On the public citation graph
Filtering Out Infrequent Behavior from Business Process Event Logs
0 signed · 186 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/widm.1045openalex-snapshot
Replaying history on process models for conformance checking and performance analysis
inferredcites →https://doi.org/10.1007/11556992_38openalex-snapshot
Efficient Modeling of Discrete Events for Anomaly Detection Using Hidden Markov Models
inferredcites →https://doi.org/10.1007/978-3-319-08222-6_4openalex-snapshot
Measuring Patient Flow Variations: A Cross-Organisational Process Mining Approach
inferredcites →https://doi.org/10.1007/978-3-319-10172-9_7openalex-snapshot
Beyond Tasks and Gateways: Discovering BPMN Models with Subprocesses, Boundary Events and Activity Markers
inferredcites →https://doi.org/10.1007/978-3-540-75183-0_4openalex-snapshot
What Makes Process Models Understandable?
inferredcites →https://doi.org/10.1007/978-3-642-22922-0_9openalex-snapshot
Finding Top-k Shortest Path Distance Changes in an Evolutionary Network
inferredcites →https://doi.org/10.1007/978-3-642-36285-9_15openalex-snapshot
Alignment Based Precision Checking
inferredcites →https://doi.org/10.1007/978-3-642-38709-8_29openalex-snapshot
Understanding Process Behaviours in a Large Insurance Company in Australia: A Case Study
inferredcites →https://doi.org/10.1007/978-3-662-49851-4openalex-snapshot
Process Mining: Data Science in Action
inferredcites →https://doi.org/10.1007/s10115-004-0174-5openalex-snapshot
Reliable detection of episodes in event sequences
inferredcites →https://doi.org/10.1007/s10115-006-0026-6openalex-snapshot
Automatic outlier detection for time series: an application to sensor data
inferredcites →https://doi.org/10.1007/s10115-006-0034-6openalex-snapshot
Finding the most unusual time series subsequence: algorithms and applications
inferredcites →https://doi.org/10.1007/s10115-008-0131-9openalex-snapshot
Disk aware discord discovery: finding unusual time series in terabyte sized datasets
inferredcites →https://doi.org/10.1016/j.dss.2017.04.005openalex-snapshot
Fodina: A robust and flexible heuristic process discovery technique
inferredcites →https://doi.org/10.1109/cidm.2011.5949453openalex-snapshot
Flexible Heuristics Miner (FHM)
inferredcites →https://doi.org/10.1109/edoc.2011.12openalex-snapshot
Conformance Checking Using Cost-Based Fitness Analysis
inferredcites →https://doi.org/10.1109/icde.2015.7113270openalex-snapshot
Cleaning structured event logs: A graph repair approach
inferredcites →https://doi.org/10.1109/ssdm.2004.1311192openalex-snapshot
Mining deviants in time series data streams
inferredcites →https://doi.org/10.1109/tkde.2004.47openalex-snapshot
Workflow mining: discovering process models from event logs
inferredcites →https://doi.org/10.1109/tkde.2010.235openalex-snapshot
Anomaly Detection for Discrete Sequences: A Survey
inferredcites →https://doi.org/10.1109/tkde.2013.184openalex-snapshot
Outlier Detection for Temporal Data: A Survey
inferredcites →https://doi.org/10.1109/tsmcc.2008.2007248openalex-snapshot
Anomaly Detection and Diagnosis Algorithms for Discrete Symbol Sequences with Applications to Airline Safety
inferredcites →https://doi.org/10.1137/1.9781611972764.9openalex-snapshot
Mining for Outliers in Sequential Databases
inferredcites →https://doi.org/10.1137/1.9781611973440.9openalex-snapshot
Local Learning for Mining Outlier Subgraphs from Network Datasets
inferredcites →https://doi.org/10.1145/1401890.1401915openalex-snapshot
Anomaly pattern detection in categorical datasets
inferredcites →https://doi.org/10.1145/322510.322526openalex-snapshot
Temporal sequence learning and data reduction for anomaly detection
inferredcites →https://doi.org/10.1145/775047.775128openalex-snapshot
Finding surprising patterns in a time series database in linear time and space
inferredcites →https://doi.org/10.3233/fi-2009-136openalex-snapshot
Process Discovery using Integer Linear Programming
inferredcites →https://doi.org/10.3233/jcs-980109openalex-snapshot
Intrusion detection using sequences of system calls
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4656941openalex-extracted
Mini-Process Cubes: Enabling Real-Time Analysis and Process Discovery in Stream Process Mining
inferred← citeshttps://doi.org/10.1002/cpe.70380openalex-snapshot
Heuristic‐Enhanced ILP Process Discovery With Multidimensional Dependency Filtering
inferred← citeshttps://doi.org/10.1002/hpm.3593openalex-snapshot
Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective
inferred← citeshttps://doi.org/10.1007/978-3-030-02671-4_6openalex-snapshot
Applying Sequence Mining for Outlier Detection in Process Mining
inferred← citeshttps://doi.org/10.1007/978-3-030-30278-8_39openalex-snapshot
The Impact of Event Log Subset Selection on the Performance of Process Discovery Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-030-30949-7_2openalex-snapshot
Modeling Users Behavior in Groupware Applications
inferred← citeshttps://doi.org/10.1007/978-3-030-37453-2_20openalex-snapshot
Discovering Process Models from Uncertain Event Data
inferred← citeshttps://doi.org/10.1007/978-3-030-49418-6_10openalex-snapshot
Truncated Trace Classifier. Removal of Incomplete Traces from Event Logs
inferred← citeshttps://doi.org/10.1007/978-3-030-49418-6_9openalex-snapshot
Cherry-Picking from Spaghetti: Multi-range Filtering of Event Logs
inferred← citeshttps://doi.org/10.1007/978-3-030-53993-1_8openalex-snapshot
Bringing Interactive Process Mining to Health Professionals: Interactive Data Rodeos
inferred← citeshttps://doi.org/10.1007/978-3-030-61527-7_40openalex-snapshot
Detecting Temporal Anomalies in Business Processes Using Distance-Based Methods
inferred← citeshttps://doi.org/10.1007/978-3-030-63119-2_47openalex-snapshot
Lambda Architecture for Anomaly Detection in Online Process Mining Using Autoencoders
inferred← citeshttps://doi.org/10.1007/978-3-030-65310-1_39openalex-snapshot
A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits
inferred← citeshttps://doi.org/10.1007/978-3-030-66498-5_21openalex-snapshot
Prototype Selection Using Clustering and Conformance Metrics for Process Discovery
inferred← citeshttps://doi.org/10.1007/978-3-030-66498-5_23openalex-snapshot
A Generic Framework for Attribute-Driven Hierarchical Trace Clustering
inferred← citeshttps://doi.org/10.1007/978-3-030-72693-5_11openalex-snapshot
An Alignment Cost-Based Classification of Log Traces Using Machine-Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-85469-0_14openalex-snapshot
A Real-Time Method for Detecting Temporary Process Variants in Event Log Data
inferred← citeshttps://doi.org/10.1007/978-3-030-95405-5_28openalex-snapshot
Cybersecurity Analysis via Process Mining: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1007/978-3-031-01333-1_21openalex-snapshot
A Generic Trace Ordering Framework for Incremental Process Discovery
inferred← citeshttps://doi.org/10.1007/978-3-031-06653-5_2openalex-snapshot
Discovering Unseen Behaviour from Event Logs
inferred← citeshttps://doi.org/10.1007/978-3-031-08848-3_14openalex-snapshot
Using Process Mining in Healthcare
inferred← citeshttps://doi.org/10.1007/978-3-031-08848-3_3openalex-snapshot
Advanced Process Discovery Techniques
inferred← citeshttps://doi.org/10.1007/978-3-031-16171-1_7openalex-snapshot
Conformance Checking over Stochastically Known Logs
inferred← citeshttps://doi.org/10.1007/978-3-031-25383-6_21openalex-snapshot
Improving Declarative Process Mining with a Priori Noise Filtering
inferred← citeshttps://doi.org/10.1007/978-3-031-27815-0_16openalex-snapshot
Detecting Surprising Situations in Event Data
inferred← citeshttps://doi.org/10.1007/978-3-031-34674-3_1openalex-snapshot
Context-Aware Change Pattern Detection in Event Attributes of Recurring Activities
inferred← citeshttps://doi.org/10.1007/978-3-031-46846-9_16openalex-snapshot
Rectify Sensor Data in IoT: A Case Study on Enabling Process Mining for Logistic Process in an Air Cargo Terminal
inferred← citeshttps://doi.org/10.1007/978-3-031-94590-8_4openalex-snapshot
Lost in Models? Structuring Managerial Decision Support in Process Mining with Multi-criteria Decision Making
inferred← citeshttps://doi.org/10.1007/978-3-031-99997-0_8openalex-snapshot
Mini-Process Cubes: Enabling Real-Time Analysis and Process Discovery in Online Process Mining
inferred← citeshttps://doi.org/10.1007/978-3-032-02929-4_3openalex-snapshot
The WHY in Business Processes: Unification of Causal Process Models
inferred← citeshttps://doi.org/10.1007/978-3-032-15538-2_3openalex-snapshot
eRooMiner: A Data-Driven Approach for Root Cause Detection of Process Data Quality Issues
inferred← citeshttps://doi.org/10.1007/978-3-032-17618-9_27openalex-snapshot
Process-Data Quality Management Lifecycle: Building Strong Data Foundations for Process Mining
inferred← citeshttps://doi.org/10.1007/978-3-032-28110-4_18openalex-snapshot
Conditioning and Stability in Process Discovery
inferred← citeshttps://doi.org/10.1007/978-3-032-37777-7_34openalex-snapshot
Linear Continuous Process Maps
inferred← citeshttps://doi.org/10.1007/978-3-319-59536-8_14openalex-snapshot
Characterizing Drift from Event Streams of Business Processes
inferred← citeshttps://doi.org/10.1007/978-3-319-59536-8_34openalex-snapshot
Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
inferred← citeshttps://doi.org/10.1007/978-3-319-63962-8_100-1openalex-snapshot
Business Process Deviance Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-63962-8_87-1openalex-snapshot
Event Log Cleaning for Business Process Analytics
inferred← citeshttps://doi.org/10.1007/978-3-319-65000-5_19openalex-snapshot
Discovering Infrequent Behavioral Patterns in Process Models
inferred← citeshttps://doi.org/10.1007/978-3-319-74030-0_16openalex-snapshot
Improving Process Discovery Results by Filtering Outliers Using Conditional Behavioural Probabilities
inferred← citeshttps://doi.org/10.1007/978-3-319-77525-8_100openalex-snapshot
Business Process Deviance Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-77525-8_87openalex-snapshot
Event Log Cleaning for Business Process Analytics
inferred← citeshttps://doi.org/10.1007/978-3-319-91563-0_17openalex-snapshot
Detection and Interactive Repair of Event Ordering Imperfection in Process Logs
inferred← citeshttps://doi.org/10.1007/978-3-319-93931-5_9openalex-snapshot
Repairing Outlier Behaviour in Event Logs
inferred← citeshttps://doi.org/10.1007/978-981-16-3153-5_22openalex-snapshot
Anomaly Detection in Business Process Event Using KNN Algorithm
inferred← citeshttps://doi.org/10.1007/s00170-026-18592-7openalex-snapshot
Data-driven analysis of product routings in discrete manufacturing using process mining and machine learning
inferred← citeshttps://doi.org/10.1007/s00607-017-0582-5openalex-snapshot
Discovering workflow nets using integer linear programming
inferred← citeshttps://doi.org/10.1007/s00607-021-00910-4openalex-snapshot
The impact of biased sampling of event logs on the performance of process discovery
inferred← citeshttps://doi.org/10.1007/s10115-018-1214-xopenalex-snapshot
Split miner: automated discovery of accurate and simple business process models from event logs
inferred← citeshttps://doi.org/10.1007/s10115-019-01376-9openalex-snapshot
Information-preserving abstractions of event data in process mining
inferred← citeshttps://doi.org/10.1007/s10115-022-01821-2openalex-snapshot
An integer linear programming model to improve the dependency graph discovery step of heuristics miner methods
inferred← citeshttps://doi.org/10.1007/s10270-021-00902-0openalex-snapshot
Interactive log-delta analysis using multi-range filtering
inferred← citeshttps://doi.org/10.1007/s10664-020-09911-xopenalex-snapshot
Discovering configuration workflows from existing logs using process mining
inferred← citeshttps://doi.org/10.1007/s10844-018-0507-6openalex-snapshot
Discovering more precise process models from event logs by filtering out chaotic activities
inferred← citeshttps://doi.org/10.1007/s10844-025-00955-3openalex-snapshot
Handling concept drifts with traditional process discovery algorithms
inferred← citeshttps://doi.org/10.1007/s10994-022-06152-5openalex-snapshot
A comparison of proximity-based methods for detecting temporal anomalies in business processes
inferred← citeshttps://doi.org/10.1007/s11227-019-02996-2openalex-snapshot
Measuring data-centre workflows complexity through process mining: the Google cluster case
inferred← citeshttps://doi.org/10.1007/s11227-022-04637-7openalex-snapshot
Improving heuristics miners for healthcare applications by discovering optimal dependency graphs
inferred← citeshttps://doi.org/10.1007/s12599-020-00641-4openalex-snapshot
Robotic Process Mining: Vision and Challenges
inferred← citeshttps://doi.org/10.1007/s13740-021-00121-2openalex-snapshot
AI-Empowered Process Mining for Complex Application Scenarios: Survey and Discussion
inferred← citeshttps://doi.org/10.1007/s41066-020-00226-2openalex-snapshot
Event abstraction in process mining: literature review and taxonomy
inferred← citeshttps://doi.org/10.1007/s42979-026-04724-zopenalex-snapshot
Data Imputation for Business Process Event Logs
inferred← citeshttps://doi.org/10.1007/s44311-025-00027-3openalex-snapshot
Generalization estimation in process mining: the impact of event data quality
inferred← citeshttps://doi.org/10.1016/j.aci.2020.01.001openalex-snapshot
Model discovery, and replay fitness validation using inductive mining techniques in medical training of CVC surgery
inferred← citeshttps://doi.org/10.1016/j.array.2021.100120openalex-snapshot
Process mining usage in cybersecurity and software reliability analysis: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104312openalex-snapshot
Using approximate matching and machine learning to uncover malicious activity in logs
inferred← citeshttps://doi.org/10.1016/j.datak.2018.04.007openalex-snapshot
Automated discovery of structured process models from event logs: The discover-and-structure approach
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106221openalex-snapshot
Frequent pattern mining-based log file partition for process mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.114662openalex-snapshot
An end-to-end approach and tool for BPMN process discovery
inferred← citeshttps://doi.org/10.1016/j.future.2020.07.030openalex-snapshot
Understanding complex process models by abstracting infrequent behavior
inferred← citeshttps://doi.org/10.1016/j.ins.2022.05.052openalex-snapshot
PROMISE: Coupling predictive process mining to process discovery
inferred← citeshttps://doi.org/10.1016/j.ins.2022.07.170openalex-snapshot
Efficient edge filtering of directly-follows graphs for process mining
inferred← citeshttps://doi.org/10.1016/j.is.2017.12.005openalex-snapshot
Event-based failure prediction in distributed business processes
inferred← citeshttps://doi.org/10.1016/j.is.2019.101438openalex-snapshot
Mining association rules for anomaly detection in dynamic process runtime behavior and explaining the root cause to users
inferred← citeshttps://doi.org/10.1016/j.is.2019.101451openalex-snapshot
Detection and removal of infrequent behavior from event streams of business processes
inferred← citeshttps://doi.org/10.1016/j.is.2020.101561openalex-snapshot
Scalable alignment of process models and event logs: An approach based on automata and S-components
inferred← citeshttps://doi.org/10.1016/j.is.2021.101810openalex-snapshot
Conformance checking over uncertain event data
inferred← citeshttps://doi.org/10.1016/j.is.2023.102180openalex-snapshot
Process mining meets model learning: Discovering deterministic finite state automata from event logs for business process analysis
inferred← citeshttps://doi.org/10.1016/j.jbi.2022.104081openalex-snapshot
Process mining-driven analysis of COVID-19’s impact on vaccination patterns
inferred← citeshttps://doi.org/10.1016/j.knosys.2019.105054openalex-snapshot
Discovering process models for the analysis of application failures under uncertainty of event logs
inferred← citeshttps://doi.org/10.1016/j.procs.2021.08.005openalex-snapshot
Semi-Supervised Anomaly Detection in Business Process Event Data using Self-Attention based Classification
inferred← citeshttps://doi.org/10.1016/j.procs.2022.09.056openalex-snapshot
Catch Me If You Can: Online Classification for Near Real-Time Anomaly Detection in Business Process Event Streams
inferred← citeshttps://doi.org/10.1016/j.sysarc.2022.102435openalex-snapshot
Process scenario discovery from event logs based on activity and timing information
inferred← citeshttps://doi.org/10.1109/access.2019.2922635openalex-snapshot
$\beta$ Algorithm: A New Probabilistic Process Learning Approach for Big Data in Healthcare
inferred← citeshttps://doi.org/10.1109/access.2020.3012147openalex-snapshot
Conformance Checking: Workflow of Hospitals and Workflow of Open-Source EMRs
inferred← citeshttps://doi.org/10.1109/access.2020.3048486openalex-snapshot
Real-Time Repair of Business Processes Based on Alternative Operations in Case of Uncertainty
inferred← citeshttps://doi.org/10.1109/access.2021.3121997openalex-snapshot
Process Model Enhancement Through Capturing Important Behaviors and Rating Trace Variants
inferred← citeshttps://doi.org/10.1109/access.2021.3135298openalex-snapshot
Improving Heuristic Process Discovery Methods Through Determining the Optimal Split/Join Patterns of Dependency Graphs
inferred← citeshttps://doi.org/10.1109/access.2022.3185235openalex-snapshot
Improving Process Discovery Algorithms Using Event Concatenation
inferred← citeshttps://doi.org/10.1109/access.2022.3208231openalex-snapshot
Complex Process Modeling in Process Mining: A Systematic Review
inferred← citeshttps://doi.org/10.1109/access.2024.3411089openalex-snapshot
Enhancement in Process Mining Model by Repairing Noisy Behavior in Event Log
inferred← citeshttps://doi.org/10.1109/access.2025.3571441openalex-snapshot
Leveraging Dual Timestamps to Enhance Process Model Construction Using Time-Infused Inductive Miner
inferred← citeshttps://doi.org/10.1109/aike48582.2020.00017openalex-snapshot
Using Event Log Timing Information to Assist Process Scenario Discoveries
inferred← citeshttps://doi.org/10.1109/cbi52690.2021.00028openalex-snapshot
Improving Process Discovery Results by Filtering Out Outliers from Event Logs with Hidden Markov Models
inferred← citeshttps://doi.org/10.1109/cis2018.2018.00089openalex-snapshot
Filtering Out Infrequent Events by Expectation from Business Process Event Logs
inferred← citeshttps://doi.org/10.1109/compsac.2019.10285openalex-snapshot
An Emperical Study on Application of Big Data Analytics to Automate Service Desk Business Process
inferred← citeshttps://doi.org/10.1109/compsac54236.2022.00047openalex-snapshot
Using Domain Knowledge to Assist Process Scenario Discoveries
inferred← citeshttps://doi.org/10.1109/fuzz-ieee55066.2022.9882673openalex-snapshot
Explanation of Anomalies in Business Process Event Logs with Linguistic Summaries
inferred← citeshttps://doi.org/10.1109/icaccs51430.2021.9441949openalex-snapshot
Excavation of Time sliced and Cost based KDD for the lead generation and promotion on B2C/B2B Sales
inferred← citeshttps://doi.org/10.1109/icccnt56998.2023.10308399openalex-snapshot
Evaluating the Effectiveness of Multi-Filtering Techniques on Comprehensibility Improvement of Spaghetti Models
inferred← citeshttps://doi.org/10.1109/icdm.2017.9openalex-snapshot
Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs
inferred← citeshttps://doi.org/10.1109/icpm.2019.00023openalex-snapshot
Mining Uncertain Event Data in Process Mining
inferred← citeshttps://doi.org/10.1109/icpm49681.2020.00017openalex-snapshot
Collaborative and Interactive Detection and Repair of Activity Labels in Process Event Logs
inferred← citeshttps://doi.org/10.1109/icpm53251.2021.9576679openalex-snapshot
Selecting Representative Sample Traces from Large Event Logs
inferred← citeshttps://doi.org/10.1109/icpm60904.2023.10271985openalex-snapshot
SKTR: Trace Recovery from Stochastically Known Logs
inferred← citeshttps://doi.org/10.1109/icpm63005.2024.10680679openalex-snapshot
The Role of Log Representativeness in Estimating Generalization in Process Mining
inferred← citeshttps://doi.org/10.1109/icws.2019.00069openalex-snapshot
Filtering Out Noise Logs for Process Modelling Based on Event Dependency
inferred← citeshttps://doi.org/10.1109/iisa.2019.8900713openalex-snapshot
Skip Miner: Towards the Simplification of Spaghetti-like Business Process Models
inferred← citeshttps://doi.org/10.1109/iscmi53840.2021.9654821openalex-snapshot
Attention-Based Anomaly Detection in Hospital Process Event Data
inferred← citeshttps://doi.org/10.1109/tcss.2021.3063538openalex-snapshot
Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach
inferred← citeshttps://doi.org/10.1109/tfuzz.2022.3197876openalex-snapshot
Deep Fuzzy Contrast-Set Deviation Point Representation and Trajectory Detection
inferred← citeshttps://doi.org/10.1109/tits.2020.3022612openalex-snapshot
A Two-Phase Anomaly Detection Model for Secure Intelligent Transportation Ride-Hailing Trajectories
inferred← citeshttps://doi.org/10.1109/tkde.2018.2841877openalex-snapshot
Automated Discovery of Process Models from Event Logs: Review and Benchmark
inferred← citeshttps://doi.org/10.1109/tkde.2019.2897557openalex-snapshot
Efficient Process Conformance Checking on the Basis of Uncertain Event-to-Activity Mappings
inferred← citeshttps://doi.org/10.1109/tkde.2020.3003258openalex-snapshot
Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach
inferred← citeshttps://doi.org/10.1109/tkde.2021.3052927openalex-snapshot
Discovering Structural Errors From Business Process Event Logs
inferred← citeshttps://doi.org/10.1109/tsc.2020.3032787openalex-snapshot
LCDD: Detecting Business Process Drifts Based on Local Completeness
inferred← citeshttps://doi.org/10.1109/tsc.2026.3664101openalex-snapshot
A Consumption-Generation Driven Deep Embedding Framework for Next Activity Prediction
inferred← citeshttps://doi.org/10.1109/tsmc.2018.2852652openalex-snapshot
Measurement and Computation of Profile Similarity of Workflow Nets Based on Behavioral Relation Matrix
inferred← citeshttps://doi.org/10.1142/s0218213018600096openalex-snapshot
Deviance-Aware Discovery of High-Quality Process Models
inferred← citeshttps://doi.org/10.1145/3289181openalex-snapshot
Local Concurrency Detection in Business Process Event Logs
inferred← citeshttps://doi.org/10.1145/3336294.3336303openalex-snapshot
Process Mining to Unleash Variability Management
inferred← citeshttps://doi.org/10.1145/3472752openalex-snapshot
A Survey on Concept Drift in Process Mining
inferred← citeshttps://doi.org/10.1145/3511707openalex-snapshot
Quality-Informed Process Mining: A Case for Standardised Data Quality Annotations
inferred← citeshttps://doi.org/10.1145/3613247openalex-snapshot
Process-Data Quality: The True Frontier of Process Mining
inferred← citeshttps://doi.org/10.1145/3672447openalex-snapshot
On Process Discovery Experimentation: Addressing the Need for Research Methodology in Process Discovery
inferred← citeshttps://doi.org/10.1145/3712587openalex-snapshot
Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage
inferred← citeshttps://doi.org/10.1145/3840381openalex-snapshot
PraeclarusPDQ: A Reference Architecture for Process Data Quality Management
inferred← citeshttps://doi.org/10.1155/2021/8874316openalex-snapshot
An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow
inferred← citeshttps://doi.org/10.1155/2022/4741232openalex-snapshot
Repairing Event Logs to Enhance the Performance of a Process Mining Model
inferred← citeshttps://doi.org/10.11606/d.100.2022.tde-04052022-215626openalex-snapshot
Detecção de anomalias em logs de eventos de processos de negócio: um estudo comparativo entre abordagens baseadas em redes neurais e baseadas em contagem
inferred← citeshttps://doi.org/10.1177/1088467x251395608openalex-snapshot
Sub-trajectory anomaly detection based on trajectory segmentation merging and grouping clustering
inferred← citeshttps://doi.org/10.1186/s40537-018-0135-6openalex-snapshot
Cross-domain similarity assessment for workflow improvement to handle Big Data challenge in workflow management
inferred← citeshttps://doi.org/10.19101/ijacr.2018.839057openalex-snapshot
An efficient parallel framework for process discovery using OpenMP
inferred← citeshttps://doi.org/10.21203/rs.3.rs-10398302/v1openalex-snapshot
A Scoring-Based Approach for Structuredness-Aware Business Process Classification
inferred← citeshttps://doi.org/10.22452/mjcs.vol36no3.2openalex-snapshot
A TRACE CLUSTERING FRAMEWORK FOR IMPROVING THE BEHAVIORAL AND STRUCTURAL QUALITY OF PROCESS MODELS IN PROCESS MINING
inferred← citeshttps://doi.org/10.2298/csis200127028sopenalex-snapshot
Improving the performance of process discovery algorithms by instance selection
inferred← citeshttps://doi.org/10.24251/hicss.2021.130openalex-snapshot
Business process analysis based on anomaly detection in event logs: a study on an incident management case
inferred← citeshttps://doi.org/10.24425/bpasts.2022.143551openalex-snapshot
Continuous update of business process trees using Continuous Inductive Miner
inferred← citeshttps://doi.org/10.3233/shti210583openalex-snapshot
Data-Driven Modeling of Complex Business Process in Heterogeneous Environment of Healthcare Organization with Health Information Systems
inferred← citeshttps://doi.org/10.3390/a15060180openalex-snapshot
Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs
inferred← citeshttps://doi.org/10.3390/app112210556openalex-snapshot
Event Log Preprocessing for Process Mining: A Review
inferred← citeshttps://doi.org/10.3390/app13053143openalex-snapshot
Experimental Analyses of Temporal Activity-Sequencing Anomalies in Process Mining
inferred← citeshttps://doi.org/10.3390/app14166994openalex-snapshot
Pre-Processing Event Logs by Chaotic Filtering Approaches Based on the Direct Following Relationship
inferred← citeshttps://doi.org/10.3390/math11132858openalex-snapshot
Event Log Data Quality Issues and Solutions
inferred← citeshttps://doi.org/10.37624/ijert/13.10.2020.3055-3066openalex-snapshot
Process Model Discovery from Unlabeled Event Logs by Using Non-Overlapping Sequential Distinct Event Patterns
inferred← citeshttps://doi.org/10.4018/ijcini.2020040101openalex-snapshot
Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation
inferred← citeshttps://doi.org/10.48175/ijarsct-12057openalex-snapshot
Pattern Based Sequence Classification
inferred← citeshttps://doi.org/10.48550/arxiv.1705.06659openalex-snapshot
Anomaly Detection in Business Process Runtime Behavior -- Challenges and Limitations
inferred← citeshttps://doi.org/10.48550/arxiv.1909.01421openalex-snapshot
Mining Insights from Weakly-Structured Event Data
inferred← citeshttps://doi.org/10.48550/arxiv.1912.00736openalex-snapshot
Prototype Selection Based on Clustering and Conformance Metrics for Model Discovery
inferred← citeshttps://doi.org/10.48550/arxiv.2004.07194openalex-snapshot
Effective Removal of Operational Log Messages: an Application to Model Inference
inferred← citeshttps://doi.org/10.5204/thesis.eprints.127157openalex-snapshot
Business Process Drift: Detection and Characterization
inferred← citeshttps://doi.org/10.5204/thesis.eprints.229543openalex-snapshot
The quality guardian: Improving activity label quality in event logs through Gamification
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.