Matthias Böehm

1.5k total citations
50 papers, 930 citations indexed

About

Matthias Böehm is a scholar working on Computer Networks and Communications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Matthias Böehm has authored 50 papers receiving a total of 930 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Networks and Communications, 21 papers in Artificial Intelligence and 17 papers in Information Systems. Recurrent topics in Matthias Böehm's work include Parallel Computing and Optimization Techniques (11 papers), Advanced Data Storage Technologies (9 papers) and Advanced Database Systems and Queries (9 papers). Matthias Böehm is often cited by papers focused on Parallel Computing and Optimization Techniques (11 papers), Advanced Data Storage Technologies (9 papers) and Advanced Database Systems and Queries (9 papers). Matthias Böehm collaborates with scholars based in Germany, United States and Austria. Matthias Böehm's co-authors include Oliver Thomas, Berthold Reinwald, Arun Kumar, Jun Yang, Shirish Tatikonda, Frederick Reiss, Prithviraj Sen, Alexandre Evfimievski, Niketan Pansare and Peter J. Haas and has published in prestigious journals such as Journal of Cleaner Production, Communications of the ACM and International Journal of Information Management.

In The Last Decade

Matthias Böehm

48 papers receiving 880 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthias Böehm Germany 15 337 278 213 181 160 50 930
Christopher Brooks United States 19 234 0.7× 241 0.9× 262 1.2× 63 0.3× 40 0.3× 50 958
Yanlin Wang China 17 528 1.6× 183 0.7× 665 3.1× 45 0.2× 69 0.4× 74 1.3k
T. B. Richard Singapore 23 250 0.7× 1.2k 4.5× 383 1.8× 104 0.6× 94 0.6× 110 1.9k
Luís Ferreira Pires Netherlands 18 573 1.7× 240 0.9× 715 3.4× 54 0.3× 147 0.9× 151 1.2k
Marco Comuzzi South Korea 19 644 1.9× 636 2.3× 662 3.1× 39 0.2× 81 0.5× 75 1.6k
Jen‐Yao Chung United States 19 328 1.0× 530 1.9× 623 2.9× 33 0.2× 60 0.4× 91 1.2k
Axel Korthaus Germany 14 421 1.2× 187 0.7× 557 2.6× 38 0.2× 40 0.3× 69 1.0k
Luis Pedrosa Portugal 11 173 0.5× 1.0k 3.6× 772 3.6× 68 0.4× 91 0.6× 18 1.3k
Koustuv Dasgupta India 17 377 1.1× 762 2.7× 461 2.2× 170 0.9× 83 0.5× 49 1.6k

Countries citing papers authored by Matthias Böehm

Since Specialization
Citations

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

Fields of papers citing papers by Matthias Böehm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthias Böehm

This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Böehm. A scholar is included among the top collaborators of Matthias Böehm 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 Matthias Böehm. Matthias Böehm 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.
Böehm, Matthias, et al.. (2025). Fast and Scalable Data Transfer Across Data Systems. Proceedings of the ACM on Management of Data. 3(3). 1–28. 1 indexed citations
2.
Balmau, Oana, Matthias Böehm, Ana Klimovic, Peter Pietzuch, & Pınar Tözün. (2025). Resource-Efficient Machine Learning (Dagstuhl Seminar 24311). DROPS (Schloss Dagstuhl – Leibniz Center for Informatics).
3.
Böehm, Matthias, Matteo Interlandi, & Chris Jermaine. (2023). Optimizing Tensor Computations: From Applications to Compilation and Runtime Techniques. 53–59. 1 indexed citations
4.
Böehm, Matthias, Paroma Varma, & Doris Xin. (2022). DEEM'22: Data Management for End-to-End Machine Learning. Proceedings of the 2022 International Conference on Management of Data. 2548–2549.
5.
Böehm, Matthias, et al.. (2021). SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging. 2290–2299. 25 indexed citations
6.
Sen, Prithviraj, Marina Danilevsky, Yunyao Li, et al.. (2020). Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification. 4211–4221. 6 indexed citations
7.
Böehm, Matthias, Arun Kumar, & Jun Yang. (2019). Data Management in Machine Learning Systems. 16 indexed citations
8.
Böehm, Matthias, et al.. (2017). SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning.. Conference on Innovative Data Systems Research. 22 indexed citations
9.
Kumar, Arun, Matthias Böehm, & Jun Yang. (2017). Data Management in Machine Learning. 1717–1722. 85 indexed citations
10.
Elgohary, Ahmed, Matthias Böehm, Peter J. Haas, Frederick Reiss, & Berthold Reinwald. (2017). Compressed linear algebra for large-scale machine learning. The VLDB Journal. 27(5). 719–744. 14 indexed citations
11.
Elgohary, Ahmed, Matthias Böehm, Peter J. Haas, Frederick Reiss, & Berthold Reinwald. (2016). Compressed linear algebra for large-scale machine learning. Proceedings of the VLDB Endowment. 9(12). 960–971. 45 indexed citations
12.
Tatikonda, Shirish, et al.. (2015). On optimizing machine learning workloads via kernel fusion. 173–182. 24 indexed citations
13.
Böehm, Matthias, et al.. (2013). The Further Education Maturity Model: Development and Implementation of a Maturity Model for the Selection of Further Education Offerings in the Field of IT Management and IT Consulting. Americas Conference on Information Systems. 2 indexed citations
14.
Böehm, Matthias, et al.. (2013). Teaching the Chief Information Officers: An Assessment of the Interrelations within their Skill Set. Journal of the Association for Information Systems. 98. 5 indexed citations
15.
Schlegel, Benjamin, et al.. (2012). A high-throughput in-memory index, durable on flash-based SSD: Insights into the winning solution of the SIGMOD programming contest 2011. Qucosa (Saxon State and University Library Dresden). 2 indexed citations
16.
Böehm, Matthias, et al.. (2011). Understanding IT-management and IT-consulting teaching as product-service system: application of an engineering model. EMISA FORUM. 219–224. 1 indexed citations
17.
Böehm, Matthias, et al.. (2011). An Integrated Approach for Teaching Professionals IT Management and IT Consulting. Journal of the Association for Information Systems. 5 indexed citations
18.
Böehm, Matthias, et al.. (2011). TOWARDS AN INTEGRATED APPROACH FOR RESOURCE-EFFICIENCY IN SERVER ROOMS AND DATA CENTERS. Journal of the Association for Information Systems. 240(12). 100–80. 7 indexed citations
19.
Böehm, Matthias, et al.. (2010). Cost-based vectorization of instance-based integration processes. Information Systems. 36(1). 3–29. 7 indexed citations
20.
Böehm, Matthias, et al.. (2009). GCIP. Qucosa (Saxon State and University Library Dresden). 1128–1131. 4 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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