Process and Data Science
Process and Data Science
RWTH Aachen University · Computer Science
About
아헨 공과대학교 컴퓨터과학과 소속 Wil van der Aalst 교수의 등록된 연구 분야는 process mining, data science, business processes입니다.
Research keywords
Recent papers
- Reusable Neural Guidance for Petri Net Alignments with Replay VerificationApplied Sciences · Oct 7
- Inductive Process Discovery from Partially Ordered Event DataPreprintarXiv (Cornell University) · Aug 20
- Analyzing structural, operational, and performance impact of process-aware information system updatesInformation Systems · Aug 20
From OpenAlex · papers whose author affiliation matches this university
Labs doing related research
Labs that recently published on topics this lab's recent papers share
Shared topic: Business Process Modeling and Analysis
Stefanie Rinderle-Ma 교수 연구실
Technical University of Munich · Department of Computer Science
Decision-Aware Suffix Prediction and Reasoning of Business ProcessesPreprintarXiv (Cornell University) · Sep 5Luise Pufahl 교수 연구실
Technical University of Munich · Department of Computer Science
A conceptual model and supporting approach for EU taxonomy alignment with green BPMSoftware & Systems Modeling · Sep 19Johannes Fottner 교수 연구실
Technical University of Munich · Department of Mechanical Engineering
Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twinInternational Journal of Computer Assisted Radiology and Surgery · Sep 19
Other labs in this department
- Computation in Neural Circuits
Abigail Morrison
neural computation, computational neuroscience
- Computer Vision
Bastian Leibe
computer vision, object detection, visual recognition
- Software Engineering
Bernhard Rumpe
software engineering, modeling
- Combinatorial Optimization
Christina Büsing
optimization, combinatorial algorithms
- Logic and Theory of Discrete Systems
Christof Löding
logic, automata theory
- Machine Learning on Graphs
Christopher Morris
graph neural networks, machine learning