What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Operating system
Matthias Weidlich mainly investigates Data mining, Process modeling, Business process modeling, Business process management and Business process.
He works mostly in the field of Data mining, limiting it down to topics relating to Queueing theory and, in certain cases, Queue, Event and Service.
His Process modeling research is multidisciplinary, incorporating elements of Causal consistency, Software and Reference model.
His research in the fields of Business process discovery overlaps with other disciplines such as Empirical research.
The Business process management study combines topics in areas such as Industrial engineering and Knowledge management.
His work carried out in the field of Business process brings together such families of science as Complex event processing and Aggregate.
His most cited work include:
- Process Mining Manifesto (684 citations)
- Blockchains for Business Process Management - Challenges and Opportunities (230 citations)
- Efficient Consistency Measurement Based on Behavioral Profiles of Process Models (158 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Business process, Process modeling, Data mining, Business process modeling and Event.
Matthias Weidlich has researched Business process in several fields, including Field and Data science.
The concepts of his Process modeling study are interwoven with issues in Software engineering and Industrial engineering.
The study incorporates disciplines such as Machine learning, Conformance checking and Artificial intelligence in addition to Data mining.
His work in the fields of Artifact-centric business process model overlaps with other areas such as TRACE.
Matthias Weidlich interconnects Knowledge management and Engineering management in the investigation of issues within Business process management.
He most often published in these fields:
- Business process (28.57%)
- Process modeling (26.73%)
- Data mining (25.81%)
What were the highlights of his more recent work (between 2019-2021)?
- Data mining (25.81%)
- Business process (28.57%)
- Process mining (15.21%)
In recent papers he was focusing on the following fields of study:
Matthias Weidlich focuses on Data mining, Business process, Process mining, Conformance checking and Event.
His research integrates issues of Complex event processing and Synthetic data in his study of Data mining.
His Business process study incorporates themes from Identification, Volume, Tree, Performance indicator and Semantics.
His Process mining research focuses on Data science and how it connects with Inter organizational, Business process management, The Internet and Autonomous agent.
His biological study spans a wide range of topics, including Process modeling, Process, Information technology, Precision and recall and Robustness.
He interconnects General Data Protection Regulation, Ranking, Conformity assessment, Pairwise comparison and Business process modeling in the investigation of issues within Process modeling.
Between 2019 and 2021, his most popular works were:
- Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems (15 citations)
- The Internet of Things Meets Business Process Management: A Manifesto (9 citations)
- Extraction, correlation, and abstraction of event data for process mining (9 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Operating system
His main research concerns Data mining, Process mining, Business process, Conformance checking and Data science.
His Data mining study integrates concerns from other disciplines, such as Complex event processing, Event correlation, Synthetic data and Event data.
His Process mining research includes elements of Consistency, Scalability and System requirements specification.
His research investigates the connection with Business process and areas like Event which intersect with concerns in Synchronization and Information sensitivity.
His Conformance checking research is multidisciplinary, relying on both Process modeling, Recall, Precision and recall, Business process discovery and Finite-state machine.
His Data science research is multidisciplinary, incorporating perspectives in Graph, Secure multi-party computation, Computation and Inter organizational.
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