Rosa Meo

1.6k citations
51 papers · 717 indexed · h-index 13
Topics
Data Mining Algorithms and Applications (19 papers)Rough Sets and Fuzzy Logic (11 papers)Data Management and Algorithms (10 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE Transactions on Information TheorySensors
Partner nations
ItalyFranceGermany

In The Last Decade

Rosa Meo

48 papers receiving 650 citations

Peers

Rosa Meo
Comparison fields: 5 of 103
  • Information Systems 340
  • Artificial Intelligence 322
  • Signal Processing 205
  • Computer Networks and Communications 181
  • Computational Theory and Mathematics 156
Replace Xiangjun Dong with:
Xiangjun Dong China
Andrea Vattani United States
Doo-Soon Park South Korea
Stephen D. Bay United States
Yun Sing Koh New Zealand
Mi Zhang China
Atri Rudra United States
Ran Wolff Israel
Bahman Bahmani United States
Rosa Meo relative to Xiangjun Dong China Xiangjun Dong's profile →
Citations per field
00.5×1.5×
Xiangjun Dong · 1×
Citations per year

Countries citing papers authored by Rosa Meo

Since Specialization
Citations

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

Fields of papers citing papers by Rosa Meo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rosa Meo

This figure shows the co-authorship network connecting the top 25 collaborators of Rosa Meo. A scholar is included among the top collaborators of Rosa Meo 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 Rosa Meo. Rosa Meo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 1
3 1
4 12
5 3
6 1
7 1
8 1
9 3
10 1
11 3
12 7
13 1
14
Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 8724
1
15 8
16 2
17
Database Support for Data Mining Applications: Discovering Knowledge with Inductive Queries (Lecture Notes in Computer Science)
3
18
Integrating web conceptual modeling and web usage mining
0
19 8
20
A New SQL-like Operator for Mining Association Rules
155

About Rosa Meo

Rosa Meo is a scholar working on Horticulture, Signal Processing and Information Systems, having authored 51 papers that have together received 717 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (19 papers), Rough Sets and Fuzzy Logic (11 papers) and Data Management and Algorithms (10 papers). The work is most often cited by research in Signal Processing (205 citations), Information Systems (340 citations) and Artificial Intelligence (322 citations). Rosa Meo has collaborated with scholars based in Italy, France and Germany. Frequent co-authors include Giuseppe Psaila, Stefano Ceri, Dino Ienco, Ruggero G. Pensa, Toon Calders, Floriana Esposito, Eyke Hüllermeier, Emilio Sulis, Mika Klemettinen and C. Simoné. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Information Theory and Sensors.

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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