Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
2020290 citationsSara Comai, Matteo Matteucci et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Sara Comai'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 Sara Comai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sara Comai more than expected).
This network shows the impact of papers produced by Sara Comai. 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 Sara Comai. The network helps show where Sara Comai may publish in the future.
Co-authorship network of co-authors of Sara Comai
This figure shows the co-authorship network connecting the top 25 collaborators of Sara Comai.
A scholar is included among the top collaborators of Sara Comai 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 Sara Comai. Sara Comai is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Comai, Sara, et al.. (2018). Disseminating Synthetic Smart Home Data for Advanced Applications.. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2482. 1–7.3 indexed citations
Vallecillo, Antonio, Nora Koch, Cristina Cachero, et al.. (2007). MDWEnet: A Practical Approach to Achieving Interoperability of Model-Driven Web Engineering Methods. RUA, Repositorio Institucional de la Universidad de Alicante (Universidad de Alicante). 261. 1–10.16 indexed citations
12.
Comai, Sara & Daniele Mazza. (2007). Automatic Display Layout in WebML: a Web Engineering Approach. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 149–163.1 indexed citations
13.
Brambilla, Marco, Jordi Cabot, & Sara Comai. (2007). Generating Extended Conceptual Schemas from Business Process Models. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 282–289.2 indexed citations
Manolescu, Ioana, Stefano Ceri, Marco Brambilla, Piero Fraternali, & Sara Comai. (2003). Exploiting the Combined Potential of Web Applications and Web Services..3 indexed citations
17.
Brambilla, Marco, Stefano Ceri, Sara Comai, Piero Fraternali, & Ioana Manolescu. (2002). Specification and Design of Workflow-driven Hypertexts.. Journal of Web Engineering. 1(2). 163–182.34 indexed citations
18.
Brambilla, Marco, Stefano Ceri, Sara Comai, Piero Fraternali, & Ioana Manolescu. (2002). Model-driven Specification of Web Services Composition and Integration with Data-intensive Web Applications. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 25(4). 53–59.10 indexed citations
19.
Comai, Sara & Piero Fraternali. (2001). A semantic model for specifying data-intensive Web applications using WebML. International Semantic Web Conference. 566–585.8 indexed citations
20.
Ceri, Stefano, Sara Comai, Piero Fraternali, et al.. (1999). XML-GL: A Graphical Language for Querying and Restructuring XML Documents.. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 151–165.18 indexed citations
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.