12 citations · 47 across the 9 of their papers we have counts for
11 papers
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…
Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data
Daniel Schuster, Michael Martini, Sebastiaan J. van Zelst +1
Event logs, as viewed in process mining, contain event data describing the execution of operational processes. Most process mining techniques take an event log as input and generat…
A Framework for Extracting and Encoding Features from Object-Centric Event Data
Jan Niklas Adams, Gyunam Park, Sergej Levich +2
Traditional process mining techniques take event data as input where each event is associated with exactly one object. An object represents the instantiation of a process. Object-c…
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments
Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst +1
Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, loc…
Defining Cases and Variants for Object-Centric Event Data
Jan Niklas Adams, Daniel Schuster, Seth Schmitz +2
The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining te…
Visualizing Trace Variants From Partially Ordered Event Data
Daniel Schuster, Lukas Schade, Sebastiaan J. van Zelst +1
Executing operational processes generates event data, which contain information on the executed process activities. Process mining techniques allow to systematically analyze event…
Freezing Sub-Models During Incremental Process Discovery: Extended Version
Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Process discovery aims to learn a process model from observed process behavior. From a user's perspective, most discovery algorithms work like a black box. Besides parameter tuning…
Cortado---An Interactive Tool for Data-Driven Process Discovery and Modeling
Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst
Process mining aims to diagnose and improve operational processes. Process mining techniques allow analyzing the event data generated and recorded during the execution of (business…
Scalable Online Conformance Checking Using Incremental Prefix-Alignment Computation
Daniel Schuster, Gero J. Kolhof
Conformance checking techniques aim to collate observed process behavior with normative/modeled process models. The majority of existing approaches focuses on completed process exe…
Alignment Approximation for Process Trees
Daniel Schuster, Sebastiaan van Zelst, Wil M. P. van der Aalst
Comparing observed behavior (event data generated during process executions) with modeled behavior (process models), is an essential step in process mining analyses. Alignments are…
Online Process Monitoring Using Incremental State-Space Expansion: An Exact Algorithm
Daniel Schuster, Sebastiaan J. van Zelst
The execution of (business) processes generates valuable traces of event data in the information systems employed within companies. Recently, approaches for monitoring the correctn…