John Gaschnig
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
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- Constraint Satisfaction and Optimization
- Advanced Database Systems and Queries
Papers in
-
- Geochemistry and Geologic Mapping 3
- Semantic Web and Ontologies 2
- Artificial Intelligence in Games 1
- Machine Learning and Algorithms 1
- AI-based Problem Solving and Planning 1
- Co-authors
- Samuel H. Fuller (1 shared paper)
- Journals
- Figshare (1 paper)International Joint Conference on Artificial Intelligence (3 papers)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
John Gaschnig
5 papers receiving 266 citations
Peers
Comparison fields: 5 of 40
- Signal Processing 108
- Computer Networks and Communications 199
- Artificial Intelligence 178
- Software 18
- Management Science and Operations Research 38
Countries citing papers authored by John Gaschnig
This map shows the geographic impact of John Gaschnig'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 John Gaschnig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Gaschnig more than expected).
Fields of papers citing papers by John Gaschnig
This network shows the impact of papers produced by John Gaschnig. 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 John Gaschnig. The network helps show where John Gaschnig may publish in the future.
Co-authors
The 1 scholars most cited alongside John Gaschnig, 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 | Performance measurement and analysis of certain search algorithms. | 1979 | 214 |
| 2 | A general backtrack algorithm that eliminates most redundant tests | 1977 | 55 |
| 3 | Preliminary performance analysis of the prospector consultant system for mineral exploration | 1979 | 14 |
| 4 | Exactly how good are heuristics?: toward a realistic predictive theory of best-first search | 1977 | 13 |
| 5 | 2018 | 4 | |
| 6 | An application of the prospector system to doe's national uranium resource evaluation | 1980 | 1 |
| 7 | Application of the PROSPECTOR system to geological exploration problernst | 1980 | 1 |
About John Gaschnig
John Gaschnig is a scholar working on Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science, Signal Processing and Environmental Engineering, having authored 7 papers that have together received 302 indexed citations. Recurring topics across this work include Geochemistry and Geologic Mapping (3 papers), Semantic Web and Ontologies (2 papers), Artificial Intelligence in Games (1 paper), Machine Learning and Algorithms (1 paper), Digital Games and Media (1 paper), AI-based Problem Solving and Planning (1 paper), Software Testing and Debugging Techniques (1 paper) and Data Management and Algorithms (1 paper). The work is most often cited by research in Signal Processing (108 citations), Computer Networks and Communications (199 citations), Artificial Intelligence (178 citations), Software (18 citations) and Management Science and Operations Research (38 citations). John Gaschnig has collaborated with scholars based in United States. Frequent co-authors include Samuel H. Fuller. Their work appears in journals such as Figshare, International Joint Conference on Artificial Intelligence and National Conference on Artificial Intelligence.
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.