Dana Nau
- Artificial Intelligence top 2%
- Computer Networks and Communications top 10%
- Information Systems top 5%
- Computer Vision and Pattern Recognition top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Malik GhallabPaolo TraversoV. S. SubrahmanianFusun YamanPatrick RoosAmbuj MahantiJames HendlerSubrata Ghosh
- Topics
- Logic, Reasoning, and Knowledge (4 papers)Data Management and Algorithms (4 papers)Semantic Web and Ontologies (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Knowledge and Data EngineeringThe American Surgeon
- Partner nations
- United StatesIndiaItaly
In The Last Decade
Dana Nau
12 papers receiving 649 citations
Hit Papers
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 525
- Computer Networks and Communications 153
- Information Systems 118
- Computer Vision and Pattern Recognition 113
- Computational Theory and Mathematics 74
Countries citing papers authored by Dana Nau
This map shows the geographic impact of Dana Nau'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 Dana Nau with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dana Nau more than expected).
Fields of papers citing papers by Dana Nau
This network shows the impact of papers produced by Dana Nau. 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 Dana Nau. The network helps show where Dana Nau may publish in the future.
Co-authorship network of co-authors of Dana Nau
This figure shows the co-authorship network connecting the top 25 collaborators of Dana Nau. A scholar is included among the top collaborators of Dana Nau 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 Dana Nau. Dana Nau is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 7 | |
| 3 | 6 | |
| 4 | 16 | |
| 5 | A motion closed world asumption | 4 |
| 6 | Going far, logically | 7 |
| 7 | A logic of motion | 7 |
| 8 | Automated Planning: Theory & Practicebreakdown → | 579 |
| 9 | 31 | |
| 10 | An application of distributed solid modeling: feature recognition | 2 |
| 11 | ITS: an efficient limited-memory heuristic tree search algorithm | 12 |
| 12 | Optimization of multiple-goal plans with limited interaction | 13 |
| 13 | Hierarchical knowledge clustering: a way to represent and use problem-solving knowledge | 7 |
About Dana Nau
Dana Nau is a scholar working on Signal Processing, Artificial Intelligence and Computer Networks and Communications, having authored 13 papers that have together received 692 indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (4 papers), Data Management and Algorithms (4 papers) and Semantic Web and Ontologies (3 papers). The work is most often cited by research in Artificial Intelligence (525 citations), Software (48 citations) and Computer Networks and Communications (153 citations). Dana Nau has collaborated with scholars based in United States, India and Italy. Frequent co-authors include Malik Ghallab, Paolo Traverso, V. S. Subrahmanian, Fusun Yaman, Patrick Roos, Ambuj Mahanti, James Hendler, Subrata Ghosh, Qiang Yang and Amy Sliva. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Knowledge and Data Engineering and The American Surgeon.
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.