Jochen Hipp

2.5k total citations · 1 hit paper
19 papers, 878 citations indexed

About

Jochen Hipp is a scholar working on Information Systems, Automotive Engineering and Artificial Intelligence. According to data from OpenAlex, Jochen Hipp has authored 19 papers receiving a total of 878 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Information Systems, 6 papers in Automotive Engineering and 6 papers in Artificial Intelligence. Recurrent topics in Jochen Hipp's work include Data Mining Algorithms and Applications (7 papers), Autonomous Vehicle Technology and Safety (6 papers) and Rough Sets and Fuzzy Logic (5 papers). Jochen Hipp is often cited by papers focused on Data Mining Algorithms and Applications (7 papers), Autonomous Vehicle Technology and Safety (6 papers) and Rough Sets and Fuzzy Logic (5 papers). Jochen Hipp collaborates with scholars based in Germany and United States. Jochen Hipp's co-authors include Ulrich Güntzer, Gholamreza Nakhaeizadeh, Christoph Stiller, Martin Haueis, Manfred Reichert, Henning Lategahn, Markus Schreiber, Carsten Knöppel, Julius Ziegler and Christoph G. Keller and has published in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems, Image and Vision Computing and IEEE Robotics and Automation Letters.

In The Last Decade

Jochen Hipp

19 papers receiving 781 citations

Hit Papers

Algorithms for association rule mining — a general survey... 2000 2026 2008 2017 2000 100 200 300 400 500
What are hit papers?

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.

  1. Algorithms for association rule mining — a general survey and comparison
    2000 566 citations Jochen Hipp, Ulrich Güntzer et al. ACM SIGKDD Explorations Newsletter profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jochen Hipp Germany 9 431 307 222 180 147 19 878
Ashish Jain India 11 256 0.6× 191 0.6× 46 0.2× 52 0.3× 153 1.0× 52 785
Wenhua Zeng China 19 277 0.6× 479 1.6× 325 1.5× 33 0.2× 151 1.0× 73 1.1k
Christian Prehofer Germany 20 281 0.7× 398 1.3× 127 0.6× 54 0.3× 130 0.9× 121 1.5k
Kamal Jambi Saudi Arabia 15 200 0.5× 697 2.3× 304 1.4× 43 0.2× 137 0.9× 62 1.2k
Stefan Edelkamp Germany 18 81 0.2× 946 3.1× 323 1.5× 96 0.5× 259 1.8× 111 1.4k
C.M.H. Kuijpers Spain 4 113 0.3× 672 2.2× 218 1.0× 70 0.4× 104 0.7× 4 1.0k
Habiba Drias Algeria 17 324 0.8× 434 1.4× 159 0.7× 116 0.6× 115 0.8× 120 880
Ming‐Chao Chiang Taiwan 16 337 0.8× 436 1.4× 103 0.5× 113 0.6× 269 1.8× 83 1.3k
Katsuhiro Honda Japan 16 212 0.5× 816 2.7× 129 0.6× 245 1.4× 466 3.2× 209 1.3k
Andrei Petrovski United Kingdom 13 164 0.4× 443 1.4× 136 0.6× 156 0.9× 71 0.5× 76 926

Countries citing papers authored by Jochen Hipp

Since Specialization
Citations

This map shows the geographic impact of Jochen Hipp'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 Jochen Hipp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jochen Hipp more than expected).

Fields of papers citing papers by Jochen Hipp

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jochen Hipp. 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 Jochen Hipp. The network helps show where Jochen Hipp may publish in the future.

Co-authorship network of co-authors of Jochen Hipp

This figure shows the co-authorship network connecting the top 25 collaborators of Jochen Hipp. A scholar is included among the top collaborators of Jochen Hipp 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 Jochen Hipp. Jochen Hipp is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Hipp, Jochen, et al.. (2026). Estimating the Joint Probability of Scenario Parameters With Gaussian Mixture Copula Models. IEEE Open Journal of Intelligent Transportation Systems. 7. 508–515. 1 indexed citations
2.
Hipp, Jochen, et al.. (2022). Identifying Scenarios in Field Data to Enable Validation of Highly Automated Driving Systems. arXiv (Cornell University). 134–142. 7 indexed citations
3.
Hipp, Jochen, et al.. (2021). Predicting the Time Until a Vehicle Changes the Lane Using LSTM-Based Recurrent Neural Networks. IEEE Robotics and Automation Letters. 6(2). 2357–2364. 23 indexed citations
4.
Hipp, Jochen, et al.. (2020). Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets. IEEE Transactions on Intelligent Transportation Systems. 22(11). 7129–7144. 29 indexed citations
5.
Hipp, Jochen, et al.. (2020). A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions. arXiv (Cornell University). 2739–2745. 5 indexed citations
6.
Hipp, Jochen, et al.. (2019). CPD: Crowd-based Pothole Detection. 33–42. 1 indexed citations
7.
Hipp, Jochen, et al.. (2019). CPD: Crowd-based Pothole Detection. HRB National Drugs Library (Health Research Board). 33–42. 4 indexed citations
8.
Pfeifer, Norbert, et al.. (2016). Deriving HD maps for highly automated driving from vehicular probe data. Fraunhofer-Publica (Fraunhofer-Gesellschaft). 1745–1752. 33 indexed citations
9.
Ziegler, Julius, Henning Lategahn, Markus Schreiber, et al.. (2014). Video based localization for Bertha. 1231–1238. 82 indexed citations
10.
Hipp, Jochen, et al.. (2008). MINING FREQUENT TEMPORAL PATTERNS IN INTERVAL SEQUENCES. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 16(5). 645–661. 3 indexed citations
11.
Hipp, Jochen, et al.. (2007). Rule-Based Measurement Of Data Quality In Nominal Data.. ICIQ. 69(823). 364–378. 7 indexed citations
12.
Hipp, Jochen, et al.. (2005). Regelbasierte Ausreißersuche zur Datenqualitätsanalyse.. Datenbank-Spektrum. 14. 22–28. 5 indexed citations
13.
Hipp, Jochen, et al.. (2004). Outlier Detection by Rareness Assumption.. GI Jahrestagung (1). 244–248. 1 indexed citations
14.
Hipp, Jochen & Ulrich Güntzer. (2002). Is pushing constraints deeply into the mining algorithms really what we want?. ACM SIGKDD Explorations Newsletter. 4(1). 50–55. 25 indexed citations
15.
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
16.
Wirth, Rüdiger, et al.. (2001). When Distribution is Part of the Semantics: A New Problem Class for Distributed Knowledge Discovery. 15 indexed citations
17.
Hipp, Jochen, Ulrich Güntzer, & Gholamreza Nakhaeizadeh. (2000). Algorithms for association rule mining — a general survey and comparison. ACM SIGKDD Explorations Newsletter. 2(1). 58–64. 566 indexed citations breakdown →
18.
Stiller, Christoph, et al.. (2000). Multisensor obstacle detection and tracking. Image and Vision Computing. 18(5). 389–396. 55 indexed citations
19.
Hipp, Jochen, et al.. (1999). Analysing Warranty Claims of Automobiles; An Application Description Following the CRISP-DM Data Mining Process. 31–40. 11 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.

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