Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Information visualization and visual data mining
2002975 citationsDaniel A. KeimIEEE Transactions on Visualization and Computer Graphicsprofile →
Countries citing papers authored by Daniel A. Keim
Since
Specialization
Citations
This map shows the geographic impact of Daniel A. Keim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel A. Keim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel A. Keim more than expected).
This network shows the impact of papers produced by Daniel A. Keim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel A. Keim. The network helps show where Daniel A. Keim may publish in the future.
Co-authorship network of co-authors of Daniel A. Keim
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel A. Keim.
A scholar is included among the top collaborators of Daniel A. Keim based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Daniel A. Keim. Daniel A. Keim is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kraus, Matthias, et al.. (2018). Visual Analytics System for Semi-automatic 4D Crime Scene Reconstruction. KOPS (University of Konstanz).1 indexed citations
9.
Schlegel, Udo, et al.. (2018). G-Rap: interactive text synthesis using recurrent neural network suggestions. KOPS (University of Konstanz).
10.
Sacha, Dominik, Michael Sedlmair, Leishi Zhang, et al.. (2016). Human-centered machine learning through interactive visualization. Middlesex University Research Repository (Middlesex University Of London). 641–646.13 indexed citations
11.
Kwon, Bum Chul, Florian Stoffel, Dominik Jäckle, Bongshin Lee, & Daniel A. Keim. (2014). VisJockey : Enriching Data Stories through Orchestrated Interactive Visualization.19 indexed citations
12.
Rohrdantz, Christian, Andreas Niekler, Annette Hautli-Janisz, Miriam Butt, & Daniel A. Keim. (2012). Lexical Semantics and Distribution of Suffixes - A Visual Analysis. KOPS (University of Konstanz). 7–15.7 indexed citations
Keim, Daniel A., et al.. (2009). Visual Analytics Challenges. Current Opinion in Rheumatology. 3(3). 457–62.7 indexed citations
16.
Mansmann, Svetlana, Florian Mansmann, Marc H. Scholl, & Daniel A. Keim. (2007). Hierarchy-driven Visual Exploration of Multidimensional Data Cubes.. BTW. 96–111.2 indexed citations
17.
Keim, Daniel A., Jörn Schneidewind, & Mike Sips. (2005). FP-Viz: Visual Frequent Pattern Mining. KOPS (University of Konstanz).11 indexed citations
18.
Zaı̈ane, Osmar R., Randy Goebel, David J. Hand, Daniel A. Keim, & Raymond T. Ng. (2002). Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. Knowledge Discovery and Data Mining.68 indexed citations
19.
Berchtold, Stefan & Daniel A. Keim. (2000). Indexing High-Dimensional Spaces: Database Support for Next Decade's Applications.. 698–699.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.