Jeff Ullman

1.1k citations
11 papers · 540 indexed · h-index 8
Topics
Advanced Database Systems and Queries (4 papers)semigroups and automata theory (2 papers)Bayesian Modeling and Causal Inference (2 papers)

In The Last Decade

Jeff Ullman

11 papers receiving 459 citations

Peers

Jeff Ullman
Comparison fields: 5 of 53
  • Computer Networks and Communications 317
  • Artificial Intelligence 260
  • Information Systems 228
  • Signal Processing 173
  • Computational Theory and Mathematics 99
Replace S.Y.W. Su with:
S.Y.W. Su United States
Jiefeng Cheng Hong Kong
Jeff Naughton United States
Georges Gardarin France
David Toman Canada
Ibrahim Abdelaziz Saudi Arabia
Marian Nodine United States
Upen S. Chakravarthy United States
Myong Kang United States
Dennis R. McCarthy United States
Jeff Ullman relative to S.Y.W. Su United States S.Y.W. Su's profile →
Citations per field
00.5×3.7×
S.Y.W. Su · 1×
Citations per year

Countries citing papers authored by Jeff Ullman

Since Specialization
Citations

This map shows the geographic impact of Jeff Ullman'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 Jeff Ullman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Ullman more than expected).

Fields of papers citing papers by Jeff Ullman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeff Ullman. 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 Jeff Ullman. The network helps show where Jeff Ullman may publish in the future.

Co-authorship network of co-authors of Jeff Ullman

This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Ullman. A scholar is included among the top collaborators of Jeff Ullman 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 Jeff Ullman. Jeff Ullman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
#WorkIndexed citations
1 1
2
Data Mining Research: Opportunities and Challenges
8
3 4
4 139
5 138
6 33
7 5
8 59
9 121
10 15
11 17

About Jeff Ullman

Jeff Ullman is a scholar working on Computational Theory and Mathematics, Computer Networks and Communications and Information Systems, having authored 11 papers that have together received 540 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (4 papers), semigroups and automata theory (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Signal Processing (173 citations), Computer Networks and Communications (317 citations) and Information Systems (228 citations). Jeff Ullman has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Avi Silberschatz, Michael Stonebraker, Craig Silverstein, Rajeev Motwani, Sergey Brin, Mike Stonebraker, Jim Gray, Stefano Ceri, Michael Lesk and David J. DeWitt. Their work appears in journals such as Communications of the ACM, Computers & Mathematics with Applications and ACM SIGMOD Record.

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.

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