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, Peter Boncz et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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This map shows the geographic impact of Peter Boncz'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 Peter Boncz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Boncz more than expected).
This network shows the impact of papers produced by Peter Boncz. 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 Peter Boncz. The network helps show where Peter Boncz may publish in the future.
Co-authorship network of co-authors of Peter Boncz
This figure shows the co-authorship network connecting the top 25 collaborators of Peter Boncz.
A scholar is included among the top collaborators of Peter Boncz 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 Peter Boncz. Peter Boncz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ghita, Bogdan, et al.. (2020). White-box compression: Learning and exploiting compact table representations. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands.6 indexed citations
6.
Raasveldt, Mark, et al.. (2018). Optimizing group-by and aggregation using GPU-CPU co-processing. Data Archiving and Networked Services (DANS). 1–10.3 indexed citations
7.
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
8.
Boncz, Peter, et al.. (2017). Exploring Query Compilation Strategies for JIT, Vectorization and SIMD.. Very Large Data Bases. 9–17.6 indexed citations
9.
Sidirourgos, Lefteris, Martin Kersten, & Peter Boncz. (2011). SciBORQ: Scientific Data Management with Bounds on Runtime and Quality. UvA-DARE (University of Amsterdam). 296–301.54 indexed citations
10.
Keulen, Maurice van, et al.. (2010). Run-time Optimization for Pipelined Systems.
11.
Żukowski, Marcin, Sándor Héman, Niels Nes, & Peter Boncz. (2007). Cooperative scans: dynamic bandwidth sharing in a DBMS. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 723–734.80 indexed citations
12.
Héman, Sándor, Marcin Żukowski, Arjen P. de Vries, & Peter Boncz. (2006). MonetDB/X100 at the 2006 TREC Terabyte Track.. Text REtrieval Conference.7 indexed citations
13.
Bhoedjang, R.A.F., et al.. (2006). Efficient XQuery Support for Stand-Off Annotation. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands.7 indexed citations
14.
Żukowski, Marcin, Peter Boncz, Niels Nes, & Sándor Héman. (2005). MonetDB/X100 - A DBMS in the CPU cache. Data Archiving and Networked Services (DANS). 28(2). 17–22.79 indexed citations
15.
Boncz, Peter, Torsten Grust, Maurice van Keulen, et al.. (2005). Pathfinder: XQuery---the relational way. University of Twente Research Information. 1322–1325.21 indexed citations
16.
Boncz, Peter. (2003). AmbientDB: P2P Database Technology for Ambient Intelligent Multimedia Applications. Data Archiving and Networked Services (DANS).1 indexed citations
17.
Manegold, Stefan, Peter Boncz, & Martin Kersten. (2000). What Happens During a Join? Dissecting CPU and Memory Optimization Effects. UvA-DARE (University of Amsterdam). 339–350.51 indexed citations
18.
Boncz, Peter, Stefan Manegold, & Martin Kersten. (1999). Database Architecture Optimized for the New Bottleneck: Memory Access. UvA-DARE (University of Amsterdam). 54–65.232 indexed citations
19.
Boncz, Peter, et al.. (1998). The Drill Down Benchmark. UvA-DARE (University of Amsterdam). 628–632.8 indexed citations
20.
Boncz, Peter, et al.. (1995). High performance support for OO traversals in Monet. UvA-DARE (University of Amsterdam). 1–169.9 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.