Sandra de Amo
- Artificial Intelligence top 10%
- Computational Theory and Mathematics top 5%
- Information Systems
- Signal Processing
- Statistical and Nonlinear Physics
- Co-authors
- Walter CarnielliJoão MarcosFabíola S. F. PereiraArnaud GiacomettiJoão GamaGina M. B. OliveiraArnaud SouletNádia Félix Felipe da Silva
- Topics
- Data Management and Algorithms (11 papers)Data Mining Algorithms and Applications (10 papers)Advanced Database Systems and Queries (6 papers)
In The Last Decade
Sandra de Amo
19 papers receiving 193 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 127
- Computational Theory and Mathematics 87
- Information Systems 41
- Signal Processing 35
- Statistical and Nonlinear Physics 28
Countries citing papers authored by Sandra de Amo
This map shows the geographic impact of Sandra de Amo'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 Sandra de Amo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sandra de Amo more than expected).
Fields of papers citing papers by Sandra de Amo
This network shows the impact of papers produced by Sandra de Amo. 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 Sandra de Amo. The network helps show where Sandra de Amo may publish in the future.
Co-authorship network of co-authors of Sandra de Amo
This figure shows the co-authorship network connecting the top 25 collaborators of Sandra de Amo. A scholar is included among the top collaborators of Sandra de Amo 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 Sandra de Amo. Sandra de Amo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 23 | |
| 2 | Improving Pairwise Preference Mining Algorithms Using Preference Degrees | 1 |
| 3 | Detecting Events in Evolving Social Networks through Node Centrality Analysis. | 4 |
| 4 | 20 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 21 | |
| 8 | FPSMining: A Fast Algorithm for Mining User Preferences in Data Streams | 3 |
| 9 | Mining User Contextual Preferences | 5 |
| 10 | 2 | |
| 11 | CPrefMiner: A Bayesian Miner of Conditional Preferences | 0 |
| 12 | Evaluation of Conditional Preference Queries | 6 |
| 13 | 5 | |
| 14 | Constraint-based Tree Pattern Mining | 1 |
| 15 | 19 | |
| 16 | 19 | |
| 17 | 2 | |
| 18 | A Paraconsistent Logic Programming Approach for Querying Inconsistent Knowledge Bases | 1 |
| 19 | 75 | |
| 20 | Mining Generalized Sequential Patterns Using Genetic Programming. | 3 |
About Sandra de Amo
Sandra de Amo is a scholar working on Signal Processing, Information Systems and Computer Networks and Communications, having authored 20 papers that have together received 212 indexed citations. Recurring topics across this work include Data Management and Algorithms (11 papers), Data Mining Algorithms and Applications (10 papers) and Advanced Database Systems and Queries (6 papers). The work is most often cited by research in Computational Theory and Mathematics (87 citations), Artificial Intelligence (127 citations) and Signal Processing (35 citations). Sandra de Amo has collaborated with scholars based in Brazil, Portugal and France. Frequent co-authors include Walter Carnielli, João Marcos, Fabíola S. F. Pereira, Arnaud Giacometti, João Gama, Gina M. B. Oliveira, Arnaud Soulet, Nádia Félix Felipe da Silva, Walter J. Silva and Humberto Razente. Their work appears in journals such as Machine Learning, International Journal of Approximate Reasoning and Information 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.