Todd Eavis
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
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- Advanced Database Systems and Queries
- Advanced Data Storage Technologies
- Peer-to-Peer Network Technologies
- Distributed and Parallel Computing Systems
Papers in
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- Data Management and Algorithms 31
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- Advanced Database Systems and Queries 33
- Advanced Data Storage Technologies 10
- Peer-to-Peer Network Technologies 3
- Co-authors
- Andrew Rau‐Chaplin (17 shared papers)Frank Dehne (11 shared papers)Fabian Gregor Dehne (6 shared papers)Ying Chen (5 shared papers)Susanne E. Hambrusch (1 shared paper)Nathalie Japkowicz (1 shared paper)Xi Zheng (1 shared paper)Elankayer Sithirasenan (1 shared paper)
- Journals
- International Journal of Data Warehousing and Mining (3 papers)Distributed and Parallel Databases (3 papers)Lecture notes in computer science (12 papers)Data Science Journal (1 paper)Future Generation Computer Systems (1 paper)
- Partner nations
- CanadaSaudi ArabiaUnited States
In The Last Decade
Todd Eavis
35 papers receiving 340 citations
Peers
Comparison fields: 5 of 39
- Signal Processing 229
- Computer Networks and Communications 309
- Information Systems 167
- Artificial Intelligence 78
- Computational Mathematics 1
Countries citing papers authored by Todd Eavis
This map shows the geographic impact of Todd Eavis'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 Todd Eavis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Todd Eavis more than expected).
Fields of papers citing papers by Todd Eavis
This network shows the impact of papers produced by Todd Eavis. 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 Todd Eavis. The network helps show where Todd Eavis may publish in the future.
Co-authors
The 14 scholars most cited alongside Todd Eavis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 36 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 41 | |
| 2 | 2006 | 32 | |
| 3 | 2001 | 31 | |
| 4 | 2003 | 26 | |
| 5 | 2002 | 19 | |
| 6 | 2007 | 15 | |
| 7 | 2007 | 14 | |
| 8 | 2000 | 13 | |
| 9 | 2001 | 13 | |
| 10 | 2001 | 12 | |
| 11 | 2008 | 12 | |
| 12 | 2008 | 11 | |
| 13 | 2007 | 11 | |
| 14 | 2006 | 11 | |
| 15 | 2004 | 11 | |
| 16 | 2009 | 9 | |
| 17 | 2005 | 8 | |
| 18 | 2006 | 8 | |
| 19 | 2007 | 7 | |
| 20 | 2008 | 7 |
About Todd Eavis
Todd Eavis is a scholar working on Signal Processing, Computer Networks and Communications, Information Systems, Artificial Intelligence and Computational Theory and Mathematics, having authored 36 papers that have together received 361 indexed citations. Recurring topics across this work include Advanced Database Systems and Queries (33 papers), Data Management and Algorithms (31 papers), Advanced Data Storage Technologies (10 papers), Data Mining Algorithms and Applications (10 papers), Cloud Computing and Resource Management (5 papers), Algorithms and Data Compression (4 papers), Semantic Web and Ontologies (4 papers) and Peer-to-Peer Network Technologies (3 papers). The work is most often cited by research in Signal Processing (229 citations), Computer Networks and Communications (309 citations), Information Systems (167 citations), Artificial Intelligence (78 citations) and Computational Mathematics (1 citation). Todd Eavis has collaborated with scholars based in Canada, Saudi Arabia and United States. Frequent co-authors include Andrew Rau‐Chaplin, Frank Dehne, Fabian Gregor Dehne, Ying Chen, Susanne E. Hambrusch, Nathalie Japkowicz, Xi Zheng, Elankayer Sithirasenan, Anisoara Nica and Nasser Taleb. Their work appears in journals such as International Journal of Data Warehousing and Mining, Distributed and Parallel Databases, Lecture notes in computer science, Data Science Journal and Future Generation Computer Systems.
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.