Challenges in Visual Data Analysis

By Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, Hartmut Ziegler. International Conference on Information Visualisation, 2006

519 citations recorded by the source corpus.

In today's applications data is produced at unprecedented rates. While the capacity to collect and store new data grows rapidly, the ability to analyze these data volumes increases at much lower pace. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information "concealed" in the data. The emerging field of visual analytics focuses on handling massive, heterogenous, and dynamic volumes of information through integration of human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, and details the most important technical research challenges in the field

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/61dd71f9c2c877651a50a5422f23739aef42f09f

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, methodology): “Recent advances in computing and storage technologies have made it possible to create, collect, and store huge volumes of data in a variety of data formats, languages, and cultures([1]).”
Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation (2008, methodology): “More formally, visual exploration [17, 19] is the use of interactive visualization for exploratory data analysis [7, 8, 34].”
Visualizing Big Data with augmented and virtual reality: challenges and research agenda (2015, background): “in order to avoid misperceptions and cognitive overload [198].”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “further, details on demand”[65] indicating that it is not sufficient to just retrieve and display the data using a visual metaphor; rather, it is necessary to analyse the data according to its value of interest, showing the most relevant aspects of the data, and at the same time providing interactio”
A survey on information visualization: recent advances and challenges (2014, methodology): “To solve this issue, researchers have developed many data reduction techniques such as sampling, filtering, clustering, PCA, and multidimensional scaling [75].”