James H. Griesmer
- Artificial Intelligence top 5%
- Logic, programming, and type systems 5
- Logic, Reasoning, and Knowledge 3
- Semantic Web and Ontologies 3
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- Auction Theory and Applications 3
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- Numerical Methods and Algorithms 3
- Polynomial and algebraic computation 2
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- Parallel Computing and Optimization Techniques 3
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- Merger and Competition Analysis 2
- Co-authors
- Richard D. JenksMartín ShubikChidanand AptéEric MaysSe June HongM. KarnaughDavid Y. Y. YunChid Apte
- Cited by
- Discrete Mathematics and CombinatoricsArtificial IntelligenceComputer Networks and Communications
- Journals
- Science (1 paper)IBM Journal of Research and Development (2 papers)ACM SIGPLAN Notices (1 paper)
- Partner nations
- United States
In The Last Decade
James H. Griesmer
19 papers receiving 388 citations
Peers
Comparison fields: 5 of 54
- Discrete Mathematics and Combinatorics 80
- Artificial Intelligence 324
- Computer Networks and Communications 146
- Management Science and Operations Research 63
- Computational Theory and Mathematics 72
Countries citing papers authored by James H. Griesmer
This map shows the geographic impact of James H. Griesmer'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 James H. Griesmer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James H. Griesmer more than expected).
Fields of papers citing papers by James H. Griesmer
This network shows the impact of papers produced by James H. Griesmer. 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 James H. Griesmer. The network helps show where James H. Griesmer may publish in the future.
Co-authorship network
The 10 scholars most cited alongside James H. Griesmer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2003 | 5 | |
| 2 | Experiences with object centered modeling of financial marketing | 1993 | 0 |
| 3 | How to achieve FAME | 1992 | 1 |
| 4 | 1992 | 1 | |
| 5 | 1987 | 30 | |
| 6 | 1986 | 24 | |
| 7 | Automation of MVS Operations, an Expert Systems Approach. | 1984 | 2 |
| 8 | 1976 | 5 | |
| 9 | 1975 | 6 | |
| 10 | 1975 | 2 | |
| 11 | 1975 | 3 | |
| 12 | 1972 | 6 | |
| 13 | 1972 | 2 | |
| 14 | 1971 | 28 | |
| 15 | 1970 | 9 | |
| 16 | 1966 | 6 | |
| 17 | 1963 | 16 | |
| 18 | 1963 | 16 | |
| 19 | 1963 | 21 | |
| 20 | 1960 | 261 |
About James H. Griesmer
James H. Griesmer is a scholar working on Hardware and Architecture, Computational Theory and Mathematics and Artificial Intelligence, having authored 20 papers that have together received 444 indexed citations. Recurring topics across this work include Logic, programming, and type systems (5 papers), Logic, Reasoning, and Knowledge (3 papers), Semantic Web and Ontologies (3 papers), Parallel Computing and Optimization Techniques (3 papers), Numerical Methods and Algorithms (3 papers), Auction Theory and Applications (3 papers), Polynomial and algebraic computation (2 papers) and Merger and Competition Analysis (2 papers). The work is most often cited by research in Discrete Mathematics and Combinatorics (80 citations), Artificial Intelligence (324 citations) and Computer Networks and Communications (146 citations). James H. Griesmer has collaborated with scholars based in United States. Frequent co-authors include Richard D. Jenks, Martín Shubik, Chidanand Apté, Eric Mays, Se June Hong, M. Karnaugh, David Y. Y. Yun, Chid Apte, David Klein and Keith R. Milliken. Their work appears in journals such as Science, IBM Journal of Research and Development and ACM SIGPLAN Notices.
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.