Overview
EKAPI 2024 is the first International Workshop on Explainable Knowledge Aware Process Intelligence aimed at contributing to the advancement of explainable, knowledge-aware process intelligence and expanding the vibrant community dedicated to addressing challenges in the process mining landscape.
Due to the ongoing digitalization undergone by organizations in recent years, there has been a notable surge in interest in Process Intelligence research. Process mining, coupled with AI techniques, plays a pivotal role in this domain, leading the charge in transforming event data into valuable knowledge for companies. Despite the effectiveness of existing techniques, two primary issues persist: the lack of documentation and traceability stemming from ad-hoc procedures, and the use of opaque black-box components. Thus, many intriguing problems in this research area, towards explainable knowledge-aware process intelligence, are left open.
The workshop location is the Club Hotel Kennedy in Roccella Ionica, Reggio Calabria, situated on the marvelous Jasmine Coast, and will be held on June 20-22, 2024.
Important Dates
Full Paper Submission Deadline: May 11, 2024 May 17, 2024
Notification of Acceptance: June 2, 2024
Pre-Registration Deadline: June 5, 2024
Workshop Date: June 20-22, 2024
Topics of Interest
Potential submission topics encompass a range of areas, including but not limited to:
Multi-perspective process models incorporating data, time, and resources.
Declarative processes.
Explainable and trustworthy AI for both process management and process mining.
Conversational systems, natural language processing, and human-machine interaction in the context of process management.
Knowledge representation for process management, covering reasoning about actions and processes, planning, and synthesis.
Answer Set Programming for Process mining.
AI techniques for various aspects of process handling, such as discovery, conformance checking, prescriptive and predictive monitoring.
AI techniques for clustering and classification of process execution traces.
Generative AI applied to Process Mining.
Machine learning for event recognition in semi-structured and unstructured data.
Mining techniques, including association rule mining, specification mining, and decision mining from process execution traces.
Declarative-based multi-perspective representation of process traces.
Introduction of novel metrics for measuring process conformance.
Addressing uncertainty in AI for process management.
Exploration of multiagent systems, strategic reasoning, game theory, and mechanism design for multi-party processes.
Consideration of multi-objective optimization, decision-making processes, and continuous improvement.
Examining the concept of value alignment in the context of process management.
Knowledge Aware Conceptual Blending Generative Knowledge Combination