Process Mining: Theory and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 March 2026 | Viewed by 1899
Special Issue Editors
Interests: process mining and automation; semantic web technologies; learning analytics and systems design; data science; artificial intelligence; machine learning; text mining; computer and education; educational innovation; educational technologies; knowledge engineering and data management; internet applications and ontology
Interests: e-learning personalization; artificial intelligence in education; digital accessibility; human-computer interaction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Process mining is a rapidly evolving discipline that bridges the gap between data science and business process management. It enables organizations to gain actionable insights by analyzing event logs to model, monitor, and optimize business processes. Process mining has established itself as a critical link between data-driven insights and process improvement, with significant contributions from several studies in different contexts emphasizing its role in business process management and system optimization. Integrating AI methods and techniques into process mining, such as reinforcement learning for adaptive process optimization, has also been identified as a transformative avenue for research and practice, including manufacturing for predictive maintenance and educational process mining and innovation.
This Special Issue aims to serve as a comprehensive platform to disseminate groundbreaking research and practical insights in process mining. This issue invites contributions that focus on both “theoretical and practical applications” of the process mining technique to address challenges such as scalability, integration with AI, and enhancing predictive capabilities. It emphasizes real-world applications in manufacturing, education, healthcare, and finance by highlighting process mining’s transformative potential in improving operational efficiency and decision-making.
Potential topics include, but are not limited to, the following:
- Process Mining;
- Event Logs;
- Process Discovery;
- Conformance Checking;
- Model Enhancement;
- Business Process Management;
- Data Science;
- Predictive Analytics;
- Process Optimization;
- Artificial Intelligence Integration;
- Scalability in Process Mining;
- Real-World Applications of Process Mining;
- Healthcare Process Mining;
- Manufacturing Analytics;
- Educational Process Mining;
- Finance Process Insights;
- Decision-making Process;
- Business Workflow.
Prof. Dr. Kingsley Okoye
Dr. Julius Nganji
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- process mining
- event logs
- process discovery
- conformance checking
- model enhancement
- business process management
- data science
- predictive analytics
- process optimization
- artificial intelligence integration
- scalability in process mining
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