Machine learning and Learning analytics are frequently intertwined in her study. Her research on Learning analytics frequently links to adjacent areas such as Data mining. She merges many fields, such as Data mining and Missing data, in her writings. In her research, she performs multidisciplinary study on Missing data and Imputation (statistics). Her Machine learning research extends to Imputation (statistics), which is thematically connected. She integrates Analytics and Software analytics in her research. Marta Indulska integrates Software analytics with Analytics in her research. Many of her studies on Data science apply to Dashboard as well. Her Dashboard study frequently intersects with other fields, such as Data science.
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How do practitioners use conceptual modeling in practice
Islay Davies;Peter Green;Michael Rosemann;Marta Indulska.
Business process modeling - a comparative analysis
Jan C. Recker;Michael Rosemann;Marta Indulska;Peter Green.
Business Process Modeling: Current Issues and Future Challenges
Marta Indulska;Jan Recker;Michael Rosemann;Peter Green.
Modeling languages for business processes and business rules: A representational analysis
Michael zur Muehlen;Marta Indulska.
Information Systems (2010)
How Good is BPMN Really? Insights from Theory and Practice
Jan C. Recker;Marta Indulska;Michael Rosemann;Peter Green.
Major issues in business process management: An expert perspective
Wasana Bandara;Marta Indulska;Sandy Chong;Shazia Sadiq.
european conference on information systems (2007)
Business Process Modeling: Perceived Benefits
Marta Indulska;Peter Green;Jan Recker;Michael Rosemann.
Do ontological de ficiencies in modeling grammars matter
Jan Recker;Michael Rosemann;Peter Green;Marta Indulska.
A study of the evolution of the representational capabilities of process modeling grammars
Michael Rosemann;Jan Recker;Marta Indulska;Peter Green.
Quantitative approaches to content analysis: identifying conceptual drift across publication outlets
Marta Indulska;Dirk S. Hovorka;Jan C. Recker.
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