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
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.
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 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
Automated Discovery of Structured Process Models: Discover Structured vs Disc...Marlon Dumas
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
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
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.
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 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.
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 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
Automated Discovery of Structured Process Models: Discover Structured vs Disc...Marlon Dumas
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
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
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.
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.
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
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.
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
Collaborative Business Process Execution on Blockchain: The Caterpillar ApproachMarlon Dumas
Invited talk at the CAiSE'2019 Workshop on Blockchains for Inter-Organizational Collaboration and Flexible Advanced Information Systems (BIOC & FAiSE 2019).
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 and Predictive Process Monitoring: From Technology to Business...Marlon Dumas
This document discusses process mining and predictive process monitoring. It begins with an overview of business process management and how process mining fits within the broader process architecture. It then covers the key techniques in process mining like process discovery, conformance checking, performance mining, and predictive process monitoring. Examples of process mining case studies in different domains are provided. The document concludes with a discussion of how process mining can be used to enable automated process improvement.
Automated Discovery of Data Transformations for Robotic Process AutomationMarlon Dumas
Paper presentation by Artem Polyvyanyy at the AAAI Workshop on Intelligent Process Automation (IPA), New York, 7 February 2020. Paper available at: https://arxiv.org/pdf/1912.01855.pdf
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.
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
Caterpillar: A Blockchain-Based Business Proces Management SystemMarlon Dumas
Caterpillar is a blockchain-based business process management system (BPMS) that uses smart contracts to store process state and drive process execution without a database or separate execution engine. Key process data like the process model and instance state are stored on the blockchain, providing a single source of truth. Process models designed in BPMN can be automatically translated to smart contracts. This ensures correct and transparent execution across participants on the blockchain network. While promising, challenges remain around transaction costs, throughput limits, and handling large amounts of process data efficiently.
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.
Automated Process Improvement: Status, Challenges, and PerspectivesMarlon Dumas
Automated process improvement uses process mining techniques to recommend optimizations to business processes. It can suggest changes to tasks, control flow, decisions, and resource allocation based on event log analysis. Process mining discovers predictive models and simulates the effects of different changes to identify sets of improvements that optimize given performance metrics. Key challenges include scaling to real processes, estimating impacts on multiple metrics, and usability of change recommendations.
Keynote talk by Marlon Dumas at the Bolzano Rules and Artificial INtelligence Summit (BRAIN 2019), RuleML+RR and GCAI Conferences, Bolzano, Italy, 17 September 2019. The talk gives an overview of state-of-the-art methods in the field of process mining and predictive process monitoring and spells out research challenges in the fields of prescriptive process monitoring and automated process improvement.
Interpreted Execution of Business Process Models on BlockchainMarlon Dumas
Research paper presentation delivered at the IEEE Enterprise Computing Conference (EDOC), Paris, France, 30 October 2019. The paper introduces the technical details of Caterpillar´s business process execution engine v3.0 https://git.io/caterpillar - Paper available at https://arxiv.org/pdf/1906.01420.pdf
Business Process Automation and Data Processing WorkflowsMarlon Dumas
Presentation on Business Process Management Systems and Data Processing Workflow Systems delivered at the Italian Statistics Institute (IStat), 3 May 2018.
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/
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
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.
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
Collaborative Business Process Execution on Blockchain: The Caterpillar ApproachMarlon Dumas
Invited talk at the CAiSE'2019 Workshop on Blockchains for Inter-Organizational Collaboration and Flexible Advanced Information Systems (BIOC & FAiSE 2019).
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 and Predictive Process Monitoring: From Technology to Business...Marlon Dumas
This document discusses process mining and predictive process monitoring. It begins with an overview of business process management and how process mining fits within the broader process architecture. It then covers the key techniques in process mining like process discovery, conformance checking, performance mining, and predictive process monitoring. Examples of process mining case studies in different domains are provided. The document concludes with a discussion of how process mining can be used to enable automated process improvement.
Automated Discovery of Data Transformations for Robotic Process AutomationMarlon Dumas
Paper presentation by Artem Polyvyanyy at the AAAI Workshop on Intelligent Process Automation (IPA), New York, 7 February 2020. Paper available at: https://arxiv.org/pdf/1912.01855.pdf
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.
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
Caterpillar: A Blockchain-Based Business Proces Management SystemMarlon Dumas
Caterpillar is a blockchain-based business process management system (BPMS) that uses smart contracts to store process state and drive process execution without a database or separate execution engine. Key process data like the process model and instance state are stored on the blockchain, providing a single source of truth. Process models designed in BPMN can be automatically translated to smart contracts. This ensures correct and transparent execution across participants on the blockchain network. While promising, challenges remain around transaction costs, throughput limits, and handling large amounts of process data efficiently.
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.
Automated Process Improvement: Status, Challenges, and PerspectivesMarlon Dumas
Automated process improvement uses process mining techniques to recommend optimizations to business processes. It can suggest changes to tasks, control flow, decisions, and resource allocation based on event log analysis. Process mining discovers predictive models and simulates the effects of different changes to identify sets of improvements that optimize given performance metrics. Key challenges include scaling to real processes, estimating impacts on multiple metrics, and usability of change recommendations.
Keynote talk by Marlon Dumas at the Bolzano Rules and Artificial INtelligence Summit (BRAIN 2019), RuleML+RR and GCAI Conferences, Bolzano, Italy, 17 September 2019. The talk gives an overview of state-of-the-art methods in the field of process mining and predictive process monitoring and spells out research challenges in the fields of prescriptive process monitoring and automated process improvement.
Interpreted Execution of Business Process Models on BlockchainMarlon Dumas
Research paper presentation delivered at the IEEE Enterprise Computing Conference (EDOC), Paris, France, 30 October 2019. The paper introduces the technical details of Caterpillar´s business process execution engine v3.0 https://git.io/caterpillar - Paper available at https://arxiv.org/pdf/1906.01420.pdf
Business Process Automation and Data Processing WorkflowsMarlon Dumas
Presentation on Business Process Management Systems and Data Processing Workflow Systems delivered at the Italian Statistics Institute (IStat), 3 May 2018.
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/
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.
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.
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.
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.
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.
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/
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.
Business Process Optimization with Enterprise SOA and AIABob Rhubart
As presented by Vishram Patwardhan at OTN Architect Day, Redwood Shores, CA, 7/22/09.
Find an OTN Architect Day event near you: http://www.oracle.com/technology/architect/archday.html
Interact with Architect Day presenters and participants on Oracle Mix: https://mix.oracle.com/groups/15511
This document discusses Oracle technologies including SOA Suite, AIA, and Fusion Apps. It begins with a disclaimer and introduction about the presenter's background. It then provides an overview of Oracle's technology stack and how AIA fits within layers for applications, platforms, and integration technologies. The document drills down on specific Oracle products like WebLogic, ODI, SOA Suite, OBIEE, and how AIA leverages a canonical data model and development strategies to enable integration and an approach to SOA.
This document discusses developing an Office of Strategic Management (OSM) to ensure ongoing success with the Balanced Scorecard approach. It outlines key attributes for leading an OSM, including being a strategist who owns the strategic process, a scorekeeper who manages how strategy is structured and implemented, and a gatekeeper and guide who focuses attention and provides direction. The document also discusses how focusing on successful customer outcomes can help align organizations and drive innovation when developing a balanced scorecard.
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
How Fast Data Is Turned into Fast Information and Timely Action (OOW 2014)Lucas Jellema
Fast data is big data, continuously streaming in, from which information is to be learned in (near) real time. This session demonstrates how Oracle Event Processing is used to analyze live streams of data to find patterns, deviations, and aggregates. The findings are reported in the form of business events that are pushed in live dashboards to Oracle Business Activity Monitoring, which also evaluates business rules on the business events and takes action when required. Examples to be demonstrated in this session include car sensors, website traffic, Twitter feeds, and bank run detection. Oracle SOA Suite 12c, WebSockets, Oracle Application Development Framework (Oracle ADF) active data visualization tools components, and JMS are used to process, forward, and act.
Complex Event Processing (CEP) involves detecting patterns in streams of event data. CEP tools analyze multiple simple events to identify complex events inferred from simpler ones. Typical applications of CEP include monitoring for business anomalies, detecting fraud or security threats. CEP augments service-oriented architectures by allowing services to trigger from events and generate new event streams. Event processing engines use techniques like filtering, windows, and correlation to detect patterns across events over time.
This document provides an overview of Apache Flink, an open-source framework for distributed stream and batch data processing. It discusses key aspects of Flink including that it executes everything as data streams, supports iterative and cyclic data flows, allows mutable state in operators, and provides high availability and checkpointing of operator state. It also provides examples of using Flink's DataStream API to perform operations like hourly and daily tweet impression counts on a continuous stream of tweet data from Kafka.
Stream processing with Apache Flink - Maximilian Michels Data ArtisansEvention
Apache Flink is an open source platform for distributed stream and batch data processing. At its core, Flink is a streaming dataflow engine which provides data distribution, communication, and fault tolerance for distributed computations over data streams. On top of this core, APIs make it easy to develop distributed data analysis programs. Libraries for graph processing or machine learning provide convenient abstractions for solving large-scale problems. Apache Flink integrates with a multitude of other open source systems like Hadoop, databases, or message queues. Its streaming capabilities make it a perfect fit for traditional batch processing as well as state of the art stream processing.
Fast data is big data, continuously streaming in, from which information is to be learned in (near) real time. This session demonstrates how Oracle Event Processing is used to analyze live streams of data to find patterns, deviations, and aggregates. The findings are reported in the form of business events that are pushed in live dashboards to Oracle Business Activity Monitoring, which also evaluates business rules on the business events and takes action when required. Examples to be demonstrated in this session include car sensors, website traffic, Twitter feeds, and bank run detection. Oracle SOA Suite 12c, WebSockets, Oracle Application Development Framework (Oracle ADF) active data visualization tools components, and JMS are used to process, forward, and act.
MuCon London 2017: Break your event chainsBernd Ruecker
- The document discusses breaking event chains, decentralizing control, and alternatives to workflow engines for orchestrating microservices. It argues that events can decrease coupling but also increase it, and that central control should be avoided but important long-running capabilities still need ownership. Lightweight workflow engines are presented as a better alternative to DIY orchestration since they address hard problems and can run decentralized.
This document provides an overview of Splunk software for security applications. It begins with an agenda for a Splunk security presentation, then discusses challenges facing security teams like advanced threats and limitations of existing security information and event management (SIEM) systems. The document demonstrates how Splunk can collect all types of machine data, perform fast searches and analytics, and be deployed more easily than traditional SIEMs. Use cases shown include incident investigations, compliance reporting, and real-time monitoring of known and unknown threats. The document highlights Splunk's customer base, performance in industry evaluations, and integrations with security vendors. It concludes by inviting the reader to learn more about Splunk on their website or contact sales.
The document discusses continuous forensic analytics (CFA) as a tool to accelerate incident response and address threats agilely. It describes the key steps and skills needed for CFA, including capturing network data, anonymizing user metadata, reconstructing user sessions, and simulating scenarios. CFA is increasingly important due to the growing number of security breaches involving extended enterprise networks and resources located both internally and externally.
Slides of the tutorial on Multi-Dimensional Process Analysis shown at the BPM 2022 conference in Muenster, Germany.
Processes are complex phenomena that emerge from the interplay of human actors, materials, data, and machines. Process science develops effective methods and techniques for studying and improving processes. The BPM field has developed mature methods and techniques for studying and improving process executions from the control-flow perspective, and the limitations of control-flow focused thinking are well-known. Current research explores concepts from related disciplines to study behavioral phenomena “beyond” control-flow. However, it remains challenging to relate models and concepts of other behavioral phenomena to the dominant control-flow oriented paradigm.
This tutorial introduces several recently developed simple models that naturally describe behavior beyond control-flow, but are inherently compatible with control-flow oriented thinking. We discuss the Performance Spectrum to study performance patterns and their propagation over time, Event Knowledge Graphs to study networks of behavior over data objects and actors, and Proclets as a formal model for reasoning over control-flow, data object, queue and actor behavior. For each model, we discuss which phenomena can be studied, which insights can be gained, which tools are available, and to which other fields they relate.
https://doi.org/10.1007/978-3-031-16103-2_3
The document discusses best practices for configuring and analyzing Windows event logs. It recommends increasing the default event log size of 20MB, enabling useful logs like security, sysmon, and printer logs, and configuring logs to be forwarded to a SIEM using WEF. Specific events that can help detect lateral movement, credential access, and other threats are also outlined. The document stresses that properly configuring logs is key to gaining valuable security insights.
Complex Event Processing (CEP) is a technique that analyzes events to detect complex patterns. CEP engines receive events from producers, filter and transform them, detect patterns across events, and send derived events to consumers. CEP with Canopsis allows for real-time event analysis, flexible monitoring of services, and better scaling of infrastructure by processing events quickly in memory.
The Corvil App Agent provides ultra-low overhead instrumentation of critical applications with precise time-stamping to monitor algorithmic trading systems. It captures time-stamped application events with microsecond accuracy from within applications using a simple API. This provides end-to-end visibility across networks and application stacks to optimize performance, understand latency, and meet regulatory compliance for algorithmic trading.
The document discusses pull systems for replenishing inventory using the example of purchasing milk. It also discusses the critical path method (CPM) for network analysis and project scheduling. CPM involves identifying the critical path of activities that determine the shortest project duration. An example CPM analysis is provided to find the earliest and latest event times for activities in a project.
The document is a master's thesis submitted by AjayKumar Katta Sudharshan to the University of Paderborn in partial fulfillment of the requirements for a Master of Science degree in Computer Science. The thesis proposes a secure and efficient solution protocol to increase transaction security in cashless payments using location-based information. It analyzes different cashless payment methods and security threats, then develops a transaction security protocol called LBPA that uses location data and multi-factor authentication. Finally, it designs the solution protocol LBPAS by integrating LBPA into the technical landscape of a cashless payment service provider.
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
The document discusses using the Splunk Universal Forwarder to monitor endpoints for security purposes. It outlines how the Universal Forwarder can collect a variety of log and system data from endpoints to gain visibility into potential attacks or malware. Specific examples are provided of how the Universal Forwarder was used by large companies to monitor millions of endpoints and detect security issues and fraud.
Keynote speech at the Belgian Process Mining Research Day 2021. I discuss the open, critical challenge of data preparation in process mining, considering the case where the original event data are implicitly stored in (legacy) relational databases. This case covers the common situation where event data are stored inside the data layer of an ERP or CRM system. This is usually handled using manual, ad-hoc, error-prone ETL procedures. I propose instead to adopt a pipeline based on semantic technologies, in particular the framework of ontology-based data access (also known as virtual knowledge graph). The approach is code-less, and relies on three main conceptual steps: (1) the creation of a data model capturing the relevant classes, attributes, and associations in the domain of interest (2) the definition of declarative mappings from the source database to the data model, following the ontology-based data access paradigm (3) the annotation of the data model with indications on which classes/associations/attributes provide the relevant notions of case, events, event attributes, and event-to-case relation. Once this is done, the framework automatically extracts the event log from the legacy data. This makes extremely smooth to generate logs by taking multiple perspectives on the same reality. The approach has been operationalized in the onprom tool, which employs semantic web standard languages for the various steps, and the XES standard as the target format for the event logs.
Software Trace and Memory Dump Analysis: Patterns, Tools, Processes and Best ...Dmitry Vostokov
The slides from Software Diagnostics Services (former Memory Dump Analysis Services) lecture at E2EVC Virtualization Conference (13th of May, 2011) that introduced an analysis methodology for software execution artifacts. Topics include: A.C.P. Root Cause Analysis; DA+TA; Spatiality vs. Narrativity; Tools for Artifact Analysis; Checklists for Analysis; Software Behavior Patterns; Adjoint Threads; Selected Trace and Log Analysis Patterns; Trace Analysis Case Study.
Shodan Search Engine: Amphion Forum San Franciscoshawn_merdinger
Shawn Merdinger gave a presentation on Shodan, a search engine for internet-connected devices. He explained that Shodan scans the internet and indexes banners from devices to make them searchable. This allows users to find unprotected devices like IP cameras, industrial control systems, and medical devices. Merdinger showed examples of sensitive devices he discovered, including traffic lights, TV station antennas, and gas station pumps. He emphasized that while concerning, the visibility provided by Shodan can encourage better security practices.
Similar to Beyond Tasks and Gateways: Automated Discovery of BPMN Models with Subprocesses, Boundary Events and Activity Markers (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.
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.
Prescriptive Process Monitoring for Cost-Aware Cycle Time ReductionMarlon Dumas
Paper presentation at the 3rd International Conference on Process Mining (ICPM), 4 November 2021.
The paper is available at: https://arxiv.org/abs/2105.07111
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Beyond Tasks and Gateways: Automated Discovery of BPMN Models with Subprocesses, Boundary Events and Activity Markers
1. Beyond Tasks and Gateways:
Discovering BPMN Models
with subprocesses, boundary events
and activity markers
Raffaele Conforti, Marcello La Rosa
Queensland University of Technology
Marlon Dumas, Luciano García-Bañuelos
University of Tartu
BPM’2014 Conference, Eindhoven 11 September 2014 1
2. 2
Automated Process Discovery
Enter Loan
Application
Retrieve
Applicant
Data
Compute
Installments
Approve
Simple
Application
Approve
Complex
Application
Notify
Rejection
Notify
Eligibility
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 -
… … … …
5. Automated Process Discovery:
Handling Complexity
Filter
• Filter out “irrelevant” events (tasks)
• Filter out “irrelevant” traces
Abstract
• Zoom into most frequent tasks or paths
• Extract subprocesses
Divide
• Divide log by variants based on similarity (trace clustering)
• Discover multiple process models rather than one
5
6. Related Work: ProM two-phase miner
Bose, Veerbeck & van det Aalst: Discovering Hierarchical Process Models using ProM
17. What’s Next
• Standalone tool implementation
• Currently in ProM nightly build
• Further evaluation
• Logs with larger number of event types
• Noise resilience
• Missing events can trick foreign key discovery
• Further enrichment
• Event-based gateways, more BPMN events…
• Adding data conditions, completion conditions, …
17