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.
Algorithms for association rule mining — a general survey and comparison
2000566 citationsJochen Hipp, Ulrich Güntzer et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Ulrich Güntzer
Since
Specialization
Citations
This map shows the geographic impact of Ulrich Güntzer'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 Ulrich Güntzer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ulrich Güntzer more than expected).
This network shows the impact of papers produced by Ulrich Güntzer. 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 Ulrich Güntzer. The network helps show where Ulrich Güntzer may publish in the future.
Co-authorship network of co-authors of Ulrich Güntzer
This figure shows the co-authorship network connecting the top 25 collaborators of Ulrich Güntzer.
A scholar is included among the top collaborators of Ulrich Güntzer 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 Ulrich Güntzer. Ulrich Güntzer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Güntzer, Ulrich, et al.. (2014). TopCrowd - Efficient Crowd-enabled Top-k Retrieval on Incomplete Data.. 122–135.2 indexed citations
Balke, Wolf‐Tilo, Wolf Siberski, & Ulrich Güntzer. (2007). Getting Prime Cuts from Skylines over Partially Ordered Domains.. BTW. 64–81.6 indexed citations
6.
Balke, Wolf‐Tilo, Ulrich Güntzer, & Christoph Lofi. (2007). User Interaction Support for Incremental Refinement of Preference-Based Queries.. 209–220.17 indexed citations
7.
Balke, Wolf‐Tilo, Ulrich Güntzer, & Christoph Lofi. (2007). Incremental Trade-Off Management for Preference-Based Queries.. 4. 75–91.8 indexed citations
Hipp, Jochen, et al.. (2001). Integrating Association Rule Mining Algorithms with Relational Database Systems.. International Conference on Enterprise Information Systems. 130–137.5 indexed citations
12.
Güntzer, Ulrich, Wolf‐Tilo Balke, & Werner Kießling. (2000). Optimizing Multi-Feature Queries for Image Databases. OPUS (Augsburg University). 419–428.188 indexed citations
13.
Güntzer, Ulrich, et al.. (1995). Modeling, Chaining and Fusion of Uncertain Knowledge. OPUS (Augsburg University). 197–205.3 indexed citations
14.
Güntzer, Ulrich, et al.. (1991). Automatic Transformation of linear Text into Hypertext. 498–506.5 indexed citations
15.
Kießling, Werner & Ulrich Güntzer. (1990). Deduktive Datenbanksysteme auf dem Weg zur Praxis. OPUS (Augsburg University). 5(4). 177–187.2 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.