Ron Musick
Impact in
- Signal Processing top 10%
- Data Management and Algorithms
-
- Advanced Database Systems and Queries
- Advanced Data Storage Technologies
- Constraint Satisfaction and Optimization
Papers in
-
- Advanced Database Systems and Queries 11
- Advanced Data Storage Technologies 4
-
- Semantic Web and Ontologies 3
- AI-based Problem Solving and Planning 2
- Co-authors
- Terence Critchlow (9 shared papers)Michael Bowling (2 shared papers)Thore Graepel (2 shared papers)Johannes Fürnkranz (2 shared papers)Stuart Russell (1 shared paper)Krzysztof Fidelis (1 shared paper)Robert R. Snapp (6 shared papers)T. Šlezak (1 shared paper)
- Journals
- Information Sciences (2 papers)Data Mining and Knowledge Discovery (1 paper)ACM SIGMOD Record (1 paper)Machine Learning (1 paper)Journal of the Brazilian Computer Society (1 paper)
- Partner nations
- United StatesGermanyCanada
In The Last Decade
Ron Musick
16 papers receiving 138 citations
Peers
Comparison fields: 5 of 58
- Signal Processing 46
- Computer Networks and Communications 61
- Information Systems and Management 16
- Artificial Intelligence 70
- Software 5
Countries citing papers authored by Ron Musick
This map shows the geographic impact of Ron Musick'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 Ron Musick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ron Musick more than expected).
Fields of papers citing papers by Ron Musick
This network shows the impact of papers produced by Ron Musick. 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 Ron Musick. The network helps show where Ron Musick may publish in the future.
Co-authors
The 15 scholars most cited alongside Ron Musick, 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 | 2006 | 41 | |
| 2 | 1999 | 27 | |
| 3 | 2000 | 26 | |
| 4 | How long will it take | 1992 | 17 |
| 5 | 2001 | 9 | |
| 6 | 2001 | 8 | |
| 7 | 1998 | 7 | |
| 8 | 2002 | 4 | |
| 9 | 2001 | 4 | |
| 10 | 2003 | 4 | |
| 11 | Ad hoc Query Support for Very Large Scientific Data: the Metadata Approach. | 2001 | 2 |
| 12 | Rethinking the learning of belief network probabilities | 1996 | 2 |
| 13 | Ad hoc Query Support for Very Large Simulation Mesh Data: the Metadata Approach | 2001 | 2 |
| 14 | 1998 | 2 | |
| 15 | 2002 | 2 | |
| 16 | Special Issue on Machine Learning and Games | 2006 | 1 |
| 17 | 1997 | 1 |
About Ron Musick
Ron Musick is a scholar working on Computer Networks and Communications, Artificial Intelligence, Signal Processing, Information Systems and Information Systems and Management, having authored 17 papers that have together received 159 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (11 papers), Data Management and Algorithms (5 papers), Advanced Data Storage Technologies (4 papers), Semantic Web and Ontologies (3 papers), Scientific Computing and Data Management (3 papers), AI-based Problem Solving and Planning (2 papers), Graph Theory and Algorithms (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Signal Processing (46 citations), Computer Networks and Communications (61 citations), Information Systems and Management (16 citations), Artificial Intelligence (70 citations) and Software (5 citations). Ron Musick has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Terence Critchlow, Michael Bowling, Thore Graepel, Johannes Fürnkranz, Stuart Russell, Krzysztof Fidelis, Robert R. Snapp, T. Šlezak, Tom Slezak and Paul Stolorz. Their work appears in journals such as Information Sciences, Data Mining and Knowledge Discovery, ACM SIGMOD Record, Machine Learning and Journal of the Brazilian Computer Society.
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.