What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Operating system
Wil M. P. van der Aalst focuses on Business process, Process mining, Petri net, Workflow and Process modeling.
His work is dedicated to discovering how Business process, Process management are connected with Information management and other disciplines.
Wil M. P. van der Aalst combines subjects such as Data mining, Business process discovery, Data science, Event and Conformance checking with his study of Process mining.
He has included themes like Correctness and Theoretical computer science in his Petri net study.
His Workflow research incorporates elements of Service-oriented architecture, Collaborative software, Exception handling and Document management system.
The concepts of his Process modeling study are interwoven with issues in Machine learning, Software and Decision support system, Artificial intelligence.
His most cited work include:
- Process Mining: Discovery, Conformance and Enhancement of Business Processes (1437 citations)
- Workflow Management: Models, Methods, and Systems (1277 citations)
- Business process management: a survey (1135 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Process mining, Process modeling, Business process, Event and Business process discovery.
His studies deal with areas such as Conformance checking, Data mining and Data science as well as Process mining.
Wil M. P. van der Aalst works mostly in the field of Process modeling, limiting it down to topics relating to Petri net and, in certain cases, Theoretical computer science and Concurrency, as a part of the same area of interest.
Wil M. P. van der Aalst has researched Business process in several fields, including Software engineering, Workflow and Process management.
His Software engineering study incorporates themes from Business Process Execution Language, Database, Systems engineering and Workflow management system, XPDL.
His Event research incorporates themes from Variety and Resource.
He most often published in these fields:
- Process mining (62.12%)
- Process modeling (38.42%)
- Business process (33.60%)
What were the highlights of his more recent work (between 2018-2021)?
- Process mining (62.12%)
- Event (33.33%)
- Process modeling (38.42%)
In recent papers he was focusing on the following fields of study:
Wil M. P. van der Aalst spends much of his time researching Process mining, Event, Process modeling, Business process discovery and Data mining.
The Process mining study combines topics in areas such as Data science, Context, Software engineering and Conformance checking.
His Event research is multidisciplinary, incorporating perspectives in Distributed computing, Object, Resource, System dynamics and Data structure.
His study looks at the relationship between Process modeling and topics such as Business process management, which overlap with Workflow.
Business process discovery is a subfield of Business process modeling that Wil M. P. van der Aalst explores.
His research in Data mining tackles topics such as Graph which are related to areas like Time complexity and Uncertain data.
Between 2018 and 2021, his most popular works were:
- Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science (52 citations)
- Online conformance checking: relating event streams to process models using prefix-alignments (33 citations)
- Earth movers’ stochastic conformance checking (22 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Operating system
His main research concerns Process mining, Event, Process modeling, Business process discovery and Data mining.
His Process mining research is under the purview of Business process.
His work on Business process management as part of general Business process research is often related to Sample, thus linking different fields of science.
His study in Process modeling is interdisciplinary in nature, drawing from both Data modeling, Distributed computing, Business Process Model and Notation, Graph and Abstract process.
His study explores the link between Business process discovery and topics such as Object that cross with problems in Information retrieval and Identifier.
His research integrates issues of Context, Chaotic and Trajectory in his study of Data mining.
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