2023 - Research.com Computer Science in Australia Leader Award
His scientific interests lie mostly in Business process, Process modeling, Business process modeling, Business process management and Business process discovery. The concepts of his Business process study are interwoven with issues in Database and Data science. His biological study spans a wide range of topics, including Software engineering and Data mining.
His work in the fields of Business process modeling, such as Business Process Model and Notation and Artifact-centric business process model, overlaps with other areas such as Multiple Models. His study in Business process management is interdisciplinary in nature, drawing from both Knowledge management, Machine learning and Artificial intelligence. His research investigates the connection with Business process discovery and areas like Process mining which intersect with concerns in Business intelligence.
His primary areas of investigation include Business process, Process modeling, Process, Process mining and Business process modeling. His work in Business process covers topics such as Process which are related to areas like Real-time computing. His Process modeling study integrates concerns from other disciplines, such as Petri net, Business process discovery, Concurrency, Business Process Model and Notation and Software engineering.
The study incorporates disciplines such as Domain, Systems engineering, Reference model and Data science in addition to Process. His work deals with themes such as Scalability, Data mining, Artificial intelligence, Event and Machine learning, which intersect with Process mining. His work on Artifact-centric business process model as part of general Business process modeling study is frequently linked to Set, bridging the gap between disciplines.
His primary scientific interests are in Event, Business process, Process mining, Process modeling and Process. His research integrates issues of Process, Data mining, Timestamp, Artificial intelligence and Machine learning in his study of Event. The Business process study which covers Data science that intersects with Taxonomy.
Business process modeling covers Marcello La Rosa research in Process mining. The concepts of his Process modeling study are interwoven with issues in Scalability, Field, Business process discovery, Concurrency and Conformance checking. In general Process study, his work on Business process management often relates to the realm of Noise and Process automation system, thereby connecting several areas of interest.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Fundamentals of Business Process Management
Marlon Dumas;Marcello La Rosa;Jan Mendling;Hajo A. Reijers.
Process Mining Manifesto
Wil van der Aalst;Wil van der Aalst;Arya Adriansyah;Ana Karla Alves de Medeiros;Franco Arcieri.
Blockchains for Business Process Management - Challenges and Opportunities
Jan Mendling;Ingo Weber;Wil Van Der Aalst;Jan Vom Brocke.
Predictive Business Process Monitoring with LSTM Neural Networks
Niek Tax;Ilya Verenich;Marcello La Rosa;Marlon Dumas.
CONFIGURABLE WORKFLOW MODELS
F Florian Gottschalk;Wmp Wil van der Aalst;MH Monique Jansen-Vullers;Marcello La Rosa.
Business Process Variability Modeling: A Survey
Marcello La Rosa;Wil M. P. Van Der Aalst;Marlon Dumas;Fredrik P. Milani.
Configurable multi-perspective business process models
Marcello La Rosa;Marlon Dumas;Arthur H. M. ter Hofstede;Jan Mendling.
Business Process Model Merging: An Approach to Business Process Consolidation
Marcello La Rosa;Marlon Dumas;Reina Uba;Remco Dijkman.
APROMORE : an advanced process model repository
Marcello La Rosa;Hajo A. Reijers;Wil M.P. van der Aalst;Remco M. Dijkman.
Automated Discovery of Process Models from Event Logs: Review and Benchmark
Adriano Augusto;Raffaele Conforti;Marlon Dumas;Marcello La Rosa.
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