What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- Software
Her primary areas of investigation include Business process, Knowledge management, Software engineering, Work in process and Process modeling.
Stefanie Rinderle-Ma has researched Business process in several fields, including Field, Risk analysis, Workflow and Data science.
Her research in Risk analysis intersects with topics in Control, Key and Systems engineering.
Her work in Knowledge management addresses subjects such as Business process management, which are connected to disciplines such as Blockchain, Autonomous agent and The Internet.
Her work deals with themes such as Abstract process, Business process modeling, Process modeling languages and Process management, which intersect with Software engineering.
Her research integrates issues of Data mining and Artificial intelligence in her study of Process modeling.
Her most cited work include:
- Change patterns and change support features - Enhancing flexibility in process-aware information systems (488 citations)
- Blockchains for Business Process Management - Challenges and Opportunities (230 citations)
- On enabling integrated process compliance with semantic constraints in process management systems (121 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Business process, Process modeling, Data mining, Business process management and Work in process.
Her biological study spans a wide range of topics, including Knowledge management, Risk analysis, Visualization, Software engineering and Process management.
Stefanie Rinderle-Ma connects Software engineering with Flexibility in her research.
Her work in Process modeling covers topics such as Business process modeling which are related to areas like Business rule.
Her Data mining research also works with subjects such as
- Process mining which is related to area like Event,
- Control flow most often made with reference to Process.
Her Business process management study integrates concerns from other disciplines, such as The Internet and Engineering management.
She most often published in these fields:
- Business process (32.43%)
- Process modeling (20.46%)
- Data mining (15.06%)
What were the highlights of her more recent work (between 2016-2021)?
- Business process (32.43%)
- Data science (12.36%)
- Process modeling (20.46%)
In recent papers she was focusing on the following fields of study:
Her primary areas of study are Business process, Data science, Process modeling, Software engineering and Domain.
Many of her research projects under Business process are closely connected to Work in process and Focus with Work in process and Focus, tying the diverse disciplines of science together.
Her Business process management research includes elements of Blockchain, Knowledge management and Process management.
Her Data science study combines topics in areas such as Field, Scalability and Visualization.
Her Process modeling research includes themes of Orchestration and Business process modeling.
Her Software engineering study incorporates themes from Business Process Model and Notation and Enterprise application integration.
Between 2016 and 2021, her most popular works were:
- Blockchains for Business Process Management - Challenges and Opportunities (230 citations)
- The Internet-of-Things Meets Business Process Management: Mutual Benefits and Challenges (34 citations)
- Patterns for emerging application integration scenarios (27 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Psychology
Her scientific interests lie mostly in Business process, Business process management, Enterprise application integration, Event and Data science.
Stefanie Rinderle-Ma has included themes like Root cause analysis and Anomaly detection, Data mining in her Business process study.
Her work deals with themes such as Distributed computing, The Internet, Blockchain and Knowledge management, which intersect with Business process management.
Her Enterprise application integration research includes elements of Graph rewriting, Model complexity, Correctness and Software engineering.
Her research integrates issues of Process mining and Concept drift in her study of Event.
Her study focuses on the intersection of Data science and fields such as Field with connections in the field of Autonomous agent.
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