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.
WhereNext
2009400 citationsAnna Monreale, Fabio Pinelli et al.CINECA IRIS Institutial research information system (University of Pisa)profile →
Citations per year, relative to Roberto Trasarti Roberto Trasarti (= 1×)
peers
Bart Kuijpers
Countries citing papers authored by Roberto Trasarti
Since
Specialization
Citations
This map shows the geographic impact of Roberto Trasarti'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 Roberto Trasarti with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roberto Trasarti more than expected).
Fields of papers citing papers by Roberto Trasarti
This network shows the impact of papers produced by Roberto Trasarti. 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 Roberto Trasarti. The network helps show where Roberto Trasarti may publish in the future.
Co-authorship network of co-authors of Roberto Trasarti
This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Trasarti.
A scholar is included among the top collaborators of Roberto Trasarti 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 Roberto Trasarti. Roberto Trasarti is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Andrienko, Gennady, Natalia Andrienko, Chiara Boldrini, et al.. (2020). (So) Big Data and the transformation of the city. International Journal of Data Science and Analytics. 11(4). 311–340.19 indexed citations
Giannotti, Fosca, et al.. (2018). SoBigData. ISTI Open Portal. 437–438.6 indexed citations
8.
Guidotti, Riccardo, Roberto Trasarti, Mirco Nanni, & Fosca Giannotti. (2015). Towards user-centric data management. CINECA IRIS Institutial research information system (University of Pisa). 80–83.1 indexed citations
9.
Gabrielli, Lorenzo, Barbara Furletti, Roberto Trasarti, Fosca Giannotti, & Dino Pedreschi. (2015). City users' classification with mobile phone data. CINECA IRIS Institutial research information system (University of Pisa).21 indexed citations
10.
Trasarti, Roberto, Barbara Furletti, Lorenzo Gabrielli, Mirco Nanni, & Dino Pedreschi. (2014). Big data analytics for smart mobility: a case study. CINECA IRIS Institutial research information system (University of Pisa). 363–364.2 indexed citations
Monreale, Anna, Roberto Trasarti, Dino Pedreschi, Chiara Renso, & Vânia Bogorny. (2011). C-safety: a framework for the anonymization of semantic trajectories. CINECA IRIS Institutial research information system (University of Pisa). 4(2). 73–101.29 indexed citations
13.
Trasarti, Roberto, Fosca Giannotti, Mirco Nanni, Dino Pedreschi, & Chiara Renso. (2011). A Query Language for Mobility Data Mining. International Journal of Data Warehousing and Mining. 7(1). 24–45.17 indexed citations
Ortale, Riccardo, Ettore Ritacco, Nikos Pelekis, et al.. (2008). The DAEDALUS framework. ISTI Open Portal. 1–4.14 indexed citations
20.
Lucchese, Claudio, Francesco Bonchi, Fosca Giannotti, et al.. (2006). On Interactive Pattern Mining from Relational Databases.. SEBD. 329–338.1 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.