95 citations · 188 across the 16 of their papers we have counts for
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ProMoAI: Process Modeling with Generative AI
Humam Kourani, Alessandro Berti, Daniel Schuster +1
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, e…
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review
Alessandro Berti, Marco Montali, Wil M. P. van der Aalst
Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from p…
Leveraging Large Language Models (LLMs) for Process Mining (Technical Report)
Alessandro Berti, Mahnaz Sadat Qafari
This technical report describes the intersection of process mining and large language models (LLMs), specifically focusing on the abstraction of traditional and object-centric proc…
Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study
Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst
Large Language Models (LLMs) are capable of answering questions in natural language for various purposes. With recent advancements (such as GPT-4), LLMs perform at a level comparab…
Applying Process Mining on Scientific Workflows: a Case Study on High Performance Computing Data
Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro +1
Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workfl…
A Collection of Simulated Event Logs for Fairness Assessment in Process Mining
Timo Pohl, Alessandro Berti, Mahnaz Sadat Qafari +1
The analysis of fairness in process mining is a significant aspect of data-driven decision-making, yet the advancement in this field is constrained due to the scarcity of event dat…
Interactive Process Identification and Selection from SAP ERP
Julian Weber, Alessandro Berti, Gyunam Park +2
SAP ERP is one of the most popular information systems supporting various organizational processes, e.g., O2C and P2P. However, the amount of processes and data contained in SAP ER…
OC-PM: Analyzing Object-Centric Event Logs and Process Models
Alessandro Berti, Wil van der Aalst
Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being fo…
Filtering and Sampling Object-Centric Event Logs
Alessandro Berti
The scalability of process mining techniques is one of the main challenges to tackling the massive amount of event data produced every day in enterprise information systems. To thi…
PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining
Alessandro Berti, Minh Phan Nghia, Wil M. P. van der Aalst
Open-source process mining provides many algorithms for the analysis of event data which could be used to analyze mainstream processes (e.g., O2C, P2P, CRM). However, compared to c…
A Scalable Database for the Storage of Object-Centric Event Logs
Alessandro Berti, Anahita Farhang Ghahfarokhi, Gyunam Park +1
Object-centric process mining provides a set of techniques for the analysis of event data where events are associated to several objects. To store Object-centric Event Logs (OCELs)…
An Event Data Extraction Approach from SAP ERP for Process Mining
Alessandro Berti, Gyunam Park, Majid Rafiei +1
The extraction, transformation, and loading of event logs from information systems is the first and the most expensive step in process mining. In particular, extracting event logs…
Process Comparison Using Object-Centric Process Cubes
Anahita Farhang Ghahfarokhi, Alessandro Berti, Wil M. P. van der Aalst
Process mining provides ways to analyze business processes. Common process mining techniques consider the process as a whole. However, in real-life business processes, different be…
Extracting Multiple Viewpoint Models from Relational Databases
Alessandro Berti, Wil van der Aalst
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually…
Increasing Scalability of Process Mining using Event Dataframes: How Data Structure Matters
Alessandro Berti
Process Mining is a branch of Data Science that aims to extract process-related information from event data contained in information systems, that is steadily increasing in amount.…
StarStar Models: Process Analysis on top of Databases
Alessandro Berti, Wil van der Aalst
Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually…