What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Database
Visualization, Geovisualization, Data science, Geospatial analysis and Geographic information system are his primary areas of study.
His Visualization research is multidisciplinary, incorporating elements of Representation, Visual programming language and Database.
His research in Geovisualization intersects with topics in Geospatial predictive modeling, Geographic information systems in geospatial intelligence and Knowledge extraction.
His Data science study combines topics from a wide range of disciplines, such as Control, Metadata and Citation.
His Geospatial analysis research includes themes of World Wide Web, Data visualization and Cyberinfrastructure.
His Geographic information system study deals with Propagation of uncertainty intersecting with Data mining.
His most cited work include:
- Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know (392 citations)
- Geospatial Cyberinfrastructure: Past, present and future (272 citations)
- Geovisualization for knowledge construction and decision support (175 citations)
What are the main themes of his work throughout his whole career to date?
Mark Gahegan spends much of his time researching Data science, Visualization, World Wide Web, Data mining and Geographic information system.
Mark Gahegan interconnects Ontology, Geovisualization and Knowledge representation and reasoning in the investigation of issues within Data science.
Mark Gahegan has included themes like Decision support system, The Internet and Usability in his Geovisualization study.
Visualization is a subfield of Artificial intelligence that Mark Gahegan investigates.
The various areas that Mark Gahegan examines in his Geographic information system study include Data modeling, Table and Geospatial analysis.
His studies in Geospatial analysis integrate themes in fields like Data visualization and Semantic interoperability.
He most often published in these fields:
- Data science (38.83%)
- Visualization (18.45%)
- World Wide Web (17.48%)
What were the highlights of his more recent work (between 2011-2021)?
- Data science (38.83%)
- World Wide Web (17.48%)
- Semantics (6.80%)
In recent papers he was focusing on the following fields of study:
Mark Gahegan focuses on Data science, World Wide Web, Semantics, Metadata and Geographic information system.
His Data science study incorporates themes from Data modeling, Ontology and Workflow.
His work in the fields of World Wide Web, such as Web science, overlaps with other areas such as Publishing, Reuse, Systems research and Observatory.
His Semantics research includes elements of Programming language, Cognitive science and Data model.
His Metadata research includes themes of Control and Data citation, Citation.
Geographic information system is closely attributed to Knowledge transfer in his research.
Between 2011 and 2021, his most popular works were:
- Biodiversity data should be published, cited, and peer reviewed (145 citations)
- Visual Semiotics a Uncertainty Visualization: An Empirical Study (145 citations)
- Frankenplace: Interactive Thematic Mapping for Ad Hoc Exploratory Search (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
His primary areas of study are Data science, Metadata, Geographic information system, Visual analytics and Citation.
When carried out as part of a general Data science research project, his work on Volunteered geographic information is frequently linked to work in Chronotope, therefore connecting diverse disciplines of study.
He interconnects Cognitive science, Relation and Data model in the investigation of issues within Metadata.
His studies deal with areas such as Data modeling, Semantics and Subject as well as Geographic information system.
Visualization and Artificial intelligence are all intrinsically tied to his study in Visual analytics.
The Data citation research he does as part of his general Citation study is frequently linked to other disciplines of science, such as Technical peer review and Data quality, therefore creating a link between diverse domains of science.
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