )
a b d e g
register examine check decide pay
request thoroughly ticket compensation
a d b e h
register check examine decide reject
request ticket thoroughly request
a b d e h
register examine check decide reject
request thoroughly ticket request PAGE 37
N4 : fitness = +, precision = +, generalization = -, simplicity = -
38. # trace
455 acdeh
Model N1 191 abdeg
177 adceh
144 abdeh
111 acdeg
82 adceg
56 adbeh
b 47 acdefdbeh
examine
thoroughly 38 adbeg
g 33 acdefbdeh
pay
c compensation 14 acdefbdeg
a examine e
11 acdefdbeg
start register casually decide end
request 9 adcefcdeh
h
d reject 8 adcefdbeh
check ticket request 5 adcefbdeg
f reinitiate 3 acdefbdefdbeg
request
N1 : fitness = +, precision = +, generalization = +, simplicity = + 2 adcefdbeg
2 adcefbdefbdeg
1 adcefdbefbdeh
1 adbefbdefdbeg
1 adcefdbefcdefdbeg
PAGE 38
1391
40. # trace
455 acdeh
Model N3 191 abdeg
177 adceh
144 abdeh
111 acdeg
82 adceg
56 adbeh
47 acdefdbeh
examine check
thoroughly b d ticket g 38 adbeg
pay 33 acdefbdeh
compensation
a 14 acdefbdeg
start register examine end 11 acdefdbeg
request casually c
e f reinitiate
reject
h 9 adcefcdeh
decide request
request 8 adcefdbeh
N3 : fitness = +, precision = -, generalization = +, simplicity = +
5 adcefbdeg
3 acdefbdefdbeg
2 adcefdbeg
2 adcefbdefbdeg
1 adcefdbefbdeh
1 adbefbdefdbeg
1 adcefdbefcdefdbeg
PAGE 40
1391
41. # trace
455 acdeh
Model N4 191 abdeg
177 adceh
144 abdeh
a d c e g 111 acdeg
register check examine decide pay
request ticket casually compensation 82 adceg
a c d e g 56 adbeh
register examine check decide pay
request casually ticket compensation 47 acdefdbeh
a d c e h 38 adbeg
register check examine decide reject
request ticket casually request 33 acdefbdeh
a c d e h 14 acdefbdeg
start end
register examine check decide reject
request casually ticket request 11 acdefdbeg
… (all 21 variants seen in the log )
9 adcefcdeh
8 adcefdbeh
5 adcefbdeg
a b d e g
register examine check decide pay 3 acdefbdefdbeg
request thoroughly ticket compensation
2 adcefdbeg
a d b e h
register check examine decide reject 2 adcefbdefbdeg
request ticket thoroughly request
1 adcefdbefbdeh
a b d e h
register examine check decide reject 1 adbefbdefdbeg
request thoroughly ticket request
1 adcefdbefcdefdbeg
N 4 : fitness = +, precision = +, generalization = -, simplicity = -
PAGE 41
1391
44. Petri net view:
Just discover the places …
“able to replay event log” “Occam’s razor”
fitness simplicity
process
discovery
generalization precision
“not overfitting the log” “not underfitting the log”
a1 b1
a2 b2
... p(A,B) ...
am bn Adding a place limits behavior:
•overfitting ≈ adding too many places
•underfitting ≈ adding too few places
A={a1,a2, … am} B={b1,b2, … bn}
PAGE 44
45. Example: Process Discovery Using
State-Based Regions
01011001101101001
01111110110100011
01100111101110000
01101101001001100 d
e
[a,e] [a,d,e]
[ a,b]
a b
event log [] [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 45
46. Example of State-Based Region
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]
enter: b,e
leave: d
do-not-cross: a,c
b
a p1 e p3 d
start end
p2 c p4
PAGE 46
47. Example: Process Discovery Using
Language-Based Regions
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 47
48. Example of Language-Based Regions
• accd
• bd ↓accd : 0 + 0 - 0 ≥ 0
c
• bce a↓ccd : 0 + 1 - 1 ≥ 0
• ace
b d ac↓cd : 0 + 2 - 2 ≥ 0
• acd
acc↓d : 0 + 3 - 3 ≥ 0
• bcce
• ade
a e
↓ade : 0 + 0 - 0 ≥ 0
X Y a↓de : 0 + 1 - 1 ≥ 0
ad↓e : 0 + 1 - 2 < 0
PAGE 48
49. Example of a completely different process
discovery technique: Genetic Mining
PAGE 49
50. Genetic process mining: Overview
create initial
population
event log mutation
next generation
compute
fitness
elitism
termination
tournament children
crossover
select best parents
individual
“dead” individuals
PAGE 50
51. 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 51
52. 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 52
54. Replaying trace “abeg”
a b e g b
examine
thoroughly
g
pay
c compensation
a examine e
start register casually decide end
request r=1
h
d m=1
reject
check ticket request
f reinitiate
request
1 1
= 0.83333
6 6
PAGE 54
55. # trace
455 acdeh
Can be lifted to log level 191 abdeg
177 adceh
N1 b 144 abdeh
examine
thoroughly
g
111 acdeg
p1
c p3 pay
compensation
82 adceg
a examine e
start register casually decide p5 end 56 adbeh
request
h
p2 d p4 reject 47 acdefdbeh
check ticket request
f reinitiate 38 adbeg
request
N2 b pay 33 acdefbdeh
compensation
examine g
thoroughly 14 acdefbdeg
a c d e
start register p1 examine p2 check p3
p4 11 acdefdbeg
decide end
request casually ticket
h 9 adcefcdeh
f reject request
reinitiate request 8 adcefdbeh
N3 c
p1 examine p3
5 adcefbdeg
casually
a e h 3 acdefbdefdbeg
start register decide p5 reject end
request
d request 2 adcefdbeg
p2 check p4
ticket 2 adcefbdefbdeg
N4 examine
b d check
1 adcefdbefbdeh
thoroughly ticket g
pay
compensation 1 adbefbdefdbeg
a p1
start register examine
c end 1 adcefdbefcdefdbeg
request casually
e f
reinitiate
reject
h PAGE 55
decide request
request 1391
56. From “playing the token game” to
optimal alignments …
observed trace: “abeg”
a b » e g
a b d e g
b
examine
thoroughly
g
pay
move in a
c
examine e
compensation
model only start register
request
casually decide
h
end
d reject
check ticket request
f reinitiate
request
PAGE 56
57. Another alignment
observed trace: “abcdeg”
a b c d e g
a b » d e g
b
examine
thoroughly
g
pay
c compensation
a examine e
start register casually decide end
move in log request
d
h
reject
only check ticket
f
request
reinitiate
request
PAGE 57
58. Moves in an alignment
move in log
trace in
event log
a b » d e g
a » c d e g
possible run
of model
move in
model move in both
Optimal alignment describes modeled behavior closest to
observed behavior PAGE 58
59. Moves have costs
… a … … » …
… » … … a …
… a … … a …
… a … … b …
• Standard cost function:
− c(x,») = 1
− c(»,y) = 1
− c(x,y) = 0, if x=y
− c(x,y) = ∞, if x≠y PAGE 59
60. Non-fitting trace: abefdeg
b
examine
thoroughly
g
abefdeg
pay
c compensation
a examine e
start register casually decide end
request
h
d reject
check ticket request
f reinitiate
request
a b » e f d » e g
2
a b d e f d b e g
a b e f d e g
2
a b » » d e g
PAGE 60
61. Any cost structure is possible
… send-letter(John,2 …
weeks, $400)
… send-email(Sue,3 …
weeks,$500)
• Similar activities (more similarity implies lower costs).
• Resource conformance (done by someone that does
not have the specified role).
• Data conformance (path is not possible for this
customer).
• Time conformance (missed the legal deadline)
PAGE 61
62. b
examine
thoroughly
g
pay
c
Fitness
compensation
1.0
a examine e
start register casually decide end
# trace
request
h 455 acdeh
d reject
check ticket request 191 abdeg
f reinitiate
request 177 adceh
N1 : fitness = +, precision = +, generalization = +, simplicity = +
144 abdeh
111 acdeg
a c d e h
0.8
82 adceg
Our A* algorithm start register
request
examine
casually
check
ticket
N2 : fitness = -, precision = +, generalization = -, simplicity = +
decide reject
request
end
56 adbeh
exploits the Petri net 47 acdefdbeh
38 adbeg
marking equation examine
thoroughly b d check
ticket
pay
g 33 acdefbdeh
and uses other compensation
14 acdefbdeg
1.0
a
start register examine 11 acdefdbeg
“tricks” to prune the c end
request casually
e f
reinitiate h
decide request reject 9 adcefcdeh
request
search space. N3 : fitness = +, precision = -, generalization = +, simplicity = + 8 adcefdbeh
5 adcefbdeg
a d c e g
3 acdefbdefdbeg
register check examine decide pay
request ticket casually compensation
2 adcefdbeg
a c d e g 2 adcefbdefbdeg
register examine check decide pay
request casually ticket compensation 1 adcefdbefbdeh
a d c e h 1 adbefbdefdbeg
register check examine decide reject
request ticket casually request 1 adcefdbefcdefdbeg
1.0 start
a
register
request
c
examine
casually
d
check
ticket
e
decide
h
reject
request
end
1391
… (all 21 variants seen in the log )
a b d e g
examine
Aligned event log is
register check decide pay
request thoroughly ticket compensation
a d b e h
starting point for other register
request
check
ticket
examine
thoroughly
decide reject
request
types of analysis. a
register
b d
check
e
decide
h
reject
examine
request thoroughly ticket request
PAGE 62
N4 : fitness = +, precision = +, generalization = -, simplicity = -
65. We applied ProM in >100 organizations
• Municipalities (e.g., Alkmaar, Heusden, Harderwijk, etc.)
• Government agencies (e.g., Rijkswaterstaat, Centraal
Justitieel Incasso Bureau, Justice department)
• Insurance related agencies (e.g., UWV)
• Banks (e.g., ING Bank)
• Hospitals (e.g., AMC hospital, Catharina hospital)
• Multinationals (e.g., DSM, Deloitte)
• High-tech system manufacturers and their customers
(e.g., Philips Healthcare, ASML, Ricoh, Thales)
• Media companies (e.g. Winkwaves)
• ...
PAGE 65
66. How can process mining help?
• Uncover bottlenecks • Provide new insights
• Detect deviations • Highlight important
• Performance measurement problems
• Auditing/compliance • An organization’s mirror
• Business Process (in two ways)
Redesign (BPR) • Helps to avoid ICT
• Continuous improvement failures
(Six Sigma) • Avoid “management by
• Operational support (e.g., PowerPoint”
recommendation and • From “politics” to
prediction) “analytics”
PAGE 66
68. Example of a Lasagna process: WMO
process of a Dutch municipality
Each line corresponds to one of the 528 requests that were handled
in the period from 4-1-2009 until 28-2-2010. In total there are 5498
events represented as dots. The mean time needed to handled a
case is approximately 25 days. PAGE 68
69. WMO process
(Wet Maatschappelijke Ondersteuning)
• WMO refers to the social support act that came into
force in The Netherlands on January 1st, 2007.
• The aim of this act is to assist people with disabilities
and impairments. Under the act, local authorities are
required to give support to those who need it, e.g.,
household help, providing wheelchairs and
scootmobiles, and adaptations to homes.
• There are different processes for the different kinds of
help. We focus on the process for handling requests
for household help.
• In a period of about one year, 528 requests for
household WMO support were received.
• These 528 requests generated 5498 events.
PAGE 69
75. Conformance check WMO process (3/3)
The fitness of the discovered process
is 0.99521667. Of the 528 cases, 496
cases fit perfectly whereas for 32
cases there are missing or remaining
tokens.
PAGE 75
78. Bottleneck analysis WMO process (3/3)
flow time of
approx. 25 days
with a standard
deviation of
approx. 28
PAGE 78
79. Two additional Lasagna processes
RWS
(“Rijkswaterstaat”)
process
WOZ (“Waardering
Onroerende Zaken”)
process
PAGE 79
80. RWS Process
• The Dutch national public works department, called
“Rijkswaterstaat” (RWS), has twelve provincial offices.
We analyzed the handling of invoices in one of these
offices.
• The office employs about 1,000 civil servants and is
primarily responsible for the construction and
maintenance of the road and water infrastructure in its
province.
• To perform its functions, the RWS office subcontracts
various parties such as road construction companies,
cleaning companies, and environmental bureaus. Also,
it purchases services and products to support its
construction, maintenance, and administrative
activities. PAGE 80
82. Social network constructed based on
handovers of work
Each of the 271 nodes
corresponds to a civil
servant. Two civil servants
are
connected if one executed
an activity causally
following an activity
executed by the other civil
servant
PAGE 82
83. Social network consisting of civil servants that
executed more than 2000 activities in a 9 month period.
The darker arcs
indicate the strongest
relationships in the
social network.
Nodes having the same
color belong to the
same clique.
PAGE 83
84. WOZ process
• Event log containing information about 745 objections
against the so-called WOZ (“Waardering Onroerende
Zaken”) valuation.
• Dutch municipalities need to estimate the value of
houses and apartments. The WOZ value is used as a
basis for determining the real-estate property tax.
• The higher the WOZ value, the more tax the owner needs
to pay. Therefore, there are many objections (i.e.,
appeals) of citizens that assert that the WOZ value is too
high.
• “WOZ process” discovered for another municipality (i.e.,
different from the one for which we analyzed the WMO
process).
PAGE 84
85. Discovered process model
The log contains events related to 745 objections against the so-
called WOZ valuation. These 745 objections generated 9583 events.
There are 13 activities. For 12 of these activities both start and
complete events are recorded. Hence, the WF-net has 25
PAGE 85
transitions.
87. Performance analysis
bottleneck detection: places are
colored based on average durations
time required to
move from one
activity to another
information on
total flow time
PAGE 87
90. Example of a Spaghetti process
Spaghetti process describing the diagnosis and treatment of 2765 patients
in a Dutch hospital. The process model was constructed based on an event
log containing 114,592 events. There are 619 different activities (taking
event types into account) executed by 266 different individuals (doctors,
nurses, etc.).
PAGE 90
92. Another example
(event log of Dutch housing agency)
The event log contains 208
cases that generated 5987
events. There are 74
different activities.
PAGE 92
97. Business information systems should
offer “TomTom” functionality
Recommend: How to get home ASAP? Take a left turn!
Detect: You drive too fast!
Predict: When will I be home? At 11.26! PAGE 97
99. Hundreds of plug-ins available covering
the whole process mining spectrum
open-source (L-GPL)
Download from: www.processmining.org PAGE 99
100. How to Get Started?
Collect event data Collect questions
• Minimal requirement: • What kind problems would
events referring to an you like to address (cost,
activity name and a time, risk, compliance,
process instance. service, etc.)?
• Good to have: • Related to discovery,
timestamps, resource conformance,
information, additional enhancement?
data elements. • Iterative process: can be
• Challenges: scoping and “curiosity driven” initially.
sometimes correlation.
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