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.
CrowdDB
2011433 citationsMichael J. Franklin, Tim Kraska et al.profile →
CrowdER
2012344 citationsJiannan Wang, Tim Kraska et al.Proceedings of the VLDB Endowmentprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Tim Kraska'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 Tim Kraska with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Kraska more than expected).
This network shows the impact of papers produced by Tim Kraska. 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 Tim Kraska. The network helps show where Tim Kraska may publish in the future.
Co-authorship network of co-authors of Tim Kraska
This figure shows the co-authorship network connecting the top 25 collaborators of Tim Kraska.
A scholar is included among the top collaborators of Tim Kraska 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 Tim Kraska. Tim Kraska is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mao, Hongzi, Parimarjan Negi, Akshay Narayan, et al.. (2019). Park: An Open Platform for Learning-Augmented Computer Systems. DSpace@MIT (Massachusetts Institute of Technology). 32. 2490–2502.39 indexed citations
9.
Kraska, Tim, Mohammad Alizadeh, Alex Beutel, et al.. (2019). SageDB: A Learned Database System. DSpace@MIT (Massachusetts Institute of Technology).70 indexed citations
10.
Zgraggen, Emanuel, et al.. (2018). Towards Quantifying Uncertainty in Data Analysis & Exploration.. IEEE Data(base) Engineering Bulletin. 41. 15–26.10 indexed citations
11.
Kraska, Tim, et al.. (2018). Slice Finder: Automated Data Sclicing for Model Validation. arXiv (Cornell University).3 indexed citations
12.
Binnig, Carsten, et al.. (2017). Rethinking Distributed Query Execution on High-Speed Networks.. ZBW Publication Archive (ZBW – Leibniz Information Centre for Economics). 40. 27–37.14 indexed citations
13.
Pinkel, Christoph, Carsten Binnig, Ernesto Jiménez-Ruiz, et al.. (2017). IncMap: A Journey towards Ontology-based Data Integration. City Research Online (City University London). 145–164.6 indexed citations
14.
Binnig, Carsten, Lorenzo De Stefani, Tim Kraska, et al.. (2017). Toward Sustainable Insights, or Why Polygamy is Bad for You.. Conference on Innovative Data Systems Research.17 indexed citations
15.
Kraska, Tim, et al.. (2017). Spotlytics: How to Use Cloud Market Places for Analytics?. TUbilio (Technical University of Darmstadt). 361–380.1 indexed citations
16.
Zgraggen, Emanuel, et al.. (2016). Towards a Benchmark for Interactive Data Exploration.. IEEE Data(base) Engineering Bulletin. 39. 50–61.13 indexed citations
Krishnan, Sanjay, Jiannan Wang, Michael J. Franklin, et al.. (2015). SampleClean: Fast and Reliable Analytics on Dirty Data.. IEEE Data(base) Engineering Bulletin. 38. 59–75.28 indexed citations
19.
Demartini, Gianluca, Beth Trushkowsky, Tim Kraska, & Michael J. Franklin. (2013). CrowdQ: Crowdsourced Query Understanding. Queensland's institutional digital repository (The University of Queensland).23 indexed citations
20.
Schäffner, Jan, Dean Jacobs, Tim Kraska, & Hasso Plattner. (2012). The Multi-Tenant Data Placement Problem. 157–162.5 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.