Michael Wurst

1.9k total citations · 1 hit paper
18 papers, 1.1k citations indexed

About

Michael Wurst is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Michael Wurst has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 5 papers in Signal Processing and 4 papers in Information Systems. Recurrent topics in Michael Wurst's work include Data Stream Mining Techniques (4 papers), Time Series Analysis and Forecasting (3 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Michael Wurst is often cited by papers focused on Data Stream Mining Techniques (4 papers), Time Series Analysis and Forecasting (3 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Michael Wurst collaborates with scholars based in Germany, United States and Switzerland. Michael Wurst's co-authors include Ingo Mierswa, Ralf Klinkenberg, Martin Scholz, Katharina Morik, Geoffrey D. Hannigan, Christopher H. Woelk, Danny A. Bitton, Grazia Piizzi, Ondřej Klempíř and David Příhoda and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Future Generation Computer Systems.

In The Last Decade

Michael Wurst

16 papers receiving 1.0k citations

Hit Papers

YALE 2006 2026 2012 2019 2006 200 400 600
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. YALE
    2006 661 citations Ingo Mierswa, Michael Wurst et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Wurst Germany 8 387 235 199 154 138 18 1.1k
Guy De Tré Belgium 18 454 1.2× 387 1.6× 112 0.6× 392 2.5× 96 0.7× 173 1.7k
Mengchen Liu China 19 598 1.5× 178 0.8× 129 0.6× 146 0.9× 771 5.6× 46 1.9k
Tobias Kötter Germany 8 173 0.4× 346 1.5× 134 0.7× 22 0.1× 112 0.8× 15 1.1k
Celine Vens Belgium 17 831 2.1× 565 2.4× 270 1.4× 59 0.4× 226 1.6× 73 1.8k
Nicolas Cebron Germany 6 196 0.5× 337 1.4× 114 0.6× 21 0.1× 92 0.7× 14 979
Lap–Kei Lee Hong Kong 16 352 0.9× 184 0.8× 212 1.1× 81 0.5× 78 0.6× 79 1.1k
Kilian Thiel Germany 7 182 0.5× 338 1.4× 124 0.6× 20 0.1× 70 0.5× 10 942
Michael J. McGuffin Canada 23 332 0.9× 227 1.0× 114 0.6× 205 1.3× 1.3k 9.5× 55 2.2k
Thomas R. Gabriel Germany 6 180 0.5× 337 1.4× 124 0.6× 22 0.1× 57 0.4× 9 932
Wen Hua Australia 21 511 1.3× 93 0.4× 182 0.9× 335 2.2× 142 1.0× 82 1.3k

Countries citing papers authored by Michael Wurst

Since Specialization
Citations

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

Fields of papers citing papers by Michael Wurst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Wurst

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

All Works

18 of 18 papers shown
1.
Wurst, Michael, et al.. (2022). Wie sieht die Arbeitsmedizin der Zukunft aus? (Pro und Contra). 2022(11). 678–682. 1 indexed citations
2.
Novak, Jasminko & Michael Wurst. (2020). Supporting Knowledge Creation and Sharing in Communities Based on Mapping Implicit Knowledge. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 10. 235–251. 5 indexed citations
3.
Hannigan, Geoffrey D., David Příhoda, Jindřich Soukup, et al.. (2019). A deep learning genome-mining strategy for biosynthetic gene cluster prediction. Nucleic Acids Research. 47(18). e110–e110. 196 indexed citations
4.
Sanderman, Jonathan, et al.. (2015). Impacts of Rotational Grazing on Soil Carbon in Native Grass-Based Pastures in Southern Australia. PLoS ONE. 10(8). e0136157–e0136157. 52 indexed citations
5.
Bouillet, Eric, et al.. (2014). TRISTAN: Real-time analytics on massive time series using sparse dictionary compression. 291–300. 18 indexed citations
6.
Bouillet, Eric, et al.. (2013). MiSTRAL: An architecture for low-latency analytics on MasSive time series. 15–21. 7 indexed citations
7.
Fusco, Francesco, et al.. (2012). Mining residential household information from low-resolution smart meter data. 3545–3548. 5 indexed citations
8.
Morik, Katharina, et al.. (2011). Multi-objective frequent termset clustering. Knowledge and Information Systems. 30(3). 715–738. 11 indexed citations
9.
Mitschang, Bernhard, et al.. (2010). Augmenting OLAP exploration with dynamic advanced analytics. 687–692. 5 indexed citations
10.
Hotho, Andreas, et al.. (2010). Ubiquitous data. 61–74. 4 indexed citations
11.
Wurst, Michael. (2007). Multi-agent Learning by Distributed Feature Extraction.. Adaptive Agents and Multi-Agents Systems. 239–254.
12.
Mierswa, Ingo & Michael Wurst. (2006). Sound Multi-objective Feature Space Transformation for Clustering. LWA. 330–337. 3 indexed citations
13.
Mierswa, Ingo & Michael Wurst. (2006). Efficient Case Based Feature Construction for Heterogeneous Learning Tasks. Technische Universität Dortmund Eldorado (Technische Universität Dortmund). 2 indexed citations
14.
Mierswa, Ingo & Michael Wurst. (2006). Information preserving multi-objective feature selection for unsupervised learning. 1545–1552. 33 indexed citations
15.
Wurst, Michael & Katharina Morik. (2006). Distributed feature extraction in a p2p setting — a case study. Future Generation Computer Systems. 23(1). 69–75. 12 indexed citations
16.
Mierswa, Ingo, et al.. (2006). YALE. 935–940. 661 indexed citations breakdown →
17.
Wurst, Michael. (2006). Analysis and evaluation of distributed knowledge management by agent-based simulation. International Journal of Knowledge-based and Intelligent Engineering Systems. 10(4). 307–317. 1 indexed citations
18.
Mierswa, Ingo, et al.. (2005). A Benchmark Dataset For Audio Classification And Clustering.. International Symposium/Conference on Music Information Retrieval. 378(2). 528–531. 69 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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