Due to increasing use of technology-enhanced educational assessment, data mining methods have been explored to analyse process data in log files from such assessment. However, most studies were limited to one data mining technique under one specific scenario. The current study demonstrates the usage of four frequently used supervised techniques, including Classification and Regression Trees (CART), gradient boosting, random forest, support vector machine (SVM), and two unsupervised methods, Self-organizing Map (SOM) and k-means, fitted to one assessment data. The USA sample (N = 426) from the 2012 Program for International Student Assessment (PISA) responding to problem-solving items is extracted to demonstrate the methods. After concrete feature generation and feature selection, classifier development procedures are implemented using the illustrated techniques. Results show satisfactory classification accuracy for all the techniques. Suggestions for the selection of classifiers are presented based on the research questions, the interpretability and the simplicity of the classifiers. Interpretations for the results from both supervised and unsupervised learning methods are provided.

Nearly two thirds of surveyed top managers of large companies operating in Russia viewed process mining as useful for purchasing, in 2021. Furthermore, over ** percent of respondents saw the technology's potential in improving the customer journey map and IT processes.

This chapter presents theoretical and practical aspects associated to the implementation of a combined model-based/data-driven approach for failure prognostics based on particle filtering algorithms, in which the current esti- mate of the state PDF is used to determine the operating condition of the system and predict the progression of a fault indicator, given a dynamic state model and a set of process measurements. In this approach, the task of es- timating the current value of the fault indicator, as well as other important changing parameters in the environment, involves two basic steps: the predic- tion step, based on the process model, and an update step, which incorporates the new measurement into the a priori state estimate. This framework allows to estimate of the probability of failure at future time instants (RUL PDF) in real-time, providing information about time-to- failure (TTF) expectations, statistical confidence intervals, long-term predic- tions; using for this purpose empirical knowledge about critical conditions for the system (also referred to as the hazard zones). This information is of paramount significance for the improvement of the system reliability and cost-effective operation of critical assets, as it has been shown in a case study where feedback correction strategies (based on uncertainty measures) have been implemented to lengthen the RUL of a rotorcraft transmission system with propagating fatigue cracks on a critical component. Although the feed- back loop is implemented using simple linear relationships, it is helpful to provide a quick insight into the manner that the system reacts to changes on its input signals, in terms of its predicted RUL. The method is able to manage non-Gaussian pdf’s since it includes concepts such as nonlinear state estimation and confidence intervals in its formulation. Real data from a fault seeded test showed that the proposed framework was able to anticipate modifications on the system input to lengthen its RUL. Results of this test indicate that the method was able to successfully suggest the correction that the system required. In this sense, future work will be focused on the development and testing of similar strategies using different input-output uncertainty metrics.

This data set belongs to the paper "Video-to-Model: Unsupervised Trace Extraction from Videos for Process Discovery and Conformance Checking in Manual Assembly", submitted on March 24, 2020, to the 18th International Conference on Business Process Management (BPM).Abstract: Manual activities are often hidden deep down in discrete manufacturing processes. For the elicitation and optimization of process behavior, complete information about the execution of Manual activities are required. Thus, an approach is presented on how execution level information can be extracted from videos in manual assembly. The goal is the generation of a log that can be used in state-of-the-art process mining tools. The test bed for the system was lightweight and scalable consisting of an assembly workstation equipped with a single RGB camera recording only the hand movements of the worker from top. A neural network based real-time object classifier was trained to detect the worker’s hands. The hand detector delivers the input for an algorithm, which generates trajectories reflecting the movement paths of the hands. Those trajectories are automatically assigned to work steps using the position of material boxes on the assembly shelf as reference points and hierarchical clustering of similar behaviors with dynamic time warping. The system has been evaluated in a task-based study with ten participants in a laboratory, but under realistic conditions. The generated logs have been loaded into the process mining toolkit ProM to discover the underlying process model and to detect deviations from both, instructions and ground truth, using conformance checking. The results show that process mining delivers insights about the assembly process and the system’s precision.The data set contains the generated and the annotated logs based on the video material gathered during the user study. In addition, the petri nets from the process discovery and conformance checking conducted with ProM (http://www.promtools.org) and the reference nets modeled with Yasper (http://www.yasper.org/) are provided.

Global process mining software market is expected to revenue of around USD 41.74 billion by 2032, growing at a CAGR of around 42.86% between 2024-2032.

Background

Any set of related activities that are executed in a repeatable manner and with a defined goal can be seen as process.

Process analytic approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support managerial-decision making across the organization.

For organisations interested in continuous improvement, such datasets allow data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of ‘current deployed process’ (whose execution trace is available), is critical to knowing whether it is worth investing in improvements, where performance problems exist, and how much variation there is in the process across the instances and what are the root-causes.

What is Process Mining (PM) ?

→ process of extracting valuable information from event logs/databases that are generated by processes.

Two topics are important i) process discovery where a process model describing the control flow is inferred from the data and ii) of conformance checking which deals with verifying that the behavior in the event log adheres to a set of business rules, e.g., defined as a process model. Rhese two use cases focus on the control-flow perspective,

Why Process Mining ?

→ identifying hidden nodes and bottlenecks in business processes.

About the Dataset

A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes.

Noise is added by randomly adding one additional event to an increasing number of traces. CPN Tools (http://cpntools.org) was used to generate the event log and inject the noise. The amount of noise can be controlled with the constant 'noise'.

Smaller dataset:

The files test0 to test5 represent process traces and maybe used for debugging and sanity check purposes

Process Mining Market Outlook

According to our latest research, the global process mining market size reached USD 1.69 billion in 2024, reflecting a robust expansion driven by the increasing demand for operational transparency and data-driven decision-making across industries. The market is expected to grow at a CAGR of 37.2% from 2025 to 2033, with the forecasted market size projected to reach USD 24.6 billion by 2033. This exceptional growth is primarily attributed to the rising adoption of digital transformation initiatives, the integration of artificial intelligence in business processes, and the need for enhanced compliance and risk management solutions.

One of the primary growth factors driving the process mining market is the mounting emphasis on business process optimization and efficiency enhancement. Organizations across sectors are under constant pressure to streamline their operations, reduce costs, and improve service delivery. Process mining technologies empower businesses to visualize, analyze, and optimize their workflows by extracting valuable insights from event logs generated by IT systems. This capability enables enterprises to identify process bottlenecks, inefficiencies, and deviations from standard operating procedures, thereby facilitating continuous process improvement. The integration of process mining with advanced analytics and machine learning further amplifies its value proposition, enabling predictive insights and automated recommendations for process enhancement.

Another significant driver is the escalating regulatory and compliance requirements faced by industries such as BFSI, healthcare, and manufacturing. As global regulations become increasingly stringent, organizations are compelled to ensure transparency, traceability, and accountability in their business processes. Process mining solutions provide a comprehensive audit trail of all business activities, enabling organizations to demonstrate compliance with regulatory standards and swiftly respond to audits and investigations. The ability to detect anomalies, monitor process adherence, and mitigate risks in real-time has positioned process mining as an indispensable tool for compliance and risk management, further fueling its adoption across compliance-sensitive sectors.

The proliferation of digital transformation initiatives and the widespread adoption of cloud computing are also catalyzing the growth of the process mining market. As organizations migrate their operations to the cloud and embrace digital technologies, the volume and complexity of process data have surged exponentially. Process mining platforms, particularly those offered as cloud-based solutions, are uniquely positioned to harness this data deluge, providing scalable, flexible, and cost-effective tools for process discovery and optimization. The synergy between process mining and other digital technologies, such as robotic process automation (RPA) and artificial intelligence, is unlocking new avenues for automation, agility, and innovation in business process management.

From a regional perspective, Europe currently leads the global process mining market, followed closely by North America and Asia Pacific. The strong presence of process mining vendors, a mature digital infrastructure, and a high level of regulatory compliance are key factors contributing to Europe's dominance. North America is witnessing rapid adoption, driven by the digital transformation initiatives of large enterprises and the growing need for operational excellence. Meanwhile, Asia Pacific is emerging as a high-growth region, propelled by the digitalization of industries, increasing investments in IT infrastructure, and the burgeoning demand for process optimization in emerging economies such as China and India.

Component Analysis

The process mining market by component is primarily segmented into software and services. The software segment dominates the market, accounting for the largest share in 2024, as organizations increasingly deploy process min

The number of publications of ^ per year. The percentile is given for the sake of comparison with the literature.

These are the data sets utilized to write the article: Monitoring Applications with Process Mining

Abstract The objective of this work is to improve the quality of the information that belongs to the database CubaCiencia, of the Institute of Scientific and Technological Information. This database has bibliographic information referring to four segments of science and is the main database of the Library Management System. The applied methodology was based on the Decision Trees, the Correlation Matrix, the 3D Scatter Plot, etc., which are techniques used by data mining, for the study of large volumes of information. The results achieved not only made it possible to improve the information in the database, but also provided truly useful patterns in the solution of the proposed objectives.

This realistic incident management event log simulates a common IT service process and includes key inefficiencies found in real-world operations. You'll uncover SLA violations, multiple reassignments, bottlenecks, and conformance issues—making it an ideal dataset for hands-on process mining, root cause analysis, and performance optimization exercises.

You can find more event logs + use case handbooks to guide your analysis here: https://processminingdata.com/

Standard Process Flow: Ticket Created -> Ticket Assigned to Level 1 Support -> WIP - Level 1 Support -> Level 1 Escalates to Level 2 Support -> WIP - Level 2 Support -> Ticket Solved by Level 2 Support -> Customer Feedback Received -> Ticket Closed

Total Number of Incident Tickets: 31,000+

Process Variants: 13

Number of Events: 242,000+

Year: 2023

File Format: CSV

File Size: 65MB

This dataset contains information about a credit requirement process in a bank. It contains data about events, time execution etc.

The process mining solutions market is experiencing robust growth, driven by the increasing need for enhanced operational efficiency and improved business process compliance across various sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $9 billion by 2033. This expansion is fueled by several key factors. The rising adoption of digital transformation initiatives across industries like manufacturing, financial services, and healthcare is a primary driver, with organizations seeking data-driven insights to optimize their processes. The increasing complexity of regulatory compliance necessitates robust process monitoring tools, further boosting market demand. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of process mining solutions, enabling more accurate analysis and predictive capabilities. The market is segmented by application (manufacturing, financial services, healthcare, retail, logistics and supply chain management) and by type (Automated Process Discovery Tools, Process Efficiency Analytics Software, Business Process Compliance Monitoring Tools), providing varied opportunities for vendors. North America currently holds a significant market share, owing to early adoption and a strong technological infrastructure, but the Asia-Pacific region is expected to exhibit the fastest growth rate in the forecast period. Competition among established players like Celonis, UiPath, and SAP Signavio, and emerging innovative startups is intensifying, fostering innovation and driving down costs. The constraints to market growth primarily involve the high initial investment costs associated with implementing process mining solutions, the need for specialized skills and expertise in data analysis and interpretation, and concerns surrounding data security and privacy. However, these challenges are being addressed through the development of more user-friendly software, cloud-based deployment options, and enhanced data security protocols. The continuous evolution of process mining technologies, incorporating advanced analytics and AI capabilities, will likely mitigate these limitations and propel market growth further. The integration of process mining with other business intelligence tools is also gaining traction, creating a more holistic approach to process optimization. This convergence of technologies is further shaping the future of the process mining solutions market and broadening its applicability across various industries.

The Process Mining Platform market is booming, projected to reach [estimated 2033 market size in millions] by 2033, driven by digital transformation and the need for operational efficiency. Discover key market trends, leading vendors (IBM, UiPath, Celonis, etc.), and growth opportunities in this comprehensive analysis.

The Supplementary Material of the paper "Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining" includes: 1) APPENDIX 1: SQL Statements for data extraction. Appendix 2: Interview for operating Staff. 2) The DataSet of the normalized data to define the predictive model.

The global process mining solutions market is expanding rapidly, with a market size valued at XXX million in 2025 and projected to grow at a CAGR of XX% during the forecast period of 2025-2033. Key drivers of this growth include increasing adoption of digital transformation initiatives, rising demand for operational efficiency, and growing need for regulatory compliance. Major market trends include the emergence of cloud-based solutions, the integration of artificial intelligence (AI) and machine learning (ML), and the adoption of process mining in new industries, such as healthcare and retail. The market is segmented into various application areas, including manufacturing, financial services, healthcare, retail, and logistics and supply chain management. Automated process discovery tools, process efficiency analytics software, and business process compliance monitoring tools are प्रमुख solution types driving the market. Top companies in the process mining domain include Celonis, SAP Signavio, IBM, ARIS, and Appian, among others. North America, Europe, Asia Pacific, and the Middle East & Africa are key regional markets for process mining solutions.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

Companies in this industry develop software for data mining. Data mining is the process of extracting patterns from large data sets.

Process Mining Software Market size was valued at USD 1.62 Billion in 2024 and is projected to reach USD 2.18 Billion by 2032, growing at a CAGR of 42.27% from 2026 to 2032.Increasing Adoption of Digital Transformation: The global push for digital transformation is a primary catalyst for the Process Mining Software Market. As organizations migrate from legacy systems to a more digitized infrastructure, they generate vast amounts of event data from every operational touchpoint. Process mining software provides the essential capability to analyze this data, offering an objective as is view of business processes. This transparency is crucial for any successful digital transformation initiative, as it allows organizations to first understand their current state before they can effectively redesign, automate, or optimize workflows.

Process Mining AI Market Outlook

As per our latest research, the global Process Mining AI market size in 2024 stands at USD 1.62 billion, reflecting robust demand across diverse industries. The market is projected to expand at a remarkable CAGR of 38.7% from 2025 to 2033, reaching a forecasted size of USD 20.76 billion by 2033. This exceptional growth is primarily fueled by the accelerating adoption of artificial intelligence and automation technologies for business process optimization, compliance, and risk management across enterprise landscapes worldwide.

A key growth driver for the Process Mining AI market is the increasing complexity of organizational processes and the subsequent need for greater transparency and operational efficiency. Modern enterprises are inundated with massive volumes of event logs and process data, making manual analysis both impractical and error-prone. Leveraging AI-powered process mining solutions allows companies to automatically discover, monitor, and improve real business processes by extracting actionable insights from their digital footprints. This not only enhances process visibility but also enables data-driven decision-making, reduces operational costs, and accelerates digital transformation initiatives, which is especially critical as organizations strive to remain agile and competitive in rapidly evolving markets.

Another significant factor propelling the growth of the Process Mining AI market is the rising emphasis on regulatory compliance and risk mitigation. With tightening global regulations such as GDPR, HIPAA, and SOX, organizations must ensure that their business processes adhere to stringent compliance standards. Process mining AI solutions facilitate continuous auditing and real-time monitoring of process deviations, ensuring that compliance requirements are met and risks are proactively managed. This capability is particularly vital in highly regulated sectors such as BFSI, healthcare, and government, where non-compliance can result in substantial financial penalties and reputational damage.

Furthermore, the proliferation of digital transformation initiatives across industries is accelerating the adoption of Process Mining AI. As enterprises increasingly migrate to cloud-based systems and integrate advanced technologies like IoT, RPA, and big data analytics, there is a growing need for intelligent solutions that can seamlessly analyze and optimize complex, end-to-end business processes. Process mining AI not only bridges the gap between process modeling and real-world execution but also empowers organizations to identify bottlenecks, eliminate inefficiencies, and enhance customer experiences. The convergence of AI with process mining is thus emerging as a strategic enabler for sustained business growth and innovation.

From a regional standpoint, North America currently dominates the Process Mining AI market, driven by the presence of leading technology vendors, high digital adoption rates, and significant investments in AI research and development. Europe follows closely, owing to strong regulatory frameworks and a mature digital ecosystem. Meanwhile, the Asia Pacific region is witnessing the fastest growth, attributed to rapid industrialization, expanding IT infrastructure, and increasing awareness of process optimization benefits among enterprises. These regional dynamics collectively underscore the global momentum behind the adoption of process mining AI solutions.

Task Mining with AI is emerging as a complementary technology to Process Mining AI, offering deeper insights into the granular aspects of business operations. While Process Mining AI focuses on end-to-end process analysis, Task Mining delves into the individual tasks performed by employees, capturing detailed data on how specific activities are executed. By leveraging AI, Task Mining can automatically record and analyze user interactions with various software applications, providing a comprehensive view of task execution patterns. This granular level of analysis helps organizations identify inefficiencies, optimize task sequences, and enhance employee productivity. As businesses strive to achieve holistic process optimization, the integration of Task Mining with AI into their digital transformation strategies is becoming increasingly vital.