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An Activity Instance Based Hierarchical Framework for Event Abstraction
16 references·2 citing·40 second hop
An Activity Instance Based Hierarchical Framework for Event Abstraction (2021)An Activity Instance Based Hierarchical F…Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity (2020)Using Multi-Level Information…Event-log abstraction using batch session identification and clustering (2020)Event-log abstraction using b…Event abstraction in process mining: literature review and taxonomy (2020)Event abstraction in process …Stage-based discovery of business process models from event logs (2019)Stage-based discovery of busi…Earth Movers’ Stochastic Conformance Checking (2019)Earth Movers’ Stochastic Conf…Event Abstraction for Process Mining Using Supervised Learning Techniques (2017)Event Abstraction for Process…Using Life Cycle Information in Process Discovery (2016)Using Life Cycle Information …Scalable process discovery and conformance checking (2016)Scalable process discovery an…From Low-Level Events to Activities - A Pattern-Based Approach (2016)From Low-Level Events to Acti…Data Science in Action (2016)Data Science in ActionDiscovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour (2014)Discovering Block-Structured …Discovering Block-Structured Process Models from Event Logs - A Constructive Approach (2013)Discovering Block-Structured …A Study Into the Factors That Influence the Understandability of Business Process Models (2010)A Study Into the Factors That…A Fresh Look at Precision in Process Conformance (2010)A Fresh Look at Precision in …What Makes Process Models Understandable? (2007)What Makes Process Models Und…Process mining with the HeuristicsMiner algorithm (2006)Process mining with the Heuri…LLM-Based Event Abstraction and Integration for IoT-Sourced Logs (2025)LLM-Based Event Abstraction a…An empirical evaluation of unsupervised event log abstraction techniques in process mining (2023)An empirical evaluation of un…Workflow mining: A survey of issues and approaches (2003)Workflow mining: discovering process models from event logs (2004)The ProM Framework: A New Era in Process Mining Tool Support (2005)Process Mining: Overview and Outlook of Petri Net Discovery Algorithms (2009)Active Trace Clustering for Improved Process Discovery (2013)Automated Discovery of Process Models from Event Logs: Review and Benchmark (2018)Conformance checking of processes based on monitoring real behavior (2007)Alignment Based Precision Checking (2013)Model repair — aligning process models to reality (2014)PM4Py: A process mining library for Python (2023)Rank Correlation Methods. (1957)Guidelines of Business Process Modeling (2000)Workflow Verification: Finding Control-Flow Errors Using Petri-Net-Based Techniques (2000)Activity labeling in process modeling: Empirical insights and recommendations (2009)Refactoring large process model repositories (2011)Process Mining Manifesto (2012)Split miner: automated discovery of accurate and simple business process models from event logs (2018)THE MIND'S EYE IN CHESS (1973)EXPERT AND EXCEPTIONAL PERFORMANCE: Evidence of Maximal Adaptation to Task Constraints (1996)Research Methods in Education (2007)Making sense of business process descriptions: An experimental comparison of graphical and textual notations (2011)Factors of process model comprehension—Findings from a series of experiments (2012)Comprehension of Procedural Visual Business Process Models (2017)Process Mining (2011)Process Mining: Discovery, Conformance and Enhancement of Business Processes (2011)Process mining in healthcare: A literature review (2016)BPMN: An introduction to the standard (2011)Process Mining (2016)Process Mining: Data Science in Action (2016)Event Log Preprocessing for Process Mining: A Review (2021)Utilizing domain knowledge in data-driven process discovery: A literature review (2022)The biggest business process management problems to solve before we die (2023)From event streams to process models and back: Challenges and opportunities (2017)act2vec, trace2vec, log2vec, and model2vec: Representation Learning for Business Processes (2018)Discovering more precise process models from event logs by filtering out chaotic activities (2018)Directly Follows-Based Process Mining: Exploration & a Case Study (2019)Automated discovery of business process simulation models from event logs (2020)Process mining in oncology: A literature review (2016)Process mining technology selection with spherical fuzzy AHP and sensitivity analysis (2021)Large Process Models: A Vision for Business Process Management in the Age of Generative AI (2024)
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References

  • 2013Discovering Block-Structured Process Models from Event Logs - A Constructive Approach648 cited
  • 2006Process mining with the HeuristicsMiner algorithm484 cited
  • 2016Data Science in Action469 cited
  • 2014Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour458 cited
  • 2007What Makes Process Models Understandable?310 cited
  • 2010A Study Into the Factors That Influence the Understandability of Business Process Models276 cited
  • 2016Scalable process discovery and conformance checking218 cited
  • 2010A Fresh Look at Precision in Process Conformance189 cited
  • 2020Event abstraction in process mining: literature review and taxonomy172 cited
  • 2017Event Abstraction for Process Mining Using Supervised Learning Techniques101 cited
  • 2016From Low-Level Events to Activities - A Pattern-Based Approach84 cited
  • 2019Earth Movers’ Stochastic Conformance Checking77 cited
  • 2016Using Life Cycle Information in Process Discovery52 cited
  • 2020Event-log abstraction using batch session identification and clustering32 cited
  • 2020Using Multi-Level Information in Hierarchical Process Mining: Balancing Behavioural Quality and Model Complexity

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21 cited
  • 2019Stage-based discovery of business process models from event logs20 cited
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    • 2023An empirical evaluation of unsupervised event log abstraction techniques in process mining8 cited
    • 2025LLM-Based Event Abstraction and Integration for IoT-Sourced Logs8 cited