What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Software
Jean-Daniel Fekete mainly focuses on Visualization, Data visualization, Information visualization, Theoretical computer science and Graph drawing.
Jean-Daniel Fekete combines topics linked to Human–computer interaction with his work on Visualization.
His Data visualization research incorporates themes from Computer graphics, Graph theory, Information retrieval, Computer vision and Graphics.
His Information visualization study integrates concerns from other disciplines, such as Visual analytics, Multimedia and Data science.
His studies in Theoretical computer science integrate themes in fields like Adjacency matrix and Graph.
The study incorporates disciplines such as Graph and Social network in addition to Graph drawing.
His most cited work include:
- Visual Analytics: Definition, Process, and Challenges (617 citations)
- NodeTrix: a Hybrid Visualization of Social Networks (399 citations)
- Visual analysis of large graphs : state-of-the-art and future research challenges (347 citations)
What are the main themes of his work throughout his whole career to date?
Jean-Daniel Fekete focuses on Visualization, Information visualization, Human–computer interaction, Data visualization and Visual analytics.
His studies deal with areas such as Multimedia, Theoretical computer science, Computer graphics and Data science as well as Visualization.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Graph, Cluster analysis and Adjacency matrix, Graph drawing, Graph.
His Information visualization research is multidisciplinary, incorporating elements of Domain, Field, Event and World Wide Web.
His Human–computer interaction research incorporates elements of User interface, Input device and Focus.
His biological study spans a wide range of topics, including Information retrieval, Computer graphics, Computer vision and Graphics.
He most often published in these fields:
- Visualization (46.26%)
- Information visualization (26.17%)
- Human–computer interaction (21.96%)
What were the highlights of his more recent work (between 2014-2021)?
- Visualization (46.26%)
- Data science (14.95%)
- Visual analytics (19.63%)
In recent papers he was focusing on the following fields of study:
Visualization, Data science, Visual analytics, Human–computer interaction and Theoretical computer science are his primary areas of study.
His work carried out in the field of Visualization brings together such families of science as Field and World Wide Web.
The various areas that he examines in his Visual analytics study include Use case, Analytics and Computer data storage.
Jean-Daniel Fekete has researched Theoretical computer science in several fields, including Hypergraph, Graph and Adjacency matrix.
His work in Information visualization addresses issues such as Domain, which are connected to fields such as Workflow.
Data visualization is a subfield of Data mining that Jean-Daniel Fekete explores.
Between 2014 and 2021, his most popular works were:
- Matrix reordering methods for table and network visualization (97 citations)
- Storytelling in Information Visualizations: Does it Engage Users to Explore Data? (63 citations)
- How Progressive Visualizations Affect Exploratory Analysis (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Software
His main research concerns Visualization, Data visualization, Human–computer interaction, Information visualization and Data science.
His research in Visualization is mostly focused on Visual analytics.
His Data visualization research includes elements of Feature extraction and Set.
The Human–computer interaction study combines topics in areas such as Embedding, Motion and Open platform.
His Information visualization study combines topics from a wide range of disciplines, such as Interactive visualization, Animation and Data physicalization.
His study in Graph drawing is interdisciplinary in nature, drawing from both Adjacency matrix and Theoretical computer science.
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