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.
Augmented reality technologies, systems and applications
2010950 citationsPaolo Ceravolo, Ernesto Damiani et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Paolo Ceravolo
Since
Specialization
Citations
This map shows the geographic impact of Paolo Ceravolo'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 Paolo Ceravolo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paolo Ceravolo more than expected).
This network shows the impact of papers produced by Paolo Ceravolo. 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 Paolo Ceravolo. The network helps show where Paolo Ceravolo may publish in the future.
Co-authorship network of co-authors of Paolo Ceravolo
This figure shows the co-authorship network connecting the top 25 collaborators of Paolo Ceravolo.
A scholar is included among the top collaborators of Paolo Ceravolo 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 Paolo Ceravolo. Paolo Ceravolo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sanna, Alberto, et al.. (2021). IoT Platform for Ageing Society: the SMART BEAR Project. 45–49.2 indexed citations
9.
Varela‐Vaca, Ángel Jesús, et al.. (2019). CHAMALEON: Framework to improve Data Wrangling with Complex Data. Journal of the Association for Information Systems.6 indexed citations
Ceravolo, Paolo, Maurice van Keulen, & Kilian Stoffel. (2017). Proceedings of the 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017). Data Archiving and Networked Services (DANS). 2016.7 indexed citations
12.
Ceravolo, Paolo, et al.. (2015). De-Materializing Local Public Administration Processes.. 311–312.3 indexed citations
13.
Azzini, Antonia, et al.. (2015). Knowledge Driven Behavioural Analysis in Process Intelligence.. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 97–111.1 indexed citations
14.
Seeber, Isabella, Ronald Maier, Paolo Ceravolo, & Fulvio Frati. (2014). TRACING THE DEVELOPMENT OF IDEAS IN DISTRIBUTED, IT-SUPPORTED TEAMS DURING SYNCHRONOUS COLLABORATION. Journal of the Association for Information Systems.4 indexed citations
15.
Damiani, Ernesto, et al.. (2009). A toward framework for generic uncertainty management. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 1169–1176.4 indexed citations
16.
Ceravolo, Paolo, et al.. (2008). Which role for an ontology of uncertainty. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 132–136.3 indexed citations
17.
Ceravolo, Paolo, et al.. (2007). Trustworthiness-related uncertainty of semantic web-style metadata: a possibilistic approach. Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano). 327. 123–124.3 indexed citations
18.
Ceravolo, Paolo, et al.. (2007). Toward Semantics-aware Representation of Digital Business Processes. University of Twente Research Information.
19.
Ceravolo, Paolo. (2004). Extracting role hierarchies from authentication data flows.. Computer Systems: Science & Engineering. 19.1 indexed citations
20.
Ceravolo, Paolo, et al.. (2004). Knowledge extraction from semi-structured data based on fuzzy techniques. Lecture notes in computer science. 3215. 328–334.
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.