What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Data mining
- Statistics
Her scientific interests lie mostly in Visual analytics, Visualization, Data science, Data mining and Data visualization.
Her Visual analytics research is multidisciplinary, relying on both Graphics, Affordance, Knowledge acquisition, Sequence and Information visualization.
Her study on Geovisualization is often connected to Space as part of broader study in Visualization.
Natalia Andrienko interconnects Decision support system, Decision engineering and Geographic information system in the investigation of issues within Data science.
Her work on Exploratory data analysis as part of general Data mining study is frequently linked to Focus, therefore connecting diverse disciplines of science.
Her Data visualization research integrates issues from Domain, Knowledge extraction and Trajectory.
Her most cited work include:
- Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach (397 citations)
- Semantic trajectories modeling and analysis (361 citations)
- Interactive maps for visual data exploration (351 citations)
What are the main themes of her work throughout her whole career to date?
Natalia Andrienko focuses on Visual analytics, Visualization, Data science, Data mining and Data visualization.
The study incorporates disciplines such as Analytics and Human–computer interaction in addition to Visual analytics.
Her Visualization research incorporates themes from Exploratory data analysis, Temporal database and Geographic information system.
Her Data science study integrates concerns from other disciplines, such as Workflow, Geospatial analysis and Big data.
She combines subjects such as Spatial analysis, Trajectory, Information retrieval and Cluster analysis with her study of Data mining.
The concepts of her Data visualization study are interwoven with issues in Task and Task analysis.
She most often published in these fields:
- Visual analytics (61.46%)
- Visualization (38.87%)
- Data science (34.55%)
What were the highlights of her more recent work (between 2018-2021)?
- Visual analytics (61.46%)
- Data science (34.55%)
- Visualization (38.87%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Visual analytics, Data science, Visualization, Human–computer interaction and Data visualization.
Her Visual analytics study combines topics in areas such as Theoretical computer science, Pairwise comparison, Workflow and Process.
Her study in the field of Volunteered geographic information also crosses realms of Level of detail.
Her study in Visualization is interdisciplinary in nature, drawing from both Embedding, Data processing, Information retrieval and Hue.
Her Data visualization research incorporates elements of Exploratory data analysis, Analytics and Task analysis.
Her biological study spans a wide range of topics, including Machine learning and Artificial intelligence.
Between 2018 and 2021, her most popular works were:
- Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling (18 citations)
- COPE: Interactive Exploration of Co-Occurrence Patterns in Spatial Time Series (18 citations)
- A conceptual framework for studying collective reactions to events in location-based social media (15 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Natalia Andrienko mostly deals with Visual analytics, Data visualization, Human–computer interaction, Visualization and Social media.
Her studies deal with areas such as Topic model, Cluster analysis, Semantic similarity, Semantics and Workflow as well as Visual analytics.
Data visualization is a subfield of Data mining that Natalia Andrienko investigates.
Her Visualization research includes themes of Information retrieval and Trajectory.
Her biological study deals with issues like Data analysis, which deal with fields such as Data science and Big data.
The various areas that she examines in her Data science study include Phase and Leverage.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
