AIPM

We are pleased to announce the 1st workshop on Artificial Intelligence in Process Mining (AIPM), a forum for researchers, practitioners, and industry experts to converge and explore the transformative
potential of artificial intelligence in the realm of process mining.

Process mining is a powerful analytical discipline that leverages event data to reconstruct, visualize, and analyze actual processes within an organization. This is performed through the extraction and analysis of event logs, capturing the digital traces left by various activities such as transactions, interactions, or system events. Through a meticulous process of discovery, conformance checking, and enhancement, process mining unveils the true contours of business processes, highlighting bottlenecks, deviations, and inefficiencies that may go unnoticed in traditional approaches. As a dynamic discipline, process mining adapts to the evolving nature of business processes. It goes beyond static models by embracing real-time insights, allowing organizations to not only understand historical performance but also anticipate future behaviors. This predictive capability opens the door to agile decision-making and continuous improvement, aligning operations with strategic goals.

By fostering a data-driven culture, process mining empowers stakeholders to make informed decisions, streamline operations, and embark on a journey of perpetual optimization. As technology advances and data becomes more abundant, the role of process mining in shaping the future of business intelligence continues to evolve, promising a more transparent, adaptive, and efficient organizational landscape. However, the significance and capacity of process mining fully unfolds when it intersects with artificial intelligence, marking a paradigm shift in how business operations are unravelled, understood, and enhanced.

Artificial intelligence injects a dose of intelligence into process mining by enabling advanced analytics, machine learning, and predictive modeling. Through AI-enhanced process mining, organizations can delve deeper into their data, extracting meaningful insights that might have otherwise remained hidden. Machine learning algorithms can identify patterns, anomalies, and trends within processes, providing a deep understanding of the intricacies that shape operational landscapes. AI-driven process mining can forecast potential bottlenecks, optimize resource allocation, and predict future process behaviors. This foresight empowers decision-makers to proactively address challenges, allocate resources efficiently, and stay one step ahead in the ever-evolving business environment.

As organizations strive for operational excellence and data-driven decision-making, the synergy between artificial intelligence (AI) and process mining becomes increasingly vital. WAIPM 2023 aims to bridge the gap between these two dynamic fields, offering a platform for sharing cutting-edge research, practical insights, and innovative solutions.

We invite contributions in the form of research papers, case studies, and demonstrations across the
following topics of interest (but not limited to):

1. AI-Enhanced Process Discovery

• Innovative approaches and algorithms combining AI techniques with process discovery methods.
• Adaptive and real-time process discovery using machine learning.
• Uncertainty Quantification

2. Evolutionary Algorithms in Process Mining:

• Applications of evolutionary algorithms for process discovery, conformance checking, and
  model optimization.
• Dynamic and online evolution of process models in response to changing business
  environments.

3. Predictive Analytics and Machine Learning in Process Optimization:

• Predictive modeling for business process optimization.
• Machine learning applications in forecasting, resource allocation, and performance analysis.

4. AI-Based Conformance Checking:

• Techniques and tools leveraging AI for automated conformance checking and anomaly
  detection.
• Real-time monitoring and adaptive conformance checking strategies.

5. AI for Process Automation:

• Integration of AI technologies with process automation for enhanced efficiency.
• Case studies on successful implementations of AI-driven process automation.

6. Explainable AI in Process Mining:

• Approaches to making AI-driven insights from process mining transparent and interpretable.
• Ensuring accountability and compliance through explainable AI in process

Workshop Chairs:

• Andreas S. Andreou, Cyprus University of Technology
• Massimo Mecella, Sapienza University of Rome, Italy
• Harris Papadopoulos, Frederick University, Cyprus

Program Committee:

• Marco Comuzzi, Ulsan National Institute of Science and Technology, South Korea
• Khuong An Nguyen (Royal Holloway, University of London, UK)
  Spyros Likothanassis, University of Patras, Greece
  Andrea Marella, Sapienza University of Rome, Italy
  Efstratios Georgopoulos, University of Peloponnese, Greece
  Willem-Jan van den Heuvel, Tilburg University, Netherlands
  Panayiotis Christodoulou, University of Nicosia, Cyprus
  Andreas Christoforou, Logisoft, Cyprus
  Michalis Pingos, Cyprus University of Technology
  Spyros Loizou, Paradisiotis Group, Cyprus
  Andreas Constantinides (Frederick University, Cyprus)
  Charalambos Eliades (Frederick University, Cyprus)
  Themis Christodoulou (Frederick University, Cyprus)

Important dates