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Process mining

Process mining is a family of a posteriori data analysis techniques that leverage event logs from information systems to discover, monitor, and enhance the actual execution of business processes.^[1] These event logs typically record sequences of activities, including timestamps, resources, and case identifiers, providing an objective basis for reconstructing how processes unfold in reality rather than relying on subjective descriptions or predefined models.^[2] Originating in the late 1990s as part of efforts to bridge business process management and data mining, process mining has evolved into a mature discipline supported by open-source tools like ProM—which began with 29 plug-ins in 2004 and now exceeds 1,500—and over 40 commercial platforms used across industries such as healthcare, finance, and logistics.^[3] At its core, process mining encompasses three main types of analysis: discovery, which automatically generates process models from event logs without prior knowledge; conformance checking, which detects deviations between observed behavior and normative models to ensure compliance and identify inefficiencies; and enhancement, which refines existing models by incorporating performance metrics, such as bottlenecks or resource utilization, to support predictive and prescriptive improvements.^[1] This fact-based approach enables organizations to move beyond traditional process mapping methods—like interviews or simulations—toward evidence-driven insights that reveal hidden patterns, root causes of delays, and opportunities for automation.^[3] Pioneered by researchers including Wil van der Aalst, the field addresses challenges such as handling noisy or incomplete data, modeling concurrency, and scaling to large event logs, as outlined in foundational manifestos and research agendas.^[2] By integrating with technologies like robotic process automation and artificial intelligence, process mining facilitates continuous process optimization in dynamic environments.^[3]

Introduction

Definition and Scope

Process mining is a data-driven discipline that extracts knowledge from event logs generated by information systems to discover, monitor, and improve real-world business processes.^[4] Unlike traditional process modeling, which depends on expert-designed models often detached from actual execution, process mining uses empirical data to reveal how processes truly operate, bridging the gap between normative prescriptions and descriptive realities. This approach enables organizations to analyze processes based on recorded events rather than assumptions or simulations. The scope of process mining encompasses three primary pillars: process discovery, conformance checking, and enhancement.^[4] In process discovery, algorithms construct a process model directly from the event log without relying on a pre-existing model, capturing the actual sequence and structure of activities.^[5] Conformance checking compares the observed behavior in the event log against a reference model to detect deviations, bottlenecks, or compliance issues.^[4] Enhancement extends or refines an existing model by incorporating additional insights from the log, such as performance metrics or resource assignments.^[5] These pillars operate across multiple perspectives, including control-flow (the order of activities), performance (timing and durations), organizational (resource involvement and handovers), and case (attributes specific to individual process instances).^[6] At its core, process mining relies on event logs as input, which consist of structured records of events linked to process instances.^[7] Each event typically includes a case ID to identify the process instance, an activity describing the executed step, and a timestamp indicating when the event occurred; additional attributes like resources or costs may also be present.^[8] Outputs include visual process models (e.g., Petri nets or BPMN diagrams), diagnostic reports, and quantitative insights into process efficiency or variants.^[4] Process mining relates to but distinguishes itself from precursor fields like workflow mining, an early term from the late 1990s focused primarily on discovering workflow models from logs in enterprise systems.^[9] It also differs from general data mining, which identifies arbitrary patterns across datasets, by emphasizing the sequential and relational nature of events to reconstruct and optimize structured processes rather than isolated correlations.^[6]

Importance and Benefits

Process mining plays a pivotal role in the era of digital transformation by enabling organizations to navigate the complexities of interconnected IT systems and fragmented data sources. As businesses increasingly adopt hybrid environments with multiple applications and automation tools, process mining extracts actionable insights from event data to reveal how processes truly operate across silos, facilitating more effective integration and optimization. This capability is essential for handling the scale and variability of modern IT landscapes, where traditional process modeling often falls short due to incomplete visibility.^[10] One of the primary benefits of process mining is its ability to identify bottlenecks, deviations, and inefficiencies in real-world processes, allowing for targeted data-driven redesign. By analyzing event logs, it uncovers variations from intended workflows, such as unnecessary loops or delays, which can lead to substantial efficiency gains; for instance, case studies have demonstrated reductions in processing times by up to 56% and operational costs by 30% in customer service scenarios. Additionally, process mining supports compliance and auditing by performing conformance checking to verify adherence to regulations and internal policies, reducing risks of non-compliance and enabling auditors to focus on anomalies rather than manual sampling.^[11]^[12]^[13] The broader impact of process mining extends to fostering continuous improvement in volatile business environments, where rapid changes demand adaptive strategies. It quantifies return on investment through metrics like throughput time reductions, with reported efficiency gains of 20-30% in key processes across industries such as finance and manufacturing, thereby justifying investments in process optimization. In agile methodologies and DevOps practices, process mining provides empirical visibility into deployment pipelines and iterative cycles, helping teams detect waste and enhance flow efficiency—for example, by monitoring lead times and failure rates to support faster, more reliable deliveries.^[11]^[14]^[10]

Historical Development

Origins and Key Milestones

Process mining emerged in the late 1990s as an extension of workflow management research, driven by the need to automatically discover process models from event data rather than relying solely on manual modeling.^[15] Pioneering work at Eindhoven University of Technology, led by Wil van der Aalst, introduced the concept through a 1999 research proposal titled "Process Design by Discovery: Harvesting Workflow Knowledge from Ad-hoc Processes," which first coined the term "process mining" and highlighted the potential to extract workflows from transaction logs in information systems.^[16] This shift addressed the limitations of traditional workflow systems, where predefined models often failed to capture real-world deviations, prompting a data-driven approach to process analysis.^[1] Key milestones in the field's early development include the publication of van der Aalst and Kees van Hee's book Workflow Management: Models, Methods, and Systems in 2002, which provided foundational models for integrating process discovery techniques into broader workflow paradigms and emphasized the role of event logs in verification and simulation.^[17] The first international workshop on Business Process Intelligence (BPI'05) was held on September 5, 2005, in Nancy, France, in conjunction with the BPM conference, fostering collaboration on process mining methods and marking the beginning of dedicated academic forums for the topic.^[18] A significant advancement came in 2009 when the IEEE Task Force on Process Mining proposed the eXtensible Event Stream (XES) as a standardized XML-based format for event logs, enabling interoperability across tools and addressing fragmentation in data representation.^[19] In 2012, the IEEE Task Force on Process Mining published the Process Mining Manifesto, providing guiding principles for the discipline.^[5] Early challenges centered on the absence of standardized data formats, which hindered the extraction and sharing of event logs from diverse systems like ERP and CRM software, often resulting in inconsistent inputs for mining algorithms.^[20] Additionally, the transition from manual process modeling to automated discovery required overcoming issues like noisy or incomplete logs, as highlighted in initial research agendas that stressed the need for robust techniques to handle real-world variability without overfitting to artifacts.^[20] These hurdles spurred innovations in log preprocessing and model validation, laying the groundwork for process mining's evolution into a mature discipline.^[1]

Integration with Data Science

Process mining occupies a unique position within data science as a discipline that bridges process-oriented analysis with machine learning and big data processing techniques, effectively situating it between business process management (BPM) and traditional data mining.^[21] It leverages event log data to extract actionable insights into operational workflows, enabling data scientists to model, predict, and optimize processes in ways that complement broader analytical paradigms like predictive modeling and pattern recognition.^[22] This integration allows for the discovery of real-world process deviations and efficiencies that pure data mining might overlook, fostering a hybrid approach that enhances decision-making in complex environments.^[23] The evolution of process mining's ties to data science accelerated in the 2010s with the widespread adoption of predictive analytics, where techniques like predictive process monitoring emerged to forecast process outcomes using historical event data and machine learning algorithms. By the 2020s, deeper integration with artificial intelligence has enabled advanced applications such as anomaly detection, where AI models identify irregularities in process flows to support proactive interventions in real-time operations.^[24] This progression has been bolstered by open standards, notably the PM4Py library released in 2019, which provides Python-based tools for scalable process analysis and has facilitated broader experimentation and adoption in data science workflows.^[25] Key academic contributions to this integration stem from research groups at RWTH Aachen University and Eindhoven University of Technology, where pioneers like Wil van der Aalst have advanced foundational algorithms and their extensions into machine learning contexts since the early 2000s.^[26]^[27] Industry recognition has grown correspondingly, with Gartner reporting the process mining software market reached $1.1 billion in 2024, reflecting a 31.7% year-over-year growth (as of August 2025).^[28] As of 2025, regulatory influences like the EU's General Data Protection Regulation (GDPR) have driven innovations in privacy-preserving process mining, incorporating techniques such as microaggregation and differential privacy to anonymize sensitive event data while maintaining analytical utility.^[29]^[30] Concurrently, hybrid models combining process mining with graph databases have gained traction, representing event logs as knowledge graphs to handle multi-entity interactions and enable more nuanced analyses of interconnected processes.^[31]^[32]

Core Concepts

Event Logs and Data Sources

Event logs serve as the foundational data structure in process mining, capturing the actual executions of business processes in the form of discrete events.^[33] Each event log consists of a collection of cases, where a case represents a specific instance of a process, such as an order fulfillment or a customer support ticket, and is recorded as a trace—a sequence of events ordered chronologically.^[34] This structure enables the reconstruction of process behavior from raw data, allowing analysts to derive insights into how processes unfold in practice.^[35] Core attributes of events within these logs include the activity name, which describes the performed step (e.g., "invoice payment"); a timestamp indicating when the activity occurred; a resource identifier, such as the user or machine involved; and optional attributes like costs or data elements.^[36] Case-level attributes may also apply, such as the customer ID or total duration, while event attributes provide granular details tied to individual steps.^[37] The eXtensible Event Stream (XES) format, standardized as IEEE 1849, represents event logs in an XML-based structure to ensure interoperability across tools, supporting extensions for custom attributes and classifications.^[38]^[39] Event logs are typically extracted from various enterprise information systems that record transactional data. Common sources include Enterprise Resource Planning (ERP) systems like SAP or Oracle, Customer Relationship Management (CRM) platforms such as Salesforce, hospital information systems for healthcare processes, and audit trails from custom applications.^[40]^[41] These systems generate logs through database transactions, workflow engines, or application interfaces, providing a digital footprint of process executions.^[42] The extraction process often involves Extract, Transform, Load (ETL) pipelines to convert raw data into a suitable event log format, addressing challenges like data incompleteness, noise from irrelevant entries, or inconsistencies in recording.^[43] For instance, relational database queries from an ERP system can be transformed into an XES-compatible log by mapping tables to events, filtering out non-process-related records, and enriching timestamps or resources as needed.^[44] This preparation ensures the log's quality for subsequent analysis, though it remains the most resource-intensive step in process mining projects.^[45] Process mining presupposes certain qualities in event logs to yield reliable results, including the assumption that events represent atomic activities—indivisible steps without internal subprocesses—and that timestamps are complete and accurate for ordering events within a trace.^[7] Events are also expected to be chronologically ordered per case, with each tied to exactly one process instance to avoid ambiguity in trace reconstruction.^[33] These event logs ultimately feed into the discovery of process models, which represent the abstracted behavior observed in the data.^[34]

Process Models and Representations

Process models in process mining serve as graphical representations that capture the structure and behavior of business processes, primarily focusing on the control-flow perspective, which includes sequences, choices, parallel executions, and loops. Common notations include Petri nets, which use places, transitions, and tokens to model concurrency and synchronization; Business Process Model and Notation (BPMN), which employs activities, events, and gateways for intuitive diagramming of control flows; and Event-driven Process Chains (EPCs), which connect events and functions with logical operators to depict process logic.^[46]^[47] These models are derived from event logs, enabling the visualization of actual process executions rather than assumed designs.^[48] To handle real-world complexities such as noisy or incomplete data, specialized representations are employed. The heuristics miner produces dependency graphs that filter infrequent or unreliable connections based on dependency and frequency thresholds, yielding robust models from imperfect event logs.^[49] Fuzzy models, on the other hand, address process variants by allowing configurable abstraction levels, where edges are weighted by significance metrics (e.g., correlation and routing probability) to simplify complex, unstructured processes into hierarchical views.^[50] Extensions incorporate additional dimensions, such as the Directly Follows Graph (DFG), a simple directed graph of activities and their immediate successors, often augmented with timestamps for temporal analysis.^[51] Resource perspectives extend models to include organizational elements, like resource roles and handover patterns.^[52] Process models encompass multiple perspectives beyond control-flow. The organizational perspective examines resource involvement, identifying roles, bottlenecks, and delegation patterns to reveal staffing efficiencies.^[53] The social perspective maps interactions between resources, such as collaboration networks and work handovers, highlighting team dynamics.^[52] The performance perspective leverages timestamps to annotate models with metrics like throughput times and waiting durations, pinpointing delays and inefficiencies.^[51] Model quality is assessed using evaluation metrics that balance behavioral fidelity. Fitness measures how well the model can replay the observed event log, quantifying the proportion of traces that conform without deadlocks or leftover tokens.^[54] Precision evaluates the model's restrictiveness, penalizing over-generalization by comparing allowed behaviors in the model against those in the log to ensure it does not permit excessive deviations. Generalization assesses the model's ability to handle unseen cases beyond the log, avoiding overfitting to specific observed behaviors. Simplicity evaluates the model's structural complexity, favoring concise representations that avoid unnecessary elements.^[55]

Techniques

Process Discovery

Process discovery is a core technique in process mining that aims to automatically construct a model representing the actual (as-is) behavior of a business process solely from event logs, without requiring any a priori knowledge of the process design. These models capture essential control-flow elements such as sequences, choices, loops, and concurrency, enabling analysts to visualize and understand how processes are executed in reality. The primary challenge lies in deriving a model that faithfully reproduces the observed behavior while avoiding over- or under-generalization from potentially noisy or incomplete logs.^[56] The Alpha algorithm, introduced in 2004, represents one of the earliest and most foundational approaches to process discovery. It operates on structured event logs to synthesize Petri nets by constructing a "footprint" matrix that encodes causal relations between activities. Specifically, it identifies directly-follows relations (where one activity immediately precedes another in traces) and always-follows relations (to detect concurrency via the absence of strict ordering), ensuring the resulting model guarantees soundness for workflow nets. However, the algorithm assumes noise-free, complete logs and struggles with unstructured processes or short loops, limiting its applicability to ideal scenarios.^[56] To address the limitations of the Alpha algorithm, particularly its sensitivity to noise and incomplete data, the Heuristics Miner was developed in 2006. This algorithm employs flexible dependency measures, such as the heuristic dependency frequency based on observed frequencies adjusted by a dependency threshold, to infer causal relations robustly even in noisy environments. It filters infrequent behaviors to produce a causal net—a graph-like representation—that can be converted to Petri nets or other models, prioritizing the most common process variants while tolerating deviations like rare loops or parallel executions. A key configurable parameter, often set to a 2:1 ratio of positive to negative dependencies, balances precision against simplicity by suppressing weak relations.^[57] Beyond these foundational methods, various algorithmic variants have emerged to handle more complex scenarios. The Fuzzy Miner, introduced in 2007, is designed for large, unstructured, and noisy event logs, producing simplified graph-based models that highlight significant relations through edge weights based on multi-perspective metrics like frequency and correlation, allowing interactive abstraction to avoid "spaghetti" models.^[58] The Inductive Miner, developed in 2014, uses a divide-and-conquer strategy on the directly-follows graph to split logs into subsets and recursively build block-structured models, guaranteeing soundness and block-structured outputs while handling noise through variants like the infrequent behavior filter.^[59] Genetic miners apply evolutionary optimization principles, where candidate process models (individuals) are iteratively evolved using genetic operators like crossover and mutation, guided by a fitness function that evaluates replayability against the event log. This approach excels in exploring large search spaces for high-fitness models but requires computational resources and parameter tuning.^[60] Region-based discovery techniques, inspired by Petri net synthesis theory, derive structured models by identifying "regions"—sets of states and transitions that separate behaviors in the log's transition system. These methods guarantee concurrency-free synthesis for certain classes of logs but can be computationally intensive due to the need to enumerate minimal regions. Evaluating discovered models relies on four key quality dimensions: fitness (the extent to which the model can replay all log traces without errors), precision (avoiding underfitting by ensuring the model does not permit extraneous behaviors unobserved in the log), generalization (preventing overfitting to specific log instances for broader applicability), and simplicity (favoring parsimonious models per Occam's razor). Algorithms like the Heuristics Miner inherently trade off these dimensions—for instance, lowering the dependency threshold improves fitness and generalization at the cost of reduced precision and increased model complexity—necessitating user-guided parameter selection for balanced outcomes.^[61]

Conformance Checking

Conformance checking in process mining involves comparing observed process executions, captured in event logs, against a predefined reference model to assess compliance and identify deviations. This technique quantifies how well the actual behavior aligns with the normative model, enabling the detection of discrepancies such as skipped activities, extra insertions, or replays that violate the model's structure. By simulating or mapping log traces onto the model, conformance checking provides diagnostic insights into process adherence, supporting quality assurance and regulatory compliance.^[62] One foundational approach is token-based replay, which simulates the execution of event log traces on a reference model, typically represented as a Petri net, by propagating tokens through transitions. During replay, counters track produced and consumed tokens to identify mismatches: missing tokens indicate unobserved model behavior, while remaining tokens highlight extra log activities not covered by the model. This method offers heuristics for handling incomplete fits, such as allowing leftover tokens in hidden places, and provides localized diagnostics for deviation points. Introduced in early conformance frameworks, token-based replay is computationally efficient for large logs but may underfit complex loops or concurrency.^[62] A more precise technique is alignments, which compute an optimal synchronous matching between a log trace and the reference model using edit-distance-like operations, such as synchronous moves (matching log and model events), log-only moves (insertions), and model-only moves (skips). These alignments employ cost-based search algorithms, often A* or genetic methods, to minimize deviation costs and generate a sequence of moves that explains the trace with the least alterations. Unlike token replay, alignments guarantee exact diagnostics by considering all possible paths, though they are more resource-intensive for noisy or long traces. This approach enhances root-cause analysis by highlighting specific deviation types and their frequencies. Key metrics for evaluating conformance include fitness, which measures the degree to which log traces can be replayed on the model, often expressed as a percentage of successfully explained behavior (e.g., 1.0 for perfect replayability). Precision assesses the model's behavioral appropriateness by quantifying how much unobserved behavior the model permits, penalizing overly permissive structures that allow extraneous traces. Structural appropriateness, sometimes called simplicity or generalization, evaluates adherence to token and game rules in Petri nets, ensuring the model avoids under- or over-generalization. These metrics are typically computed via replay or alignment results, balancing recall-like coverage (fitness) with specificity (precision).^[62]^[63] In auditing applications, conformance checking facilitates root-cause analysis of non-compliance by pinpointing deviations in financial or operational processes, such as unauthorized skips in approval workflows. For instance, auditors may apply a fitness threshold of 95% to certify process adherence, flagging cases below this for investigation, as demonstrated in internal control evaluations using real event logs from enterprise systems. These insights can inform process enhancements, such as targeted controls to reduce detected deviations.^[64]

Process Enhancement

Process enhancement in process mining refers to techniques that repair, extend, or predict aspects of process models by leveraging insights from event logs and external data sources.^[65] This approach builds on discovered or existing models to address deviations identified through conformance checking, aiming to create more accurate and actionable representations of business processes.^[66] Key subtypes include repair, which fixes discrepancies between the model and observed behavior in event logs, and extension, which augments the model with additional attributes such as performance metrics or resource details derived from the logs.^[67] In repair techniques, algorithms align the process model with the event log by inserting or removing activities to minimize mismatches, often prioritizing impactful changes to improve model fitness.^[68] For instance, impact-driven repair methods evaluate potential edits based on their effect on overall conformance, ensuring the revised model better reflects real-world executions without overcomplicating the structure.^[68] Extension, on the other hand, enriches models by projecting log data onto existing representations, such as adding timestamps to reveal waiting times or frequencies. Performance mining, a core method within enhancement, focuses on detecting bottlenecks by analyzing waiting times, service durations, and throughput in event logs.^[69] Techniques classify bottlenecks using heuristics like queueing thresholds or dotted charts to visualize delays, enabling targeted interventions to reduce cycle times.^[69] Predictive monitoring extends this by employing machine learning models, such as long short-term memory (LSTM) neural networks, to forecast next activities or remaining case durations from partial traces in event logs.^[70] These models achieve high accuracy in outcome prediction, supporting proactive process adjustments.^[70] Advanced enhancement includes organizational mining, which discovers roles and social networks from resource interactions in event logs, and decision mining, which extracts rules governing choice points in processes.^[71] Organizational mining constructs handover-of-work graphs to identify collaboration patterns, revealing informal structures that influence efficiency.^[72] Decision mining applies rule induction, such as decision trees, to infer conditions for routing decisions, like approval thresholds based on attributes in the log.^[67] Enhancement often integrates with simulation for what-if analysis, where enhanced models are simulated to evaluate hypothetical changes, such as resource reallocations, on process outcomes.^[73] Recent advancements in the 2020s incorporate AI hybrids, like reinforcement learning, to optimize process paths by treating event logs as environments for agent training, rewarding sequences that minimize costs or delays.^[74] These methods demonstrate improved optimization in dynamic settings, with reinforcement agents outperforming traditional heuristics in simulated scenarios.^[74]

Applications

In Business Process Management

Process mining is integral to Business Process Management (BPM), where it leverages event log data to discover, analyze, and enhance operational workflows, bridging the gap between designed processes and actual executions. In BPM, it supports end-to-end optimization by identifying deviations, bottlenecks, and inefficiencies, allowing organizations to align processes with strategic goals like cost reduction and compliance. This data-driven approach complements traditional BPM methods by providing empirical evidence for process redesign, often integrating with conformance checking to audit adherence to predefined rules.^[75] In supply chain management within BPM, process mining excels in cycle time analysis, enabling visibility into lead times and material flows to pinpoint delays such as supplier bottlenecks or transportation issues. By applying techniques like process performance analysis, organizations can quantify cycle times across the supply chain, facilitating targeted improvements in order fulfillment and inventory management. For instance, end-to-end network visibility use cases demonstrate how process mining reduces delivery times by highlighting temporal patterns in historical data, as explored in comprehensive reviews of SCM applications.^[76] In the financial sector, process mining enhances BPM compliance efforts, particularly in fraud detection within transaction logs and digital onboarding processes. It analyzes event sequences to identify anomalous patterns indicative of fraud, such as irregular verification steps, combining process discovery with machine learning classifiers like XGBoost to achieve up to 80% accuracy in distinguishing fraudulent from legitimate cases. A real-world application in a Brazilian fintech's onboarding logs, involving over 61,000 traces, showed that time-based features and trace embeddings effectively flag deviations, supporting regulatory adherence and risk mitigation in BPM frameworks.^[77] Case studies in manufacturing illustrate process mining's impact on BPM through order-to-cash (O2C) optimization. At 3M, process discovery techniques reduced O2C cycle times by 20% and improved payment term compliance from 65% to 92%, protecting millions of invoices from errors. Similarly, Siemens applied process mining to eliminate 10 million manual activities in O2C, yielding $15 million in annual cost savings and increasing automation by 24%. In service industries, such as call centers, process mining supports resource allocation by modeling workflow patterns and resource utilization from event logs, enabling dynamic staffing adjustments to balance workloads and reduce wait times, as demonstrated in systematic reviews of resource behavior in BPM executions.^[78]^[79] Integration with BPM suites amplifies process mining's value in end-to-end management. ARIS, for example, connects its process mining module to the core platform via OAuth and project room configurations, allowing seamless transfer of discovered variants into BPMN models for conformance analysis and simulation. SAP Signavio's Process Intelligence similarly embeds process mining within its BPM suite, enabling collaborative analysis of execution data alongside modeling tools to drive continuous improvement. These integrations facilitate ROI realization, with metrics like Siemens' $15 million savings from automation highlighting cost reductions of 10-20% in operational processes through targeted enhancements.^[80]^[81]^[78] Process mining aligns with BPM standards like BPMN 2.0, supporting import and export of models for consistent representation across tools. Platforms such as IBM Process Mining generate BPMN 2.0 diagrams from event logs, ensuring compatibility for process simulation and enhancement in BPM cycles. This standardization promotes interoperability, allowing discovered processes to inform normative models while maintaining traceability in BPM governance.^[82]

In Other Domains

Process mining has found significant applications in healthcare, where it analyzes event logs from electronic health record (EHR) systems to map and optimize patient pathways, identifying inefficiencies such as delays in treatment or resource allocation. For instance, in emergency departments (EDs), process mining techniques have been used to discover actual patient flows from EHR data, revealing bottlenecks like prolonged triage or waiting for diagnostics, which contribute to overcrowding and extended wait times. By comparing discovered process models with normative guidelines, conformance checking can quantify deviations, enabling targeted interventions that reduce average ED wait times in simulated scenarios based on real logs. A comparative study demonstrated that process mining outperforms traditional simulation in accurately modeling ED processes for overcrowding mitigation, providing actionable insights for staffing adjustments and workflow redesign.^[83] During the COVID-19 pandemic (2020-2022), process mining supported epidemic modeling by extracting care pathways from hospital logs to trace patient movements and contact patterns, aiding in infection control and resource forecasting. Case studies applied process discovery to EHR data from COVID-19 wards, uncovering variants in treatment sequences and compliance with isolation protocols, which informed predictive enhancements for surge capacity planning. One analysis of medical data during the outbreak used conformance checking to evaluate guideline adherence in intensive care units, highlighting delays in ventilator allocation that impacted outcomes. These applications extended to broader epidemic response, where process enhancement techniques integrated with machine learning predicted pathway deviations under high caseloads, improving hospital preparedness. In the public sector, process mining enhances e-government efficiency by analyzing logs from administrative systems to detect deviations in permit processing workflows, such as unnecessary approvals or delays in document handling. For example, discovery algorithms applied to public administration databases reveal non-conformant paths in licensing procedures, allowing for automation of redundant steps and reduction in processing times. A framework for public administration solutions uses process mining to monitor service delivery, identifying compliance issues in regulatory workflows and supporting data-driven policy adjustments. In education, process mining examines student enrollment workflows from learning management systems, mapping application reviews, registration, and advising sequences to pinpoint bottlenecks like manual verifications that delay onboarding. Conformance analysis on enrollment logs has helped institutions streamline processes, reducing administrative overhead and improving student satisfaction through targeted enhancements. Within IT and DevOps, process mining detects bottlenecks in software development pipelines, particularly continuous integration/continuous deployment (CI/CD) workflows, by mining logs from tools like Jenkins or GitLab to visualize deployment cycles and identify delays in testing or merging. Discovery techniques uncover hidden inefficiencies, such as prolonged build times due to resource contention, enabling optimizations that shorten release cycles in agile teams. In cybersecurity, process mining facilitates intrusion process discovery by analyzing system event logs to model attack sequences, distinguishing normal from anomalous behaviors through conformance checking against secure baselines. For instance, applying process mining to network intrusion detection systems has improved alert visualization and false positive reduction, enhancing threat response in industrial control environments. Emerging applications in 2025 leverage process mining for sustainability, particularly in supply chain management, where enhanced models integrate carbon footprint metrics from logistics logs to quantify emissions across procurement and delivery processes. For example, process mining drives digital transformation in enterprise logistics for circular and sustainable supply chain performance.^[84] Process enhancement techniques predict environmental impacts by augmenting event data with sustainability indicators, supporting green optimizations like route rerouting to minimize CO2 output. Additionally, process mining on blockchain transaction logs discovers decentralized workflows, such as in smart contracts, revealing compliance patterns and fraud risks in cryptocurrency platforms like Augur. Recent studies show process mining enhances transparency in blockchains, aiding in technical setup for better visibility.^[85] with deep learning extensions detecting suspicious sequences for regulatory auditing.

Tools and Implementation

Open-Source Software

Open-source software plays a crucial role in process mining by providing extensible platforms for researchers and practitioners to experiment with algorithms without licensing costs. These tools emphasize flexibility, community-driven development, and integration with broader data science ecosystems. ProM, first released in 2004, is a foundational open-source framework implemented in Java that supports a wide array of process mining techniques through its plugin architecture.^[86] It features over 1,500 plugins dedicated to process discovery, conformance checking, and enhancement, enabling advanced analyses such as control-flow extraction and performance monitoring from event logs.^[87] A lightweight variant, RapidProM, integrates ProM's capabilities with the RapidMiner environment to facilitate the creation of process mining workflows in a user-friendly, data analysis-oriented interface.^[88] PM4Py, an open-source Python library introduced in 2018, complements ProM by offering scripting-based access to state-of-the-art process mining algorithms, including support for importing and exporting XES event log formats.^[89] It enables custom analyses through programmatic interfaces, such as applying the heuristics miner to discover process models from data, and integrates seamlessly with Jupyter notebooks for reproducible workflows.^[90] As of 2025, PM4Py's version 2.7 includes extensions for machine learning, notably improved integration with large language models for enhanced process analysis tasks.^[91] The development and maintenance of these tools are bolstered by the IEEE Task Force on Process Mining, which promotes open-source contributions through standards, tutorials, and collaboration on techniques implemented in ProM and PM4Py.^[92] This community support ensures ongoing updates and accessibility for academic and exploratory use, in contrast to commercial solutions that prioritize enterprise scalability.

Commercial Solutions

Commercial process mining solutions are proprietary software platforms designed for enterprise-scale deployment, emphasizing user-friendly interfaces, integration with existing IT ecosystems, and advanced analytics to support industrial applications in process optimization. These tools typically operate as software-as-a-service (SaaS) offerings, enabling real-time monitoring and actionable insights from event logs across large organizations. Leading vendors provide dashboards tailored for executive decision-making, ensuring accessibility without requiring deep technical expertise.^[93] Celonis stands as a prominent vendor in the commercial process mining landscape, offering a SaaS platform focused on real-time process intelligence and automation. The company achieved a valuation of approximately $13 billion following its 2022 funding round, reflecting its market dominance driven by breakthroughs in AI-enhanced process mining. In 2023, Celonis acquired Symbioworld GmbH (Symbio), an AI-driven business process management provider, to bolster collaborative features and AI capabilities within its ecosystem. By 2025, Celonis introduced generative AI tools like the Process Copilot, allowing users to query process data via natural language for faster analysis and recommendations. Its platform supports petabyte-scale event logs, making it suitable for global enterprises in sectors like manufacturing and finance.^[94]^[95]^[96] UiPath Process Mining integrates seamlessly with robotic process automation (RPA), providing end-to-end visibility into workflows to identify automation opportunities and bottlenecks. This tool connects to diverse data sources, extracting event logs to map actual processes and simulate RPA impacts, thereby enhancing efficiency in operations like purchase-to-pay cycles. As part of UiPath's broader automation suite, it emphasizes scalability for high-volume tasks, with cloud-based deployment options for rapid implementation. In 2025, UiPath enhanced its offerings with AI-driven insights, aligning process mining with agentic automation trends.^[97]^[98]^[99] SAP Signavio, acquired by SAP in 2021, specializes in process mining with a strong emphasis on Business Process Model and Notation (BPMN) for modeling and conformance checking. The platform enables collaborative process discovery from IT systems, generating BPMN diagrams directly from event data to bridge as-is and to-be processes. It supports governance through standardized modeling and integration with SAP's ERP ecosystem, facilitating compliance in regulated industries. In March 2025, SAP Signavio launched an AI-assisted process modeler with text-to-process functionality, converting natural language descriptions into BPMN models to accelerate design phases. Cloud deployment ensures accessibility for distributed teams, with dashboards providing variant analysis for process variants.^[100]^[101]^[102] Software AG's ARIS Process Mining focuses on governed business process transformation, incorporating features like insight-to-action workflows that trigger governance actions based on mining results. It excels in root cause analysis and conformance checking, supporting large-scale simulations for process improvement while ensuring regulatory compliance through structured governance. The tool handles complex event logs from enterprise systems, offering customizable dashboards for stakeholders. ARIS was recognized for its AI-driven enhancements in process mining by 2025, including automated anomaly detection. Its on-premises and cloud options cater to organizations prioritizing data sovereignty and integration with legacy systems.^[103]^[104]^[105] Common features across these commercial solutions include cloud-based scalability for processing petabyte-scale logs, interactive executive dashboards for visualizing key performance indicators, and integrations with enterprise resource planning (ERP) and customer relationship management (CRM) systems. By 2025, generative AI integrations enabled natural language querying and predictive analytics, reducing the time from data ingestion to actionable insights. These platforms prioritize security and compliance, often with role-based access controls, distinguishing them from open-source alternatives by offering dedicated support and out-of-the-box scalability for non-technical users.^[106]^[107] In the market, commercial process mining vendors are evaluated in reports like the 2025 Gartner Magic Quadrant for Process Mining Platforms, where leaders such as Celonis, ARIS, and Pegasystems were positioned highest for vision completeness and execution ability. This recognition underscores their role in driving enterprise adoption, with the sector projected to grow due to increasing demand for AI-augmented process optimization. Recent acquisitions and mergers, including Celonis's expansions, have further consolidated the market around integrated intelligence platforms.^[93]^[108]^[109]

Challenges and Future Directions

Current Limitations

One major challenge in process mining stems from data quality issues in event logs, which are often incomplete or noisy, leading to inaccurate process models and analyses. Noisy event logs, characterized by outliers, inconsistencies, or erroneous entries, can distort the discovered processes, as algorithms may interpret noise as legitimate behavior, resulting in overly complex or unreliable models. For instance, missing attributes such as timestamps or resources are common, complicating the reconstruction of process sequences and timings, which hampers techniques like conformance checking and performance analysis.^[110]^[111]^[112] Privacy concerns further exacerbate data-related limitations, particularly when event logs contain sensitive personal information that must comply with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These laws mandate strict controls on data processing, storage, and sharing, yet process mining often requires access to detailed logs that include identifiers, potentially enabling re-identification of individuals and risking violations through pseudonymization failures or unauthorized disclosures. Balancing analytical depth with anonymization techniques remains difficult, as excessive data suppression can degrade log quality and utility.^[113]^[113] On the technical front, scalability poses significant hurdles for process mining with big data, as many discovery algorithms exhibit exponential computational complexity when handling large volumes of events or long traces. For example, state-based discovery methods, such as those relying on Petri nets or transition systems, can become intractable for logs with millions of events due to the explosion in state space exploration. While approximations like divide-and-conquer strategies mitigate this to some extent, they often sacrifice completeness or precision in real-world, high-volume scenarios. Additionally, the lack of standardization beyond the XES format limits interoperability, as XES's single-case perspective struggles with object-centric or multi-perspective data, complicating exchanges between tools and extensions for unstructured or relational logs.^[114]^[115]^[116]^[117] Methodologically, process discovery techniques frequently suffer from overfitting, where models achieve high precision on the training log but exhibit poor generalization to unseen behaviors or variations in the actual process. This occurs because algorithms like the Alpha miner or genetic approaches prioritize fitting every observed trace, leading to spaghetti-like models that capture noise rather than the underlying process structure. In resource perspectives, biases arise from incomplete log coverage or skewed resource assignments, such as underrepresented roles or uneven workload distributions, which can propagate unfair insights into organizational analyses.^[118]^[118]^[119] Organizationally, adoption barriers include substantial skill gaps among practitioners, who often lack interdisciplinary expertise in data science, domain knowledge, and process modeling required to interpret and apply mining results effectively. This expertise deficit, coupled with resistance to change and inadequate governance, slows integration into business workflows. Ethical issues, such as algorithmic bias in process enhancement, further complicate adoption; biases in logs or models can amplify discriminatory outcomes, for example, in resource allocation or performance predictions, raising concerns about fairness and accountability in decision-making.^[10]^[120]^[121]^[122]^[123]

Emerging Trends

Recent advancements in process mining are increasingly incorporating artificial intelligence and machine learning techniques to address limitations in traditional methods, particularly through hybrid models that enhance tasks like trace clustering and conformance checking. Deep learning approaches, such as Path Complex Neural Networks (PCNN), leverage topological representations of event logs to capture higher-order sequential dependencies, improving classification accuracy in complex process data on benchmark datasets compared to standard recurrent neural networks. These hybrid models integrate graph-based neural architectures with classical process discovery algorithms, enabling more nuanced trace clustering that identifies subprocess variants in noisy or incomplete logs. For instance, PCNN uses message-passing mechanisms on path-complex structures (e.g., 0-paths for events and 2-paths for tri-event sequences) to optimize inductive learning for process activity prediction.^[124] Explainable AI (XAI) is emerging as a critical component for conformance checking, providing interpretable insights into deviations between event logs and normative models while maintaining the black-box advantages of deep learning. Systematic reviews highlight that AI-driven conformance techniques, including transformers and optimization algorithms, can handle multi-perspective processes and uncertainty more efficiently than alignment-based methods, though adoption remains limited to experimental settings as of 2023-2025. XAI methods, such as pattern-based explanations for deviation clusters, allow process owners to understand root causes of nonconformance, fostering trust in automated recommendations. Research agendas propose further integration of machine learning for alternative modeling paradigms, addressing computational scalability in large-scale event data.^[125]^[126] In handling big data and cloud environments, distributed processing frameworks like Apache Spark facilitate scalable event log analysis, enabling parallel computation for discovery and enhancement tasks on massive datasets exceeding traditional in-memory limits. Spark's integration supports iterative algorithms for process model induction, reducing execution times in distributed clusters for logs with millions of traces, as demonstrated in port operation monitoring systems. Complementing this, real-time streaming mining via Apache Kafka addresses dynamic event generation, treating Kafka topics as infinite event streams for continuous process discovery. Proposed architectures use standardized formats like JXES or OCEL JSON for serialization, supporting both offline and online mining with topic strategies (e.g., case-ID partitioning) to maintain low-latency extraction without data replication. This enables proactive conformance in environments with high-velocity data, such as supply chains.^[127]^[128] New frontiers in process mining extend to Internet of Things (IoT) applications, where sensor data streams are abstracted into discrete events for end-to-end process analysis in domains like manufacturing and smart homes. A review of 36 studies from 2014 to 2022 identifies common pipelines involving preprocessing, activity recognition, and event log generation from sensors (e.g., motion and temperature types), revealing use cases in process monitoring and anomaly detection but gaps in handling continuous streams and underrepresented sensor modalities like chemical detectors. These approaches transform raw IoT data into XES-compliant logs, enabling discovery of hidden workflows from physical interactions. Sustainability analytics represents another frontier, with analysis patterns bridging process mining meta-models and Life Cycle Assessment (LCA) to quantify environmental and social impacts. Patterns such as sustainability-relevant inputs/outputs and impact measurement integrate with mining tools to enrich event logs with resource metrics (e.g., energy consumption), supporting greener process redesign; evaluations show most existing tools lack such capabilities, proposing extensions like object-centric enrichment for comprehensive audits.^[129]^[130] Research directions emphasize privacy-preserving techniques, including federated learning frameworks that allow collaborative process mining across organizations without sharing raw event logs. These approaches train shared models on decentralized data, mitigating privacy risks in cross-silo scenarios while achieving comparable accuracy to centralized methods for prediction tasks. Additionally, integration with agent-based modeling and simulation (ABMS) is gaining traction for virtual process experimentation, combining event log insights with socio-technical simulations to forecast outcomes in dynamic systems; a literature review screening an initial pool of 189 papers indicates a growing trend but calls for standardized hybrid pipelines. Post-2023 developments, including metaverse-like simulations, are underexplored but hold promise for immersive process visualization and what-if analysis.^[131]^[132]

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May 22, 2025 · This paper addresses this research gap through a systematic literature review of conformance checking approaches contrasted against trends in artificial ...
[122]
Explainable conformance checking: : Understanding patterns of ...
This paper aims to provide business process owners with human-interpretable explanations for patterns of deviations found between process models and event data.
[123]
Scalable and distributed architecture based on Apache Spark ...
Mar 28, 2019 · In this work, we design a scalable and distributed architecture for real time monitoring of operational business processes of a RoRo port ...
[124]
[PDF] Towards Process Mining on Kafka Event Streams - CEUR-WS
Abstract. Process mining is a family of techniques to analyze business processes based on the event logs produced during their execution.
[125]
Process mining on sensor data: a review of related works
Mar 3, 2025 · In this paper, a comprehensive literature study is conducted to understand the field of process mining for sensor data.
[126]
Sustainability Analysis Patterns for Process Mining and Process ...
Mar 28, 2025 · This paper describes a set of sustainability analysis patterns that integrate BPM concepts with concepts from existing sustainability analysis ...
[127]
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[128]
Towards integrating process mining with agent-based modeling and ...
Jul 1, 2025 · This study explores the role of process mining and its impact on the ABMS paradigm, identifying the current state of the art, gaps in the literature, and ...