Visual‐interactive preprocessing of multivariate time series data J Bernard, M Hutter, H Reinemuth, H Pfeifer, C Bors, J Kohlhammer Computer Graphics Forum 38 (3), 401-412, 2019 | 76 | 2019 |
Capturing and visualizing provenance from data wrangling C Bors, T Gschwandtner, S Miksch IEEE computer graphics and applications 39 (6), 61-75, 2019 | 28 | 2019 |
Visual interactive creation, customization, and analysis of data quality metrics C Bors, T Gschwandtner, S Kriglstein, S Miksch, M Pohl Journal of Data and Information Quality (JDIQ) 10 (1), 1-26, 2018 | 22 | 2018 |
Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series. J Bernard, C Bors, M Bögl, C Eichner, T Gschwandtner, S Miksch, ... EuroVA@ EuroVis, 49-53, 2018 | 18 | 2018 |
A provenance task abstraction framework C Bors, J Wenskovitch, M Dowling, S Attfield, L Battle, A Endert, O Kulyk, ... IEEE computer graphics and applications 39 (6), 46-60, 2019 | 13 | 2019 |
Visual support for rastering of unequally spaced time series C Bors, M Bögl, T Gschwandtner, S Miksch Proceedings of the 10th International Symposium on Visual Information …, 2017 | 12 | 2017 |
Quantifying Uncertainty in Multivariate Time Series Pre-Processing C Bors, J Bernard, M Bögl, T Gschwandtner, S Miksch, J Kohlhammer EuroVis Workshop on Visual Analytics 10, 31 - 35, 2019 | 8 | 2019 |
Visually Exploring Data Provenance and Quality of Open Data. C Bors, T Gschwandtner, S Miksch EuroVis (Posters), 9-11, 2018 | 5 | 2018 |
Qualityflow: Provenance generation from data quality C Bors, T Gschwandtner, S Miksch Poster Proceedings of the EuroGraphics Conference on Visualization, 2014 | 5 | 2014 |
Qualitytrails: Data quality provenance as a basis for sensemaking C Bors, T Gschwandtner, S Miksch, J Gärtner Proceedings of the International Workshop on Analytic Provenance for Sensemaking, 2014 | 5 | 2014 |
The Effect of Graph Layout on the Perception of Graph Properties. E Kypridemou, M Zito, M Bertamini EuroVis (Short Papers), 1-5, 2020 | 4 | 2020 |
Categorizing Uncertainties in the Process of Segmenting and Labeling Time Series Data. M Bögl, C Bors, T Gschwandtner, S Miksch EuroVis (Posters), 45-47, 2018 | 4 | 2018 |
Exploring Time Series Segmentations Using Uncertainty and Focus+ Context Techniques. C Bors, C Eichner, S Miksch, C Tominski, H Schumann, T Gschwandtner EuroVis (Short Papers), 7-11, 2020 | 3 | 2020 |
Facilitating data quality assessment utilizing visual analytics: tackling time, metrics, uncertainty, and provenance C Bors Technische Universität Wien, 2019 | 2 | 2019 |
4.3 A Novel Approach to Task Abstraction to Make Better Sense of Provenance Data C Bors, S Attfield, L Battle, M Dowling, A Endert, S Koch, OA Kulyk, ... Provenance and Logging for Sense Making, 46, 0 | 1 | |
AProvenance Task C Bors, J Wenskovitch, M Dowling, S Attfield, L Battle, A Endert, O Kulyk, ... | | 2019 |
Uncertainty types in segmenting and labeling time series data M Bögl, C Bors, T Gschwandtner, S Miksch | | 2018 |
Quantifying Uncertainty in Time Series Data Processing C Bors, M Bögl, J Bernard, T Gschwandtner, S Miksch | | 2018 |
Storage and visualization of heterogeneous data from online social networks C Bors, R Krejci | | 2013 |
Vorträge und Posterpräsentationen (ohne Tagungsband-Eintrag) Y Huang, J Neidhardt, N Contractor | | |