Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis
- Submitting institution
-
City, University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 766
- Type
- D - Journal article
- DOI
-
10.1109/TVCG.2016.2598470
- Title of journal
- IEEE Transactions on Visualization and Computer Graphics
- Article number
- -
- First page
- 131
- Volume
- 23
- Issue
- 1
- ISSN
- 1077-2626
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First guidelines for high-dimensional data analysis through progressive analysis. Presented at IEEE VIS (A CORE), Baltimore, 2016 - acceptance 25%. Initiated by a Newton Fund supported research visit, bringing together researchers from three universities and the analysis team of one of Turkey’s largest banks. Led to invited talk at Dagstuhl Seminar 18411 on Progressive Data Analysis. Contributed to first author's (Turkay’s) recognition as EuroVis Young Researcher 2019 and EPSRC First Grant "nlvis: Natural Language Interaction for Visual Data Analysis" (EP/P025501/1). Influenced further work in deep neural network analysis (Pezzotti et al. 2017) and database applications (Moritz et al., 2017).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
