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.
How good are query optimizers, really?
2015382 citationsViktor Leis, Andrey Gubichev et al.Proceedings of the VLDB Endowmentprofile →
The adaptive radix tree: ARTful indexing for main-memory databases
2013259 citationsViktor Leis, Alfons Kemper et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Viktor Leis'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 Viktor Leis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Viktor Leis more than expected).
This network shows the impact of papers produced by Viktor Leis. 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 Viktor Leis. The network helps show where Viktor Leis may publish in the future.
Co-authorship network of co-authors of Viktor Leis
This figure shows the co-authorship network connecting the top 25 collaborators of Viktor Leis.
A scholar is included among the top collaborators of Viktor Leis 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 Viktor Leis. Viktor Leis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Renen, Alexander van, Lukas Vogel, Viktor Leis, Thomas Neumann, & Alfons Kemper. (2020). Building blocks for persistent memory. The VLDB Journal. 29(6). 1223–1241.21 indexed citations
10.
Haas, Gabriel P., et al.. (2020). Exploiting Directly-Attached NVMe Arrays in DBMS.. Conference on Innovative Data Systems Research.5 indexed citations
11.
Leis, Viktor, et al.. (2019). Optimistic Lock Coupling: A Scalable and Efficient General-Purpose Synchronization Method.. IEEE Data(base) Engineering Bulletin. 42. 73–84.31 indexed citations
12.
Kipf, Andreas, Thomas Kipf, Bernhard Radke, et al.. (2018). Learned Cardinalities: Estimating Correlated Joins with Deep Learning. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands.14 indexed citations
13.
Leis, Viktor, Bernhard Radke, Andrey Gubichev, Alfons Kemper, & Thomas Neumann. (2017). Cardinality Estimation Done Right: Index-Based Join Sampling.. Conference on Innovative Data Systems Research.46 indexed citations
14.
Neumann, Thomas, Viktor Leis, & Alfons Kemper. (2017). The Complete Story of Joins (in HyPer).. BTW. 31–50.7 indexed citations
15.
Leis, Viktor. (2016). Query Processing and Optimization in Modern Database Systems. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 507–516.3 indexed citations
16.
Neumann, Thomas & Viktor Leis. (2014). Compiling Database Queries into Machine Code.. IEEE Data(base) Engineering Bulletin. 37. 3–11.34 indexed citations
Kemper, Alfons, et al.. (2012). HyPer: Adapting Columnar Main-Memory Data Management for Transactional AND Query Processing.. IEEE Data(base) Engineering Bulletin. 35. 46–51.11 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.