Visual Analytics: Scope and Challenges

By Daniel A. Keim, Florian Mansmann, Jörn Schneidewind, James J. Thomas, Hartmut Ziegler. Visual Data Mining, 2008

606 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 rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating 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, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/4caf28975d0fd8bf6f7356ce2f6706e1101d3612

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Information Systems and Management

Andreas Holzinger

Citations in the corpus

Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together? (2013, positive): “Keim et al. [29] provided a formal description of the visual analytics process, following some notions of Van Wijk [32], which demonstrates a core research of HCI relevant to solve problems involved with complex data: Let the input data sets be , … , whereas each set , (cid:1861) ∈ (cid:4666)1, … ,…”
The importance of interpretability and visualization in machine learning for applications in medicine and health care (2019, background): “Visual analytics has, in fact, become a research ﬁeld on its own [26, 27].”
Quality Metrics in High-Dimensional Data Visualization: An Overview and Systematization (2011, background): “Nonetheless, the usefulness of the proposed model is demonstrated by its ability to describe the whole set of papers and to identify relevant gaps interesting for future research.”
Knowledge Generation Model for Visual Analytics (2014, positive): “First, we build our initial process model by significantly extending the previous models [15, 16] (Section 2).”
Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics (2021, neutral): “However, this was at a time where Deep Learning was not used very much [20].”