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pro vyhledávání: '"Van der Aa"'
Autor:
Neuberger, Julian, van der Aa, Han, Ackermann, Lars, Buschek, Daniel, Herrmann, Jannic, Jablonski, Stefan
Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can uti
Externí odkaz:
http://arxiv.org/abs/2410.01356
Autor:
Kirchdorfer, Lukas, Blümel, Robert, Kampik, Timotheus, van der Aa, Han, Stuckenschmidt, Heiner
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Alt
Externí odkaz:
http://arxiv.org/abs/2408.08571
Over the past decade, extensive research efforts have been dedicated to the extraction of information from textual process descriptions. Despite the remarkable progress witnessed in natural language processing (NLP), information extraction within the
Externí odkaz:
http://arxiv.org/abs/2407.18540
Mining Constraints from Reference Process Models for Detecting Best-Practice Violations in Event Log
Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typically require a normative process model as input, specifically designed
Externí odkaz:
http://arxiv.org/abs/2407.02336
The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability of LLMs to support process analysis and even, to some extent, that the
Externí odkaz:
http://arxiv.org/abs/2407.02310
Autor:
van Herpen, Merel Marjolein, Boeschoten, Manon A, te Brake, Hans, van der Aa, Niels, Olff, Miranda
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 6, p e29776 (2021)
Externí odkaz:
https://doaj.org/article/1d7e8bdde5aa49fda3c0700a20d33339
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperfor
Externí odkaz:
http://arxiv.org/abs/2404.06267
Autor:
van Herpen, Merel Marjolein, Boeschoten, Manon A, te Brake, Hans, van der Aa, Niels, Olff, Miranda
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 9, p e19716 (2020)
BackgroundMost people who experience a potentially traumatic event (PTE) recover on their own. A small group of individuals develops psychological complaints, but this is often not detected in time or guidance to care is suboptimal. To identify these
Externí odkaz:
https://doaj.org/article/9b7b33c894374c1085e803249cc8eb19
Autor:
Leopold, Henrik, van der Aa, Han, Pittke, Fabian, Raffel, Manuel, Mendling, Jan, Reijers, Hajo A.
Documenting business processes using process models is common practice in many organizations. However, not all process information is best captured in process models. Hence, many organizations complement these models with textual descriptions that sp