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.
Locality-sensitive hashing scheme based on p-stable distributions
20041.9k citationsMayur Datar, Piotr Indyk et al.profile →
Models and issues in data stream systems
20021.5k citationsBrian Babcock, Shivnath Babu et al.profile →
Google news personalization
2007958 citationsAbhinandan Das, Mayur Datar et al.profile →
Models and issues in data stream systems
2002531 citationsBrian Babcock, Shivnath Babu et al.profile →
Maintaining Stream Statistics over Sliding Windows
2002503 citationsMayur Datar, Aristides Gionis et al.SIAM Journal on Computingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Mayur Datar'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 Mayur Datar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mayur Datar more than expected).
This network shows the impact of papers produced by Mayur Datar. 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 Mayur Datar. The network helps show where Mayur Datar may publish in the future.
Co-authorship network of co-authors of Mayur Datar
This figure shows the co-authorship network connecting the top 25 collaborators of Mayur Datar.
A scholar is included among the top collaborators of Mayur Datar 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 Mayur Datar. Mayur Datar 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.
Das, Abhinandan, et al.. (2007). Google news personalization. 271–280.958 indexed citations breakdown →
2.
Cui, Hang, Vibhu O. Mittal, & Mayur Datar. (2006). Comparative experiments on sentiment classification for online product reviews. National Conference on Artificial Intelligence. 1265–1270.202 indexed citations
3.
Basu, Parikshit & Mayur Datar. (2004). Financial sector reforms in India: some institutional imbalances.4 indexed citations
4.
Babcock, Brian, Shivnath Babu, Mayur Datar, Rajeev Motwani, & Dilys Thomas. (2004). Operator scheduling in data stream systems. The VLDB Journal. 13(4). 333–353.99 indexed citations
Arasu, Arvind, Brian Babcock, Shivnath Babu, et al.. (2003). STREAM: The Stanford Stream Data Manager.. IEEE Data(base) Engineering Bulletin. 26. 19–26.253 indexed citations
7.
Motwani, Rajeev, Jennifer Widom, Arvind Arasu, et al.. (2003). Query Processing, Approximation, and Resource Management in a Data Stream Management System.. Conference on Innovative Data Systems Research.261 indexed citations
8.
Babcock, Brian, Mayur Datar, & Rajeev Motwani. (2003). Load Shedding Techniques for Data Stream Systems.34 indexed citations
9.
Arasu, Arvind, et al.. (2003). STREAM. 665–665.158 indexed citations
Motwani, Rajeev, Jennifer Widom, Arvind Arasu, et al.. (2002). Query Processing, Resource Management, and Approximation ina Data Stream Management System.222 indexed citations
17.
Datar, Mayur, Aristides Gionis, Piotr Indyk, & Rajeev Motwani. (2002). Maintaining Stream Statistics over Sliding Windows. SIAM Journal on Computing. 31(6). 1794–1813.503 indexed citations breakdown →
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.