MMPM Workshop
Process mining has emerged as a critical area in business process management, enabling organizations to discover, monitor, and improve real processes by extracting knowledge from event logs readily available in today's information systems. Traditional process mining techniques primarily rely on structured data from information systems. However, with the advent of advanced data collection technologies, there is an increasing availability of multimodal data sources such as videos, images, audio recordings, and textual documents that can provide rich insights into processes, especially manual and unstructured ones.
The 1st International Workshop on Multimodal Process Mining (MMPM) aims to bring together researchers and practitioners to explore the integration of multimodal data in process mining. This workshop will focus on the challenges, methodologies, and applications of incorporating diverse data types, such as video, audio, and textual data, into process mining techniques to enhance process understanding and analysis. Through presentations, discussions, and collaborative sessions, MMPM seeks to advance the field of multimodal process mining and foster collaborations among participants.
When? 16th June 2025 (CAiSE'25)
Where? Vienna, Austria
Type? Presentation-oriented and Discussion-oriented
Main Goals of the Workshop
- To bring together researchers and practitioners interested in the application of process mining techniques to multimodal data.
- To discuss the challenges and opportunities in process discovery and conformance checking with multimodal event logs.
- To explore novel approaches for visualizing multimodal evidence in process models.
- To foster collaboration on multimodal AI methods for process mining.
- To engage participants in human-in-the-loop methodologies for enhancing multimodal process mining.
Workshop Topics
The workshop invites, but is not limited to, the following topics:
- Foundations and Methodologies in Multimodal Process Mining
- Process Discovery from Multimodal Data
- Conformance Checking in Multimodal Process Mining
- Multimodal Event Logs
- Integration of Multimodal Data Sources in Process Mining
- Challenges in Data Preprocessing for Multimodal Process Mining
- Deep Learning Approaches for Multimodal Process Mining
- Benchmarking and Evaluation of Multimodal Process Mining Techniques
- User Interaction, Visualization, and Human-Centric Aspects
- Visualizing Multimodal Evidence in Process Models
- Human-in-the-loop for Multimodal Process Mining
- User Interaction and Experience in Multimodal Process Mining Tools
- Applications, Analytics, and Security
- Multimodal AI for Process Mining
- Predictive Analytics in Multimodal Process Mining
- Applications of Multimodal Process Mining in Industry
- Real-time Multimodal Process Monitoring
- Privacy and Security in Multimodal Process Mining
- Case Studies of Multimodal Process Mining Implementations
Submission
- The papers have to be submitted via EasyChair (https://easychair.org/conferences/?conf=caise2025), choosing the present workshop as the track to which you submit the paper.
- Full papers must conform to the Springer LNCS/LNBIP format and should not exceed 12 pages. Please refer to the Springer's authors' guidelines.
- The proceedings of the conference workshops will be published as one volume in the Springer LNBIP series. Short papers will be placed in a designated section.
- Paper Submission: March 7th, 2025
- Acceptance Notification: April 7th, 2025
- Camera-Ready Submission: April 14th, 2025
- The Workshop Day: June 16th, 2025
Type of Contributions
Full papers
(up to 12 pages)Illustrating novel research methodologies or case studies performed in industry.
Implementation papers
(up to 6 pages)Illustrating a tool or implementation of MMPM.
Visionary papers
(up to 4 pages)Discussing the future steps of MMPM.
Important Dates
Paper Submission
Acceptance Notification
Camera-Ready Submission
The Workshop Day
Dates are Anywhere on Earth (AoE)
Organizers
Aleksandar Gavric is an University Assistant and a researcher at TU Wien working in the area of process mining and data analysis. His research involves integrating various data modalities, such as images, video, audio, and sensor data, with conceptual models. His work on multimodal process mining and conceptual modeling has shown promising results and employs techniques from computer vision and natural language processing to create semantic connections within these models.
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Maxim Vidgof is an Assistant Professor at Vienna University of Economics and Business, specializing in process mining and process automation. His doctoral research was centered on the complexity of business processes, aiming to develop new methodologies for understanding and managing this complexity. His current research interests include examining process complexity and the application of Large Language Models in Business Process Management.
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