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.
Caries Detection on Intraoral Images Using Artificial Intelligence
2021133 citationsJan Kühnisch, Ole Meyer et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Volker Gruhn'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 Volker Gruhn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Volker Gruhn more than expected).
This network shows the impact of papers produced by Volker Gruhn. 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 Volker Gruhn. The network helps show where Volker Gruhn may publish in the future.
Co-authorship network of co-authors of Volker Gruhn
This figure shows the co-authorship network connecting the top 25 collaborators of Volker Gruhn.
A scholar is included among the top collaborators of Volker Gruhn 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 Volker Gruhn. Volker Gruhn is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Meyer, Ole, Marc Hesenius, & Volker Gruhn. (2020). Using Concepts to Understand Intelligent Agents..
7.
Hesenius, Marc, et al.. (2018). [Journal First] Does Syntax Highlighting Help Programming Novices?. International Conference on Software Engineering.
8.
Laue, Ralf, et al.. (2014). Analysing the Style of Textual Labels in i* Models..1 indexed citations
9.
Laue, Ralf & Volker Gruhn. (2007). Good and Bad Excuses for Unstructured Business Process Models.. European Conference on Pattern Languages of Programs. 279–290.12 indexed citations
10.
Gruhn, Volker, et al.. (2006). Aligning Software Architectures of Mobile Applications on Business Requirements.. Qucosa (Saxon State and University Library Dresden).3 indexed citations
11.
Gruhn, Volker & Ralf Laue. (2006). Komplexitätsmetriken für Geschäftsprozessmodelle. Qucosa (Saxon State and University Library Dresden). 289–292.1 indexed citations
12.
Book, Matthias, et al.. (2005). A Methodology for Deriving the Architectural Implications of Different Degrees of Mobility in Information Systems. Qucosa (Saxon State and University Library Dresden). 281–292.2 indexed citations
Beydeda, Sami & Volker Gruhn. (2003). BINTEST - Binary Search-based Test Case Generation. Qucosa (Saxon State and University Library Dresden).18 indexed citations
15.
Gruhn, Volker, et al.. (2000). Structuring Complex Software Processes by Process Landscaping (Short Paper). 138–149.2 indexed citations
Gruhn, Volker, et al.. (1995). Software Process Improvement by Business Process Orientation.. Software Process Improvement and Practice. 1. 49–56.10 indexed citations
19.
Gruhn, Volker. (1993). Software process simulation in MELMAC. Systems Analysis Modelling Simulation. 11(2). 121–141.2 indexed citations
20.
Deiters, Wolfgang & Volker Gruhn. (1991). Software process model analysis based on FUNSOFT nets. Systems Analysis Modelling Simulation. 8(4). 315–325.7 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.