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.
Benchmarking cloud serving systems with YCSB
20102.4k citationsRaghu Ramakrishnan et al.profile →
Crowdsourcing systems on the World-Wide Web
2011870 citationsAnHai Doan, Raghu Ramakrishnan et al.profile →
Incognito
2005676 citationsKristen LeFevre, Raghu Ramakrishnan et al.profile →
Big data and its technical challenges
2014634 citationsRaghu Ramakrishnan et al.profile →
PNUTS
2008607 citationsRaghu Ramakrishnan et al.profile →
Citations per year, relative to Raghu Ramakrishnan Raghu Ramakrishnan (= 1×)
peers
Gerhard Weikum
Countries citing papers authored by Raghu Ramakrishnan
Since
Specialization
Citations
This map shows the geographic impact of Raghu Ramakrishnan'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 Raghu Ramakrishnan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Raghu Ramakrishnan more than expected).
Fields of papers citing papers by Raghu Ramakrishnan
This network shows the impact of papers produced by Raghu Ramakrishnan. 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 Raghu Ramakrishnan. The network helps show where Raghu Ramakrishnan may publish in the future.
Co-authorship network of co-authors of Raghu Ramakrishnan
This figure shows the co-authorship network connecting the top 25 collaborators of Raghu Ramakrishnan.
A scholar is included among the top collaborators of Raghu Ramakrishnan 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 Raghu Ramakrishnan. Raghu Ramakrishnan 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.
Curino, Carlo, Subru Krishnan, Konstantinos Karanasos, et al.. (2019). Hydra: a federated resource manager for data-center scale analytics. Networked Systems Design and Implementation. 177–192.20 indexed citations
2.
Agarwal, Deepak, et al.. (2008). Online Models for Content Optimization. Neural Information Processing Systems. 21. 17–24.71 indexed citations
3.
Chen, Bee-Chung, Kristen LeFevre, & Raghu Ramakrishnan. (2007). Privacy skyline: privacy with multidimensional adversarial knowledge. Minds at UW (University of Wisconsin). 770–781.97 indexed citations
4.
Shen, Warren, AnHai Doan, Jeffrey F. Naughton, & Raghu Ramakrishnan. (2007). Declarative information extraction using datalog with embedded extraction predicates. Very Large Data Bases. 1033–1044.117 indexed citations
5.
DeRose, Pedro, Warren Shen, Fei Chen, AnHai Doan, & Raghu Ramakrishnan. (2007). Building structured web community portals: a top-down, compositional, and incremental approach. Very Large Data Bases. 399–410.52 indexed citations
6.
Doan, AnHai, Raghu Ramakrishnan, Fei Chen, et al.. (2006). Community Information Management.. IEEE Data(base) Engineering Bulletin. 29. 64–72.63 indexed citations
7.
Chaudhuri, Surajit, et al.. (2005). Integrating DB and IR Technologies: What is the Sound of One Hand Clapping?. Max Planck Institute for Plasma Physics. 1–12.66 indexed citations
8.
Guo, Hongfei, Per-Åke Larson, & Raghu Ramakrishnan. (2005). Caching with good enough currency, consistency, and completeness. Minds at UW (University of Wisconsin). 457–468.18 indexed citations
9.
Ganti, Venkatesh, Mong Li Lee, & Raghu Ramakrishnan. (2000). ICICLES: Self-Tuning Samples for Approximate Query Answering. Very Large Data Bases. 176–187.75 indexed citations
10.
Agarwal, Rakesh, et al.. (1999). On the computation of multidimensional aggregates. MIT Press eBooks. 361–386.40 indexed citations
Ramakrishnan, Raghu & Divesh Srivastava. (1994). Fault Tolerance Issues in Data Declustering for Parallel Database Systems.. IEEE Data(base) Engineering Bulletin. 17. 14–17.5 indexed citations
13.
Ramakrishnan, Raghu, Kenneth A. Ross, Divesh Srivastava, & S. Sudarshan. (1994). Efficient incremental evaluation of queries with aggregation. International Conference on Logic Programming. 204–218.30 indexed citations
14.
Sudarshan, S., Divesh Srivastava, Raghu Ramakrishnan, & Catriel Beeri. (1993). Extending the well-founded and valid semantics for aggregation. International Conference on Logic Programming. 590–608.13 indexed citations
15.
Ramakrishnan, Raghu, et al.. (1993). MIMSY: A System for Analyzing Time Series Data in the Stock Market Domain.. 33–43.12 indexed citations
16.
Srivastava, Divesh, Raghu Ramakrishnan, Praveen Seshadri, & S. Sudarshan. (1993). Coral++: Adding Object-Orientation to a Logic Database Language. Very Large Data Bases. 158–170.30 indexed citations
17.
Ramakrishnan, Raghu, Divesh Srivastava, & S. Sudarshan. (1992). CORAL - Control, Relations and Logic. Very Large Data Bases. 238–250.83 indexed citations
18.
Ramakrishnan, Raghu, Divesh Srivastava, & S. Sudarshan. (1992). Controlling the Search in Bottom-Up Evaluation.. 273–287.24 indexed citations
19.
Sudarshan, S. & Raghu Ramakrishnan. (1991). Aggregation and Relevance in Deductive Databases. Very Large Data Bases. 501–511.51 indexed citations
20.
Ramakrishnan, Raghu & Abraham Silberschatz. (1985). The MR diagram: a model for conceptual database design. Very Large Data Bases. 376–393.1 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.