The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Visual Analytics: Definition, Process, and Challenges
Open accessAbstract
We are living in a world which faces a rapidly increasing amount of data to be dealt with on a daily basis. In the last decade, the steady improvement of data storage devices and means to create and collect data along the way influenced our way of dealing with information: Most of the time, data is stored without filtering and refinement for later use. Virtually every branch of industry or business, and any political or personal activity nowadays generate vast amounts of data. Making matters worse, the possibilities to collect and store data increase at a faster rate than our ability to use it for making decisions. However, in most applications, raw data has no value in itself; instead we want to extract the information contained in it.
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Cites
Toward measuring visualization insight
North, C. (2006) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2006.70 · Cited by 456
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“Although both areas have been intensively studied with regard to large, distributed, heterogeneous, and streaming data fusion [144] and data visualisation and analysis [145] , the current, rapid evolution of social media sources jointly with big data technologies creates some particularly interesting challenges relate…”
BackgroundDiez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633
Visualizing high-dimensional data: advances in the past decade
Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2016.2640960 · Cited by 427
Leading teachers' perspective on teacher-AI collaboration in education
Kim, J. (2024) · Education and Information Technologies · doi:10.1007/s10639-023-12109-5 · Cited by 376
Visual learning analytics of educational data: A systematic literature review and research agenda
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Metadata
- Authors
- Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., & Melançon, G.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008-07-18
- Pages
- 154-175
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2141115795
- Cited by
- 1,063
- References
- 1
- FWCI
- 13.62
- Type
- conference paper
- Language
- en
- Open access
- green
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- Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual Analytics: Definition, Process, and Challenges. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-70956-5_7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
