Caetano Traina

4.9k citations
260 papers · 2.9k · h-index 25

Impact in

Papers in

Caetano Traina

242 papers receiving 2.7k citations

Peers

Caetano Traina
Comparison fields: 5 of 136
  • Signal Processing 1.2k
  • Computer Vision and Pattern Recognition 1.4k
  • Artificial Intelligence 1.1k
  • Information Systems 539
  • Computer Networks and Communications 523
Replace Agma J. M. Traina with:
Agma J. M. Traina Brazil
Ben Kao Hong Kong
Erin Renshaw United States
Uri Shaft United States
Ira Assent Denmark
Jiong Yang United States
Eui-Hong Han United States
Jin Huang China
Jian Yin China
King-Ip Lin United States
Caetano Traina relative to Agma J. M. Traina Brazil Agma J. M. Traina's profile →
Citations per field
00.5×1.5×
Agma J. M. Traina · 1×
Citations per year

Countries citing papers authored by Caetano Traina

Since Specialization
Citations

This map shows the geographic impact of Caetano Traina'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 Caetano Traina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caetano Traina more than expected).

Fields of papers citing papers by Caetano Traina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caetano Traina. 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 Caetano Traina. The network helps show where Caetano Traina may publish in the future.

Co-authors

The 25 scholars most cited alongside Caetano Traina, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Caetano Traina Line = papers co-authored together Caetano Traina links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 260 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2000183
2 2002136
3 2011131
4 2011124
5 200296
6 201188
7 200384
8 200666
9 201565
10 200063
11 200858
12 202053
13 201940
14 200039
15 200338
16 200733
17 201831
18 200527
19 201827
20 200926

About Caetano Traina

Caetano Traina is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 260 papers that have together received 2.9k indexed citations. Recurring topics across this work include Data Management and Algorithms (109 papers), Advanced Image and Video Retrieval Techniques (89 papers), Image Retrieval and Classification Techniques (85 papers), Advanced Database Systems and Queries (44 papers), Data Mining Algorithms and Applications (38 papers), Algorithms and Data Compression (26 papers), Time Series Analysis and Forecasting (20 papers) and Data Visualization and Analytics (19 papers). The work is most often cited by research in Signal Processing (1.2k citations), Computer Vision and Pattern Recognition (1.4k citations), Artificial Intelligence (1.1k citations), Information Systems (539 citations) and Computer Networks and Communications (523 citations). Caetano Traina has collaborated with scholars based in Brazil, United States and Moldova. Frequent co-authors include Agma J. M. Traina, Christos Faloutsos, Bernhard Seeger, Marcela Xavier Ribeiro, Paulo Mazzoncini de Azevedo‐Marques, Humberto Razente, Marcos R. Vieira, Maria Camila N. Barioni, Joaquim Cezar Felipe and Robson L. F. Cordeiro. Their work appears in journals such as Information Systems, Data & Knowledge Engineering, Computer Methods and Programs in Biomedicine, Computers in Biology and Medicine and Information Sciences.

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.

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