Research paper presentation at the 35th International Conference on Conceptual Modeling (ER'2016), Gifu, Japan, 15 Nov. 2016
Presentation delivered by Raffaele Conforti.
Paper available at: http://goo.gl/5EN3l2
Process Mining and Predictive Process MonitoringMarlon Dumas
This document discusses process mining and predictive process monitoring. It begins with an overview of offline process mining techniques like process discovery, conformance checking, and deviance mining. It then discusses applying these techniques online for predictive process monitoring, including predicting outcomes, deviations, or failures. Various techniques are presented like nearest neighbor classification of partial traces and clustering traces before classification. The goal is to accurately predict outcomes during process execution based on control flow, data attributes, and textual case data.
Split Miner: Discovering Accurate and Simple Business Process Models from Eve...Marlon Dumas
Paper presentation delivered by Adriano Augusto at the IEEE International Conference on Data Mining (ICDM'2017) on 21 November 2017. The paper is available at: http://kodu.ut.ee/~dumas/pubs/icdm2017-split-miner.pdf
Learning Accurate LSTM Models of Business ProcessesMarlon Dumas
Presentation delivered at the 17th International Conference on Business Process Management (BPM), Vienna, Austria, 3 September 2019. Paper available at: http://kodu.ut.ee/~dumas/pubs/bpm2019lstm.pdf
Presenter: Manuel Camargo
Beyond Tasks and Gateways: Automated Discovery of BPMN Models with Subprocess...Marlon Dumas
Paper presentation at the 12th International BPM Conference, Eindhoven, The Netherlands, September 2014. The corresponding paper can be found at: http://math.ut.ee/~dumas/pubs/bpm2014bpmnminer.pdf
Multi-Perspective Comparison of Business Processes Variants Based on Event LogsMarlon Dumas
This document presents a method for multi-perspective comparison of business process variants based on event logs. The method involves constructing perspective graphs from different abstractions of event logs to analyze processes from different perspectives based on event attributes. Differential perspective graphs are then used to identify statistically significant differences between two event logs, representing different process variants. The method was experimentally applied to compare differences between divisions in an IT incident handling process using various abstractions and observations. The experiments revealed differences in activity statuses, control flows between countries, and control flow frequencies over time between the divisions.
Process Mining Reloaded: Event Structures as a Unified Representation of Proc...Marlon Dumas
Keynote talk at the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2015).
Screencast available at: https://youtu.be/9bQr0r_WaoE
Process Mining and Predictive Process MonitoringMarlon Dumas
Presentation delivered at the Second Colombian Forum on Business Process Management, University of Los Andes, Bogotá, 22 June 2018 - https://sistemas.uniandes.edu.co/en/foros-isis/temas-foros-isis/bpm/foro-2/80-foros-isis/bpm
Process Mining and Predictive Process MonitoringMarlon Dumas
This document discusses process mining and predictive process monitoring. It begins with an overview of offline process mining techniques like process discovery, conformance checking, and deviance mining. It then discusses applying these techniques online for predictive process monitoring, including predicting outcomes, deviations, or failures. Various techniques are presented like nearest neighbor classification of partial traces and clustering traces before classification. The goal is to accurately predict outcomes during process execution based on control flow, data attributes, and textual case data.
Split Miner: Discovering Accurate and Simple Business Process Models from Eve...Marlon Dumas
Paper presentation delivered by Adriano Augusto at the IEEE International Conference on Data Mining (ICDM'2017) on 21 November 2017. The paper is available at: http://kodu.ut.ee/~dumas/pubs/icdm2017-split-miner.pdf
Learning Accurate LSTM Models of Business ProcessesMarlon Dumas
Presentation delivered at the 17th International Conference on Business Process Management (BPM), Vienna, Austria, 3 September 2019. Paper available at: http://kodu.ut.ee/~dumas/pubs/bpm2019lstm.pdf
Presenter: Manuel Camargo
Beyond Tasks and Gateways: Automated Discovery of BPMN Models with Subprocess...Marlon Dumas
Paper presentation at the 12th International BPM Conference, Eindhoven, The Netherlands, September 2014. The corresponding paper can be found at: http://math.ut.ee/~dumas/pubs/bpm2014bpmnminer.pdf
Multi-Perspective Comparison of Business Processes Variants Based on Event LogsMarlon Dumas
This document presents a method for multi-perspective comparison of business process variants based on event logs. The method involves constructing perspective graphs from different abstractions of event logs to analyze processes from different perspectives based on event attributes. Differential perspective graphs are then used to identify statistically significant differences between two event logs, representing different process variants. The method was experimentally applied to compare differences between divisions in an IT incident handling process using various abstractions and observations. The experiments revealed differences in activity statuses, control flows between countries, and control flow frequencies over time between the divisions.
Process Mining Reloaded: Event Structures as a Unified Representation of Proc...Marlon Dumas
Keynote talk at the 36th International Conference on Application and Theory of Petri Nets and Concurrency (Petri Nets 2015).
Screencast available at: https://youtu.be/9bQr0r_WaoE
Process Mining and Predictive Process MonitoringMarlon Dumas
Presentation delivered at the Second Colombian Forum on Business Process Management, University of Los Andes, Bogotá, 22 June 2018 - https://sistemas.uniandes.edu.co/en/foros-isis/temas-foros-isis/bpm/foro-2/80-foros-isis/bpm
In Processes We Trust: Privacy and Trust in Business ProcessesMarlon Dumas
This document discusses challenges and opportunities around privacy and trust in business processes. It begins by defining key concepts like security, privacy, and trust. It then outlines topics related to business process security and privacy, such as access control, flow analysis to detect unauthorized data access, and privacy-aware business process execution. The document proposes approaches for privacy-aware business processes using techniques like k-anonymization and multi-party computation. It describes a system called Pleak.io that aims to model stakeholders, data flows, and privacy-enhancing technologies to quantify privacy leaks and accuracy loss in processes. The document concludes by discussing challenges around collaborative processes with untrusted parties and the potential use of distributed ledgers and smart contracts to address issues of
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.
Business Process Analytics: From Insights to PredictionsMarlon Dumas
Keynote talk at the 13th Baltic Conference on Databases and Information Systems, Trakai, Lithuania, 2 July 2018.
Abstract
Business process analytics is a body of methods for analyzing data generated by the execution of business processes in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining) allow us to understand how a given business process is actually executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This keynote will present a framework for conceptualizing business process analytics methods and applications. The talk will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities.
Process Mining 2.0: From Insights to ActionsMarlon Dumas
The document discusses several topics in process mining research including predictive process monitoring, prescriptive process monitoring, robotic process mining, data-driven simulation, and causal process mining. It provides references for further research on each topic, with links to relevant papers that outline techniques in each area.
Apromore: Advanced Business Process Analytics on the CloudMarlon Dumas
Tutorial delivered at the 16th International Conference on Business Process Management (BPM'2018), Sydney, Australia, 13 September 2018. The tutorial provides an introduction to process mining and predictive process monitoring using Apromore
Predictive Process Monitoring with Hyperparameter OptimizationMarlon Dumas
1. The document presents a predictive process monitoring framework that is enhanced with technique and hyperparameter optimization.
2. The framework evaluates multiple configurations of machine learning techniques and their hyperparameters on historical execution traces to identify the most suitable configuration for a given prediction problem and dataset.
3. An evaluation of the framework on two prediction problems and datasets found that it was able to identify suitable configurations within a reasonable time frame, though the best configuration varied depending on the prediction problem and users' performance criteria.
White-box prediction of process performance indicators via flow analysisMarlon Dumas
Presentation delivered by Ilya Verenich at the International Conference on Software Processes (ICSSP'2017), Paris, France, July 2017. This paper received the best paper award at the conference. Paper available at: http://kodu.ut.ee/~dumas/pubs/icssp2017whitebox.pdf
1) The document proposes a novel approach to using mathematical formulas to revise business processes modeled with Petri nets. The goal is to reduce cost and cycle time by reducing the number of tasks needed to complete the process.
2) The approach generates alternative business process designs by excluding certain transitions in the Petri net model. Excluded transitions are evaluated based on minimizing duration.
3) Results show the proposed approach can efficiently optimize process attributes like cost and duration. It was able to reduce the duration of the case study process by 12.5% by removing unnecessary tasks.
Complete and Interpretable Conformance Checking of Business ProcessesMarlon Dumas
This document presents a new approach for conformance checking of business processes that identifies all differences between a process model and an event log. It generates natural language statements to describe each difference. The approach works by translating the model and log into prime event structures and extracting mismatches by comparing their partially synchronized product. It can identify seven elementary mismatch patterns to characterize deviations. The approach was implemented in a standalone Java tool and evaluated on a real-life process with over 150,000 event traces.
Predictive Business Process Monitoring with Structured and Unstructured DataMarlon Dumas
Presentation delivered by Irene Teinemaa at the BPM'2016 conference, Rio de Janeiro, 22 September 2016. Paper available at: http://kodu.ut.ee/~dumas/pubs/bpm2016predictivemonitoring.pdf
Evidence-Based Business Process ManagementMarlon Dumas
1. The document discusses trends in business process management, specifically the rise of evidence-based business process management using process mining techniques.
2. Process mining allows companies to analyze process data from event logs to understand their actual processes, quantify the impact of changes, and discover opportunities for improvement.
3. The techniques discussed include process discovery, conformance checking, predictive monitoring, and rule mining to provide insights into deviations, bottlenecks, and other process issues.
Fundamentals of Business Process Management: A Quick Introduction to Value-Dr...Marlon Dumas
Marlon Dumas of University of Tartu gives an introduction and quick tour of the business process management lifecycle. Seminar given at the Estonian BPM Roundtable, 10 October 2013.
Minería de Procesos y de Reglas de NegocioMarlon Dumas
Charla sobre minería de procesos y reglas de negocio en el 1er Foro Colombiano de BPM organizado por la Universidad de los Andes (Bogotá), 29 de Noviembre 2013 - http://forosisis.uniandes.edu.co/bpm/1er-forodebpm/
Introduction to Business Process Analysis and RedesignMarlon Dumas
Special course on business process analysis and design delivered at University of Granada on 23-24 January 2014. The course covers qualitative and quantitative process analysis techniques and redesign heuristics. Based on the textbook Fundamentals of Business Process Management by Dumas et al.
My business processes are deviant! What should I do about it?Marlon Dumas
This document discusses techniques for identifying and addressing deviant business processes. It defines deviance as processes that violate compliance rules, service level objectives, or cost targets. The document recommends a two-pronged approach of deviance mining and predictive monitoring. Deviance mining involves analyzing process event logs to discover patterns that distinguish normal and deviant cases, in order to explain the causes of deviance. Predictive monitoring uses the patterns to predict future deviance and generate alerts. Several case studies are described where organizations successfully applied these techniques to problems like late deliveries, faulty products, and software issues. The key takeaway is that organizations should quantify, analyze, monitor, and predict deviance to preempt problems in their business processes.
Minimizing Overprocessing Waste in Business Processes via Predictive Activity...Marlon Dumas
This document presents an approach to minimize overprocessing waste in business processes by predictively ordering activities. It does so by first executing the activity that is most likely to reject a case based on predictive models of each case's attributes. This avoids performing expensive downstream activities unnecessarily. The approach is evaluated on two real-world datasets, showing it reduces the average number of process checks required by selectively performing the knockout activity earlier. Overall, predictive activity ordering provides a way to reduce overprocessing waste in business processes.
In Processes We Trust: Privacy and Trust in Business ProcessesMarlon Dumas
This document discusses challenges and opportunities around privacy and trust in business processes. It begins by defining key concepts like security, privacy, and trust. It then outlines topics related to business process security and privacy, such as access control, flow analysis to detect unauthorized data access, and privacy-aware business process execution. The document proposes approaches for privacy-aware business processes using techniques like k-anonymization and multi-party computation. It describes a system called Pleak.io that aims to model stakeholders, data flows, and privacy-enhancing technologies to quantify privacy leaks and accuracy loss in processes. The document concludes by discussing challenges around collaborative processes with untrusted parties and the potential use of distributed ledgers and smart contracts to address issues of
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.
Business Process Analytics: From Insights to PredictionsMarlon Dumas
Keynote talk at the 13th Baltic Conference on Databases and Information Systems, Trakai, Lithuania, 2 July 2018.
Abstract
Business process analytics is a body of methods for analyzing data generated by the execution of business processes in order to extract insights about weaknesses and improvement opportunities, both at the tactical and operational levels. Tactical process analytics methods (also known as process mining) allow us to understand how a given business process is actually executed, if and how its execution deviates with respect to expected or normative pathways, and what factors contribute to poor process performance or undesirable outcomes. Meantime, operational process analytics methods allow us to monitor ongoing executions of a business process in order to predict future states and undesirable outcomes at runtime (predictive process monitoring). Existing methods in this space allow us to predict, for example, which task will be executed next in a case, when, and who will perform it? When will an ongoing case complete? What will its outcome be and how can negative outcomes be avoided? This keynote will present a framework for conceptualizing business process analytics methods and applications. The talk will provide an overview of state-of-art methods and tools in the field and will outline open challenges and research opportunities.
Process Mining 2.0: From Insights to ActionsMarlon Dumas
The document discusses several topics in process mining research including predictive process monitoring, prescriptive process monitoring, robotic process mining, data-driven simulation, and causal process mining. It provides references for further research on each topic, with links to relevant papers that outline techniques in each area.
Apromore: Advanced Business Process Analytics on the CloudMarlon Dumas
Tutorial delivered at the 16th International Conference on Business Process Management (BPM'2018), Sydney, Australia, 13 September 2018. The tutorial provides an introduction to process mining and predictive process monitoring using Apromore
Predictive Process Monitoring with Hyperparameter OptimizationMarlon Dumas
1. The document presents a predictive process monitoring framework that is enhanced with technique and hyperparameter optimization.
2. The framework evaluates multiple configurations of machine learning techniques and their hyperparameters on historical execution traces to identify the most suitable configuration for a given prediction problem and dataset.
3. An evaluation of the framework on two prediction problems and datasets found that it was able to identify suitable configurations within a reasonable time frame, though the best configuration varied depending on the prediction problem and users' performance criteria.
White-box prediction of process performance indicators via flow analysisMarlon Dumas
Presentation delivered by Ilya Verenich at the International Conference on Software Processes (ICSSP'2017), Paris, France, July 2017. This paper received the best paper award at the conference. Paper available at: http://kodu.ut.ee/~dumas/pubs/icssp2017whitebox.pdf
1) The document proposes a novel approach to using mathematical formulas to revise business processes modeled with Petri nets. The goal is to reduce cost and cycle time by reducing the number of tasks needed to complete the process.
2) The approach generates alternative business process designs by excluding certain transitions in the Petri net model. Excluded transitions are evaluated based on minimizing duration.
3) Results show the proposed approach can efficiently optimize process attributes like cost and duration. It was able to reduce the duration of the case study process by 12.5% by removing unnecessary tasks.
Complete and Interpretable Conformance Checking of Business ProcessesMarlon Dumas
This document presents a new approach for conformance checking of business processes that identifies all differences between a process model and an event log. It generates natural language statements to describe each difference. The approach works by translating the model and log into prime event structures and extracting mismatches by comparing their partially synchronized product. It can identify seven elementary mismatch patterns to characterize deviations. The approach was implemented in a standalone Java tool and evaluated on a real-life process with over 150,000 event traces.
Predictive Business Process Monitoring with Structured and Unstructured DataMarlon Dumas
Presentation delivered by Irene Teinemaa at the BPM'2016 conference, Rio de Janeiro, 22 September 2016. Paper available at: http://kodu.ut.ee/~dumas/pubs/bpm2016predictivemonitoring.pdf
Evidence-Based Business Process ManagementMarlon Dumas
1. The document discusses trends in business process management, specifically the rise of evidence-based business process management using process mining techniques.
2. Process mining allows companies to analyze process data from event logs to understand their actual processes, quantify the impact of changes, and discover opportunities for improvement.
3. The techniques discussed include process discovery, conformance checking, predictive monitoring, and rule mining to provide insights into deviations, bottlenecks, and other process issues.
Fundamentals of Business Process Management: A Quick Introduction to Value-Dr...Marlon Dumas
Marlon Dumas of University of Tartu gives an introduction and quick tour of the business process management lifecycle. Seminar given at the Estonian BPM Roundtable, 10 October 2013.
Minería de Procesos y de Reglas de NegocioMarlon Dumas
Charla sobre minería de procesos y reglas de negocio en el 1er Foro Colombiano de BPM organizado por la Universidad de los Andes (Bogotá), 29 de Noviembre 2013 - http://forosisis.uniandes.edu.co/bpm/1er-forodebpm/
Introduction to Business Process Analysis and RedesignMarlon Dumas
Special course on business process analysis and design delivered at University of Granada on 23-24 January 2014. The course covers qualitative and quantitative process analysis techniques and redesign heuristics. Based on the textbook Fundamentals of Business Process Management by Dumas et al.
My business processes are deviant! What should I do about it?Marlon Dumas
This document discusses techniques for identifying and addressing deviant business processes. It defines deviance as processes that violate compliance rules, service level objectives, or cost targets. The document recommends a two-pronged approach of deviance mining and predictive monitoring. Deviance mining involves analyzing process event logs to discover patterns that distinguish normal and deviant cases, in order to explain the causes of deviance. Predictive monitoring uses the patterns to predict future deviance and generate alerts. Several case studies are described where organizations successfully applied these techniques to problems like late deliveries, faulty products, and software issues. The key takeaway is that organizations should quantify, analyze, monitor, and predict deviance to preempt problems in their business processes.
Minimizing Overprocessing Waste in Business Processes via Predictive Activity...Marlon Dumas
This document presents an approach to minimize overprocessing waste in business processes by predictively ordering activities. It does so by first executing the activity that is most likely to reject a case based on predictive models of each case's attributes. This avoids performing expensive downstream activities unnecessarily. The approach is evaluated on two real-world datasets, showing it reduces the average number of process checks required by selectively performing the knockout activity earlier. Overall, predictive activity ordering provides a way to reduce overprocessing waste in business processes.
Business Process Performance Mining with Staged Process FlowsMarlon Dumas
This document proposes a new technique called staged process flows to analyze how business process performance evolves over time. It introduces measures to quantify the performance of each process stage and the overall process. These measures are calculated over time periods to show how performance changes. The approach is evaluated on two real-world event logs where it is able to answer questions about how overall performance, bottlenecks, and demand/capacity affect the processes over time. The technique provides novel visual analytics to help investigate process patterns and bottlenecks.
This presentation provides you with an overview of Business Process Management (BPM). The slides are from AIIM's BPM Certificate Program, which is a training program designed from global best practices among AIIM's 65,000 Associate and Professional members. The BPM program covers concepts and technologies for process streamlining and re-engineering; requirements gathering and analysis; application integration; process design and modelling; monitoring and process analysis; and managing change. For more information visit www.aiim.org/training
Introduction to Business Process ManagementAlan McSweeney
Training Course - Introduction to Business Process Management
It is intended to be a good general and practical introduction to the subject. It covers the following topics:
1. Business Process Management
2. Process Modelling
3. Process Analysis
4. Process Design
5. Process Performance Management
6. Process Transformation
7. Process Management Organisation
8. Enterprise Process Management
9. Business Process Management Technologies
10. Business Process Management and Business Analysis
11. Business Process Management Technology Review
Viviendo siempre en la frontera de las nuevas prácticas, Antonio nos presenta en esta sesión las enormes posibilidades que brinda la disciplina de la Minería de Procesos para ganar en conocimiento de nuestros procesos de gestión de servicios, analizarlos de forma objetiva, plantear acciones de mejora y contrastar los resultados obtenidos.
La minería de procesos permite a cualquier organización descubrir, cuantificar, analizar y auditar la ejecución de sus procesos de una forma totalmente objetiva y automatizada; su aplicación fundamental se centra en los procesos de negocio, sin embargo en esta ocasión se da la coincidencia de que el principal impulsor de esta disciplina en el mundo empresarial español es también un experto reconocido en el sector de la Gestión de Servicios IT y por lo tanto veremos en esta ponencia las ventajas concretas de la utilización de estas técnicas en el estudio de los procesos ITSM.
Así mismo, se conectan en esta presentación los principales puntos de coincidencia entre el uso de esta disciplina y la aplicación de Lean-IT para la mejora de los flujos. La exposición se apoya en el paper “Posibilidades de uso de la Minería de Procesos en ITSM”, publicado en el número 223 de a revista Novatica y que fue seleccionado como finalista en la elección del mejor artículo de 2013 y mostrará casos reales de uso de la minería de procesos y un video mostrando la animación del flujo de procesos.
Организация ИМ Центр в программном докладе обозначает подход к глобальным изменениям российских рынков через подготовку современных специалистов.
www.im-centr.ru
Обзор современных трендов на Европейском рынке систем управления информацией, подготовленный организацией Docville для организации ИМ Центр в 2016 году. Данный обзор даёт отправную точку для прогнозирования развития рынка систем управления информацией в Российской Федерации.
www.im-centr.ru
Fast Data Summit Frankfurt - Highlights of TIBCO ActiveMatix BPMJörg Grote
Dynamic Procedures and Case Management
Highlights of TIBCO ActiveMatrix BPM 4.0 (Oct. 2015)
incl. new UI Options e.g. AngularJS, HTML5 & CSS3,
advanced BPMN Pattern Support, local & global Signals.
Predictive Performance Monitoring of Material Handling Systems Using the Perf...Vadim Denisov
This document proposes using predictive performance monitoring of material handling systems based on the performance spectrum approach. It describes modeling system behavior over time using a multi-channel performance spectrum that captures features like speed, screening results, and sorting status. Machine learning models are trained on historical performance spectra to predict future values like required operator numbers and re-circulation amounts. The approach is evaluated on simulated and real-world baggage handling system event data, showing improved prediction accuracy over baselines. Future work includes incorporating domain knowledge through formal process models and further validation in practical applications.
The document discusses video surveillance systems for smart cities. It outlines the need for new paradigms in video surveillance that integrate data from multiple sources, use collaborative planning approaches, and share infrastructure. Some key applications mentioned include identifying missing persons, reducing accidents, vehicle identification, parking management using video, and measuring building energy usage. The new approach calls for integrated planning across different areas like security, traffic, and environment, as well as sharing captured data across government agencies.
"Performance Monitoring for Efficiency" presents an ongoing commissioning process to ensure buildings stay energy efficient over time.
Tom Anderson's and Emily Cross' presentation at the 2012 Better Buildings by Design Conference uses the Burlington International Airport as a case study.
This document discusses developing a computational methodology to support decision making across all phases of the acquisition process. It aims to maximize the value delivered to warfighters while minimizing time and cost. The methodology incorporates data from testing and assessments to evaluate alternatives based on measures of effectiveness and performance. It also discusses prototypes for valuing specific systems like a camera and optimizing plug-and-play sensor configurations and portfolios based on threat assessments. The methodology aims to support valuation of individual components, systems-of-systems, and evolving needs.
ProSIM has been providing engineering design, and R&D services to OEMs, Operators, EPC contractors, System Integrators and vendors of nuclear power sector. ProSIM has assisted in the design and seismic evaluation/ analysis of systems, structures and components (SSCs) of nuclear power plants (NPP). Driven by its competence and focus on quality and project management processes, ProSIM has delivered value to its customers. ProSIM has interacted with regulatory bodies and code committees related to nuclear design codes. Methodologies for seismic analysis of mechanical equipment (rotary and static), electrical engineering, instrumentation and control, and structures have been developed by ProSIM using ASME boiler and pressure vessel (B&PV), RCC, IEEE, ASCE and similar codes. Several hundreds of reports of seismic analysis/ evaluation submitted by ProSIM have been approved by the operators or regulatory bodies. ProSIM has taken up several detailed engineering projects. Worked on design optimisation of structures/ equipment, pipelines, supports etc. ProSIM has also supported seismic qualification of equipment/ systems by physical testing by coordinating with agencies.
In addition to the seismic analysis during engineering stage for structural integrity assessment, ProSIM has worked on seismic margin assessment, seismic re-evaluation, fitness for service (FFS), remaining life assessment and extension (RLA/RLE), and failure analysis.
Development of industrial ct system for 2 d 3d tomographic images of concrete...Walmor Godoi
The document summarizes the development of an industrial CT system by a research group in Brazil to perform 2D and 3D tomographic imaging of concrete cores and polymeric insulators. It describes the motivation to analyze concrete dams and power distribution components, outlines the system components developed over multiple iterations, and provides examples of applications in concrete analysis, insulator defect detection, and other materials testing.
Testing Dynamic Behavior in Executable Software Models - Making Cyber-physica...Lionel Briand
This document discusses testing dynamic behavior in executable software models for cyber-physical systems. It presents challenges for model-in-the-loop (MiL) testing due to large input spaces, expensive simulations, and lack of simple oracles. The document proposes using search-based testing to generate critical test cases by formulating it as a multi-objective optimization problem. It demonstrates the approach on an advanced driver assistance system and discusses improving performance with surrogate modeling.
For more than 50 years, Epec has continued its tradition of perfection in engineering and manufacturing printed circuit boards for the most discerning customers. Our years of manufacturing experience provide us with a competitive edge when it comes to PCB layout and design. From a simple single sided board to a complex multi-layer, double sided surface mount design, our goal is to provide you a design that meets your requirements and is the most cost effective to manufacture.
The Institute of Drilling Technology (IDT) in Dehradun, India is a premier drilling institute in Southeast Asia that was established in 1978. It caters to approximately 100 oil and gas companies from India and abroad. IDT conducts applied research and development related to drilling, drilling fluids, cementing, and completion fluids. It also provides technical support, monitoring of drilling operations, and training through its Drilling Technology School and Well Control School. IDT has various state-of-the-art facilities and equipment to simulate drilling conditions and test drilling parameters. It aims to become a leading global institute providing services to support exploration and production drilling activities.
Future-Proofing Your Business with TechnologySkoda Minotti
Technology is rapidly moving from a business enabler to the core of the business. New technologies such as “big data” and analytics, the internet of things (IoT), robotics, mobile technology, artificial intelligence and cybersecurity are transforming the way business gets done.
We explore the business implications of technology and their impact on businesses of all sizes and scopes, and presents strategies for charting a path through these disruptive times.
Project Route Map - OMC South Kaliapani Chromite UG MineTapas Das
M.N. Dastur & Company is a consulting engineering firm established in 1955 in Kolkata, India. It has over 2,000 employees and has successfully completed over 1,000 industrial projects globally, including in steel, power, mining and other industries. The document discusses M.N. Dastur & Company providing engineering services for the development of an underground chromite mine in Odisha, India owned by Odisha Mining Corporation. The services will include feasibility studies, exploration works, mine planning, engineering design, and project management during construction and operation of the mine.
Presentation by Don Matthews on Intelligent Compaction for the CalAPA Spring Asphalt Pavement Conference & Equipment Expo, April 20-21, 2016, in Ontario, CA.
Business Process Monitoring and MiningMarlon Dumas
Lecture delivered at the Second Latin-American Summer School in Business Process Management, Bogota, Colombia, 28 June 2017 - http://ii-las-bpm.uniandes.edu.co/
The New Drone Rules: What Utilities Need to Know, Ron PatelTWCA
Ron Patel of Dallas Water Utilities discusses the utility's use of drones. The utility acquired drones to help inspect its large wastewater treatment plant from the air, covering over 450 acres and processing 65% of the city's flow. Drones are used to inspect roofs, facilities, construction sites, fences, and other infrastructure. A roof inspection project using drones saved significant time and costs compared to conventional inspection methods. The presentation covers FAA drone rules, safety practices, and potential future uses of drones at water and wastewater utilities like monitoring assets and processes.
This document summarizes a data analysis project on building and safety permit information for the City of Los Angeles. The analysis was conducted using IBM Bluemix and HiveQL to analyze over 200MB of permit data. Various queries were written to analyze attributes of the permit data like permit subtype, category, contractor, status, issue date, and valuation. The query results were visualized using Tableau, Google Fusion Tables, PowerView in Excel, and 3D maps in Excel to produce charts, graphs, and maps. The analysis found trends like most permits being for mechanical work, most contractors being from California, and electrical permit categories increasing most year-over-year.
This document summarizes a data analysis project on building and safety permit information for the City of Los Angeles. The analysis was conducted using IBM Bluemix and HiveQL to analyze over 200MB of permit data. Various queries were written to analyze attributes of the permit data like permit subtype, category, contractor, status, issue date, and valuation. The query results were visualized using Tableau, Google Fusion Tables, PowerView and 3D maps in Excel to produce charts, graphs and maps. The analysis found trends like most permits being for mechanical work, most contractors being in-state, and electrical permit growth exceeding other categories.
Back to the Basics: Principles for Constructing Quality SoftwareTechWell
Using an analogy to the building codes followed by architects and contractors in the construction of buildings, Rick Spiewak explores the fundamental principles for developing and delivering high quality, mission-critical systems. Just as buildings are constructed using different materials and techniques, we use a variety of languages, methodologies, and tools to develop software. Although there is no formal "building code" for software, software projects should consider-and judiciously apply-the recognized "best" practices of static analysis, automated unit testing, code re-use, and peer reviews. Rick takes you on a deep dive into each of these techniques where you'll learn about their advantages, disadvantages, costs, challenges, and more. Learn to recognize when you should apply the practices, gaining an appreciation and understanding of how you can achieve better quality without increasing costs or lengthening the development to delivery cycle time.
Traceability Beyond Source Code: An Elusive Target?Lionel Briand
This document discusses traceability beyond source code, which is an elusive target. It provides an overview and examples from industrial research projects on requirements-requirements, requirements-design, requirements-test cases traceability. The examples show challenges in capturing changes precisely and change rationales. Automated traceability approaches can reduce effort but may lack accuracy required for certification. Traceability is important for certification, change management and economic decision making.
Similar to Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure (20)
How GenAI will (not) change your business?Marlon Dumas
Not all new technology waves are the same. Some waves are vertical (3D printing, digital twins, blockchain) while others are horizontal (the PC in the 80s, the Web in the 90s). GenAI is a horizontal wave. The question is not if GenAI will impact my business, but what will be the scope of this impact. In this talk, we will go through a journey of collisions: GenAI colliding with customer service, clerical work, information search, content production, IT development, product design, and other knowledge work. A common thread to understand the impact of GenAI is to distinguish between descriptive use cases (search, summarize, expand, transcribe & translate) versus creative use.
Walking the Way from Process Mining to AI-Driven Process OptimizationMarlon Dumas
While generative AI grabs headlines, most organizations are yet to achieve continuous process improvement from predictive and prescriptive analytics.
Why? It’s largely about data, people, and a methodical approach to deploy AI to connect data and people. The good news is that if your organization has built a process mining capability, you are well placed to climb the ladder to achieve AI-driven process optimization. But to get there, you need a disciplined step-by-step approach along two tracks: a tactical management track and an operational management track.
First, it’s about predicting what will happen if you leave your process as-is, and what will happen if you implement a change in your process. At a tactical level, a predictive capability allows you to prioritize improvement opportunities. At an operational level, it allows you to predict issues, such as deadline violations. The challenges here are how to manage the inherent uncertainty of data-driven AI systems, and how to change your people and culture to manage processes proactively, rather than reactively. One thing is to deploy predictive dashboards, another entirely different thing is to get people to use them effectively to improve the processes.
Next, it’s about becoming preemptive: continuously optimizing your processes by leveraging streams of data-driven recommendations to trigger changes and actions. At the tactical level, this prescriptive capability allows you to implement the right changes to maximize competing KPIs. At the operational level, it means triggering interventions in your processes to “wow” customers and to meet SLAs in a cost-effective manner. The challenge here is how to help process owners, workers, and other stakeholders to understand the causes of performance issues and how the recommendations generated by the AI-driven optimization system will tackle those causes?
And finally, as an icing on the cake, generative AI allows you to produce improvement scenarios to adapt to external changes. Importantly, the transformative potential of generative AI in the context of process improvement does not come from its ability to provide question-and-answer interfaces to query data. It comes from its ability to support continuous process adaptation by generating and validating hypotheses based on a holistic view of your organization.
In this talk, we will discuss how organizations are driving sustainable business value by strategically layering predictive, prescriptive, and generative AI onto a process mining foundation, one brick at a time.
Industry keynote talk by Marlon Dumas at the 5th International Conference on Process Mining (ICPM'2023), Rome, Italy, 25 October 2023
Discovery and Simulation of Business Processes with Probabilistic Resource Av...Marlon Dumas
In the field of business process simulation, the availability of resources is captured by assigning a calendar to each resource, e.g., Monday-Friday 9:00-18:00. Resources are assumed to be always available to perform activities during their calendar. This assumption often does not hold due to interruptions, breaks, or because resources time-share across multiple processes. A simulation model that captures availability via crisp time slots (a resource is either on or off during a slot) does not capture these behaviors, leading to inaccuracies in the simulation output. This paper presents a simulation approach wherein resource availability is modeled probabilistically. In this approach, each availability time slot is associated with a probability, allowing us to capture, for example, that a resource is available on Fridays between 14:00-15:00 with 90% probability and between 17:00-18:00 with 50% probability. The paper proposes an algorithm to discover probabilistic availability calendars from event logs. An empirical evaluation shows that simulation models with probabilistic calendars discovered from event logs, replicate the temporal distribution of activity instances and cycle times of a process more closely than simulation models with crisp calendars.
This presentation was delivered at the 5th International Conference on Process Mining (ICPM'2023), Rome, Italy, October 2023.
The paper is available at: https://easychair.org/publications/preprint/Rz9g
Can I Trust My Simulation Model? Measuring the Quality of Business Process Si...Marlon Dumas
Business Process Simulation (BPS) is an approach to analyze the performance of business processes under different scenarios. For example, BPS allows us to estimate what would be the cycle time of a process if one or more resources became unavailable. The starting point of BPS is a process model annotated with simulation parameters (a BPS model). BPS models may be manually designed, based on information collected from stakeholders and empirical observations, or automatically discovered from execution data. Regardless of its origin, a key question when using a BPS model is how to assess its quality. In this paper, we propose a collection of measures to evaluate the quality of a BPS model w.r.t. its ability to replicate the observed behavior of the process. We advocate an approach whereby different measures tackle different process perspectives. We evaluate the ability of the proposed measures to discern the impact of modifications to a BPS model, and their ability to uncover the relative strengths and weaknesses of two approaches for automated discovery of BPS models. The evaluation shows that the measures not only capture how close a BPS model is to the observed behavior, but they also help us to identify sources of discrepancies.
Presentation delivered by David Chapela-Campa at the BPM'2023 conference, Utrecht, September 2023.
Business Process Optimization: Status and PerspectivesMarlon Dumas
For decades, business process optimization has been largely about art and craft (and sometimes wizardry). Apart from narrowly scoped approaches to optimize resource allocation (often assuming that workers behave like robots), a lot of business process optimization relies on high-level guidelines, with A/B testing for idea validation, which is hard to scale to complex processes. As a result, managers end up settling for a "good enough" process. Can we do more? In this talk, we review recent work on the use of high-fidelity simulation models discovered from execution data. The talk also explores the possibilities (and perils) that LLMs bring to the field of business process optimization.
This talk was delivered at the Workshop on Data-Driven Business Process Optimization at the BPM'2023 conference.
Learning When to Treat Business Processes: Prescriptive Process Monitoring wi...Marlon Dumas
Paper presentation at the 35th International Conference on Advanced Information Systems Engineering (CAiSE'2023).
Abstract.
Increasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.
Why am I Waiting Data-Driven Analysis of Waiting Times in Business ProcessesMarlon Dumas
Presentation of a research paper at the 35th International Conference on Advanced Information Systems Engineering (CAiSE) in Zaragoza Spain. The paper presents a classification of causes of waiting times in business processes and a method to automatically detect and quantify the presence of each of these causes in a business process recorded in an event log.
This talk introduces the concept of Augmented Business Process Management System: An ABPMS is a process-aware information system that relies on trustworthy AI technology to
reason and act upon data, within a set of restrictions, with the aim to continuously adapt and
improve a set of business processes with respect to one or more key performance indicators.
The talk describes the transition from existing process mining technology to AI-Augmented BPM as a pyramid, where predictive, prescriptive, conversational and reasoning capabilities are stacked up incrementally to reach the level of Augmented BPM.
Talk delivered at the AAAI'2023 Workshop on AI for Business Process Management.
Process Mining and Data-Driven Process SimulationMarlon Dumas
Guest lecture delivered at the - Institut Teknologi Sepuluh on 8 December 2022.
This lecture gives an overview of process mining and simulation techniques, and how the two can be used together in process improvement projects.
Modeling Extraneous Activity Delays in Business Process SimulationMarlon Dumas
This paper presents a technique to enhance the fidelity of business process simulation models by detecting unexplained (extraneous) delays from business process execution data, and modeling these delays in the simulation model, via timer events.
The presentation was delivered at the 4th International Conference on Process Mining (ICPM'2022).
Paper available at: https://arxiv.org/abs/2206.14051
Business Process Simulation with Differentiated Resources: Does it Make a Dif...Marlon Dumas
Existing methods for discovering business process simulation models from execution data (event logs) assume that all resources in a pool have the same performance and share the same availability calendars. This paper proposes a method for discovering simulation models, wherein each resource is treated as an individual entity, with its own performance and availability calendar. An evaluation shows that simulation models with differentiated resources more closely replicate the distributions of cycle times and the work rhythm in a process than models with undifferentiated resources. The paper is available at: https://link.springer.com/chapter/10.1007/978-3-031-16103-2_24
Prescriptive Process Monitoring Under Uncertainty and Resource ConstraintsMarlon Dumas
This paper presents an approach to trigger runtime interventions at runtime, in order to improve the success rate of a process, when the number of resources who can perform these interventions is limited.
The paper is available at: https://link.springer.com/chapter/10.1007/978-3-031-16171-1_13
The presentation delivered at the 20th International Conference on Business Process Management (BPM'2022), in Muenster, Germany, September 2022.
Slides of a lecture delivered at the First Process Mining Summer School in Aachen, Germany, July 2022.
This lecture introduces techniques in the area of "task mining" with an emphasis on Robotic Process Mining. Robotic Process Mining (RPM) is a family of techniques to discover repetitive routines that can be automated using Robotic Process Automation (RPA) technology, by analyzing interactions between
one or more workers and one or more software applications, during the performance of one or more tasks in a business process. In general, RPM techniques take as input logs of User Interactions (UI logs). These UI logs are recorded while workers interact with one or more applications, typically desktop applications. Based on these logs, RPM techniques produce specifications of one or more routines that can be automated using RPA or related tools.
Accurate and Reliable What-If Analysis of Business Processes: Is it Achievable?Marlon Dumas
This document discusses using event logs to generate business process simulation models. It describes traditional discrete event simulation approaches that discover simulation models from event logs recorded by information systems. Deep learning techniques are also discussed that can generate traces without an explicit process model. The document suggests that combining discrete event simulation and deep learning may produce more accurate simulations, but challenges remain around validating such hybrid approaches and testing them in previously unseen scenarios. More research is needed before these data-driven simulation methods can reliably predict the effects of interventions.
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
Process Mining: A Guide for PractitionersMarlon Dumas
This document presents a guide for practitioners on process mining. It introduces process mining and discusses its main use cases. These use cases are categorized into discovery oriented, future and change oriented, alignment oriented, variant oriented, and performance oriented. The document also provides a framework to classify use cases and discusses the business-oriented questions that can be answered using different process mining use cases, such as improving transparency, quality, agility, efficiency and conformance.
Process Mining for Process Improvement.pptxMarlon Dumas
Presentation of a research paper at the 16th International Conference on Research Challenges in Information Science (RCIS). The paper presents the results of an empirical study on how practitioners use process mining to identify business process improvement opportunities. The paper is available at: https://link.springer.com/chapter/10.1007/978-3-031-05760-1_13
Data-Driven Analysis of Batch Processing Inefficiencies in Business ProcessesMarlon Dumas
Slides of a research paper presentation at the 16th International Conference on Research Challenges in Information Science (RCIS).
The research paper presents an approach to analyze event logs of business processes in order to identify batched activities and to analyze the waiting times caused by these activities.
Paper available at: https://link.springer.com/chapter/10.1007/978-3-031-05760-1_14
Optimización de procesos basada en datosMarlon Dumas
Ponencia en BPM Day Lima 2021.
En esta charla, hablaremos de métodos y aplicaciones emergentes en el ámbito de la optimización de procesos basada en datos. Hablaremos de avances en el área de la minería de procesos, de métodos de construcción de gemelos digitales de procesos y de métodos de monitoreo predictivo. Mostraremos por medio de ejemplos y casos de estudio, cómo estos métodos permiten guiar las iniciativas de transformación digital y de mejora continua de procesos, En particular, ilustraremos el uso de estos métodos para: (1) analizar el rendimiento de los procesos de negocio de manera a identificar fricciones y oportunidades de automatización; (2) predecir el impacto de cambios, y en particular, predecir el impacto de una iniciativa de automatización; (3) realizar predicciones sobre el rendimiento del proceso y ajustar la ejecución del proceso de manera a prevenir incumplimientos del SLA, quejas de clientes, y otros eventos indeseables.
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.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
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Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Automated Discovery of Structured Process Models: Discover Structured vs Discover and Structure
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Automated Discovery of
Structured Process Models:
Discover Structured
vs
Discover and Structure
Adriano Augusto, Raffaele Conforti, Marlon Dumas,
Marcello La Rosa, and Giorgio Bruno
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Automated Process Discovery
CID Task Time Stamp …
13219 Enter Loan Application 2007-11-09 T 11:20:10 -
13219 Retrieve Applicant Data 2007-11-09 T 11:22:15 -
13220 Enter Loan Application 2007-11-09 T 11:22:40 -
13219 Compute Installments 2007-11-09 T 11:22:45 -
13219 Notify Eligibility 2007-11-09 T 11:23:00 -
13219 Approve Simple Application 2007-11-09 T 11:24:30 -
13220 Compute Installements 2007-11-09 T 11:24:35 -
… … … …
Enter Loan
Application
Retrieve
Applicant
Data
Compute
Installments
Approve
Simple
Application
Approve
Complex
Application
Notify
Rejection
Notify
Eligibility
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Process Quality Dimensions
Process
Discovery
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Process Quality Dimensions
Process
Discovery
Fitness
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Process Quality Dimensions
Process
Discovery
Fitness
Precision
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Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization
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Process Quality Dimensions
Process
Discovery
Fitness
Precision
Generalization
Complexity
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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
High-Fitness
Low-Complexity
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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
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Process Model discovered with
Heuristics Miner
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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
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Process Discovery Algorithms:
The Two Worlds
High-Fitness
High-Precision
Heuristic
Miner
Fodina Miner
High-Fitness
Low-Complexity
Inductive
Miner
Evolutionary
Tree Miner
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Process Model discovered with
Inductive Miner
• Structured by construction
• Based on process tree
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Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity
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Process Discovery Algorithms
High-Fitness
High-Precision
Low-Complexity
Structured
Miner
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Process Model discovered with
Structured Miner
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Discover and Structure:
A two phases approach
• Phase One: discover a process model focussing
on fitness and precision without constraints on
its structure. For example using Heuristic Miner
or Fodina Miner.
• Phase Two: simplify the discovered process
model structuring it at posteriori.
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Phase Two: Structuring
Discover the RPST of the model
Process Fragment:
• Trivial (T) – single edge
• Polygon (P) – sequence of fragments
• Bond (B) – set of fragments sharing two nodes
• Rigid (R) – none of the above cases
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Phase Two: Structuring
Discover the RPST of the model
Reject
Payment
Request
Inform
Customer
Payby
Cash
Payby
Cheque
Approve
Update
Account
P1
P1
B1
B1
P3
R1P2
P2 P3
R1
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Phase Two: Structuring
Discover the RPST of the model
Structure sound AND-Homogeneous
or Heterogeneous rigids using
BPSTruct (Polyvyanyy 2014)
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Phase Two: Structuring
Discover the RPST of the model
Structure sound AND-Homogeneous
or Heterogeneous rigids using
BPSTruct (Polyvyanyy 2014)
Structure XOR-Homogeneous and
unsound rigids using Extended
Oulsnam
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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Injection
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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Push-Down
– Push down-stream the gateway causing the injection
– Duplicate everything in between the gateway causing
the injection and the gateway down-stream
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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Ejection
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Oulsnam’s Algorithm Extended for
BPMN Process Models
• Pull-Up
– Pull up-stream the gateway causing the injection
– Duplicate everything in between the gateway causing
the injection and the gateway up-stream
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Evaluation Setup
• Real-Life dataset: IBM (54 models) and SAP
(545 models) collections
• Synthetic dataset: 20 models
• Generated three sets of logs for a total of 619
logs
• We retained all logs for which Heuristics Miner
produced an unstructured model - 129 logs
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Evaluation Setup
• Four process discovery algorithms:
– Inductive Miner
– Evolutionary Tree Miner
– Heuristics Miner
– Structured Miner (on top of Heuristics Miner)
• Four quality dimensions:
– Fitness
– Precision
– Generalization
– Complexity
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Evaluation Results
• Real-life datasets:
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Evaluation Results
• Real-life datasets:
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Heuristics Miner - Real-life Dataset
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Inductive Miner - Real-life Dataset
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Structured Miner - Real-life Dataset
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Future Work
• Experiment with alternative discovery algorithms to
explore alternative tradeoffs between model quality
metrics
• Explore the option of sacrificing weak bisimilarity to
obtain models with higher structuredness
• Use process model clone detection techniques to
refactor duplicates introduced by the structuring phase