Sharon C. Salveter
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Signal Processing top 10%
- Information Systems top 10%
- Computational Theory and Mathematics
- Topics
- Semantic Web and Ontologies (8 papers)Data Management and Algorithms (6 papers)Advanced Database Systems and Queries (6 papers)
- Partner nations
- United States
In The Last Decade
Sharon C. Salveter
16 papers receiving 190 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 169
- Computer Networks and Communications 124
- Signal Processing 89
- Information Systems 48
- Computational Theory and Mathematics 21
Countries citing papers authored by Sharon C. Salveter
This map shows the geographic impact of Sharon C. Salveter'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 Sharon C. Salveter with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sharon C. Salveter more than expected).
Fields of papers citing papers by Sharon C. Salveter
This network shows the impact of papers produced by Sharon C. Salveter. 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 Sharon C. Salveter. The network helps show where Sharon C. Salveter may publish in the future.
Co-authorship network of co-authors of Sharon C. Salveter
This figure shows the co-authorship network connecting the top 25 collaborators of Sharon C. Salveter. A scholar is included among the top collaborators of Sharon C. Salveter 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 Sharon C. Salveter. Sharon C. Salveter is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 32 | |
| 2 | Rule Discovery for Query Optimization. | 9 |
| 3 | 14 | |
| 4 | Review of Conceptual structures: information processing in mind and machine by John F. Sowa. Addison-Wesley 1984. | 81 |
| 5 | 1 | |
| 6 | Supporting Natural Language Database Update by Modeling Real World Actions. | 2 |
| 7 | 3 | |
| 8 | Review of The handbook of artificial intelligence, vol. 1 by Avron Barr and Edward A. Feigenbaum. William Kaufman, Inc. 1981. | 5 |
| 9 | 31 | |
| 10 | 7 | |
| 11 | Review of Natural language processing by Harry Tennant. Petrocelli Books 1981. | 1 |
| 12 | 28 | |
| 13 | 2 | |
| 14 | 8 | |
| 15 | 4 | |
| 16 | 4 |
About Sharon C. Salveter
Sharon C. Salveter is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 16 papers that have together received 232 indexed citations. Recurring topics across this work include Semantic Web and Ontologies (8 papers), Data Management and Algorithms (6 papers) and Advanced Database Systems and Queries (6 papers). The work is most often cited by research in Signal Processing (89 citations), Computer Networks and Communications (124 citations) and Artificial Intelligence (169 citations). Sharon C. Salveter has collaborated with scholars based in United States. Frequent co-authors include David Maier, Edward Sciore, Michael Siegel, Jacob Stein and David S. Warren. Their work appears in journals such as Cognitive Science, ACM Transactions on Database Systems and Computational Linguistics.
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.