A5101506719- Business Process Modeling and Analysis
- Service-Oriented Architecture and Web Services
- Semantic Web and Ontologies
- Scientific Computing and Data Management
- Robotic Mechanisms and Dynamics
- Big Data and Business Intelligence
- Collaboration in agile enterprises
- Semiconductor materials and devices
- Data Quality and Management
- Flexible and Reconfigurable Manufacturing Systems
- Fluid Dynamics Simulations and Interactions
- Robotic Path Planning Algorithms
- Complex Network Analysis Techniques
- Computational and Text Analysis Methods
- Software System Performance and Reliability
- Topic Modeling
- Manufacturing Process and Optimization
- Iterative Learning Control Systems
- Soft Robotics and Applications
- Advanced Surface Polishing Techniques
- Information Technology Governance and Strategy
- Advanced Database Systems and Queries
- Fluid Dynamics and Heat Transfer
- Software Engineering Research
- Digital Transformation in Industry
RWTH Aachen University
2020-2023
Fraunhofer Institute for Applied Information Technology
2020-2023
University of Bologna
2000-2017
University of Padua
2014-2015
Digital Wave (United States)
2002
University of Illinois Urbana-Champaign
1999
PM4Py is a Python library providing comprehensive array of tools for process mining. This paper presents an in-depth overview the library, including its integration with other libraries and latest features, such as object-centric Furthermore, we discuss significant impact within academia, industry, open-source community, evidenced by wide adoption substantial evolution. In short, essential tool researchers practitioners, paving way advancements in
Process mining, i.e., a sub-field of data science focusing on the analysis event generated during execution (business) processes, has seen tremendous change over past two decades. Starting off in early 2000's, with limited to no tool support, nowadays, several software tools, both open-source, e.g., ProM and Apromore, commercial, Disco, Celonis, ProcessGold, etc., exist. The commercial process mining tools provide support for implementing custom algorithms. Moreover, open-source are often...
Techniques to discover Petri nets from event data assume precisely one case identifier per event. These identifiers are used correlate events, and the resulting discovered net aims describe life-cycle of individual cases. In reality, there is not possible notion, but multiple intertwined notions. For example, events may refer mixtures orders, items, packages, customers, products. A package products, order, customer. Therefore, we need that each refers a collection objects, having type...
This paper presents an efficient interval-analysis-based algorithm to solve the direct geometrico-static problem (DGP) of underconstrained cable-driven parallel robots (CDPRs). Solving DGP for a generic CDPR consists finding all possible equilibrium poses end effector given cable lengths. Since cables impose unilateral constraints, configurations with one or more slack may occur. When number taut is smaller than six robot and solutions must be found considering loop-closure mechanical...
Abstract Object-centric process mining is a novel branch of that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced form mutually exclusive groups events with the specification case notion. The development object-centric related exploiting logs, which includes exploring and filtering behavior contained in logs constructing models can encode different classes objects their interactions (which be discovered logs). This paper...
This paper proposes a hybrid position–force control strategy for overconstrained cable-driven parallel robots (CDPRs). Overconstrained CDPRs have more cables (m) than degrees of freedom (n), and the idea proposed controller is to n in length other m−n ones force. Two implementations are developed, one using motor torque following-error feedback loop cable force control. A friction model robot kinematic chain introduced improve accuracy estimation. Compared similar approaches available...
The assessment of Large Language Models (LLMs) has traditionally focused on performance metrics tied directly to their task-solving capabilities. This paper introduces a novel benchmark explicitly designed measure personality traits in LLMs through scenario-based interpretive prompts. We detail the methodology behind this benchmark, where are presented with structured prompts inspired by psychological scenarios, and responses assessed via judge LLM. evaluation encompasses such as emotional...
The assessment of Large Language Models (LLMs) has traditionally focused on performance metrics tied directly to their task-solving capabilities. This paper introduces a novel benchmark explicitly designed measure personality traits in LLMs through scenario-based interpretive prompts. We detail the methodology behind this benchmark, where are presented with structured prompts inspired by psychological scenarios, and responses assessed via judge LLM. evaluation encompasses such as emotional...
<title>Abstract</title> The assessment of Large Language Models (LLMs) has traditionally focused on performance metrics tied directly to their task-solving capabilities. This paper introduces a novel benchmark explicitly designed measure personality traits in LLMs through scenario-based interpretive prompts. We detail the methodology behind this benchmark, where are presented with structured prompts inspired by psychological scenarios, and responses assessed via judge LLM. evaluation...
This technical report describes the intersection of process mining and large language models (LLMs), specifically focusing on abstraction traditional object-centric artifacts into textual format. We introduce explore various prompting strategies: direct answering, where model directly addresses user queries; multi-prompt which allows to incrementally build knowledge obtained through a series prompts; generation database queries, facilitating validation hypotheses against original event log....
Object-Centric Event Logs (OCELs) form the basis for Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered development of a range OCPM techniques. 2.0 forms new, more expressive standard, allowing extensive process analyses while remaining an easily exchangeable format. In contrast to it can depict changes objects, provide information on object relationships, qualify these relationships other objects or specific events. Compared XES, is expressive, less complicated, better...
This paper studies the phenomenon of sloshing in field automatic machines for packaging liquid products, with specific reference to containers planar motions. After introducing two equivalent discrete models based on a mass-spring-damper system borrowed from literature (one linear and one non-linear), novel method is proposed evaluate height liquid, namely deviation its free surface at wall container equilibrium condition. The merits this are that it easy use, requiring no experimental...
Abstract Process mining provides a collection of techniques to gain insights into business processes by analyzing event logs. Organizations can various their using process techniques. Such use logs extracted from relational databases supporting the as input. However, extracting is challenging due size data, and it remains ad-hoc. Existing commercial tools partly support extraction logs, but they are proprietary focus on mainstream such Purchase-To-Pay (P2P) Order-To-Cash (O2C). Moreover,...
Abstract This paper studies the design of antisloshing trajectories for application in automatic machines packaging liquid products, with specific reference to cylindrical containers and emphasis on prescribed motion durations. Different strategies, based a discrete linear model sloshing phenomenon applicable real-time, are analyzed perform feedforward control container motion: finite impulse response (FIR) filters (input shapers others), dynamic-model inversion, infinite (IIR) filters....