D. Bonacorsi

36.1k total citations
53 papers, 254 citations indexed

About

D. Bonacorsi is a scholar working on Computer Networks and Communications, Nuclear and High Energy Physics and Information Systems and Management. According to data from OpenAlex, D. Bonacorsi has authored 53 papers receiving a total of 254 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Computer Networks and Communications, 22 papers in Nuclear and High Energy Physics and 13 papers in Information Systems and Management. Recurrent topics in D. Bonacorsi's work include Distributed and Parallel Computing Systems (37 papers), Advanced Data Storage Technologies (28 papers) and Particle physics theoretical and experimental studies (16 papers). D. Bonacorsi is often cited by papers focused on Distributed and Parallel Computing Systems (37 papers), Advanced Data Storage Technologies (28 papers) and Particle physics theoretical and experimental studies (16 papers). D. Bonacorsi collaborates with scholars based in Italy, United States and Switzerland. D. Bonacorsi's co-authors include L. Giommi, L. Tuura, I. Semeniouk, J. M. Hernández, T. Wildish, Ricky Egeland, S. Metson, В. Е. Кузнецов, Derek Feichtinger and T. Diotalevi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment and IEEE Transactions on Nuclear Science.

In The Last Decade

D. Bonacorsi

50 papers receiving 241 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
D. Bonacorsi Italy 8 206 81 53 37 36 53 254
A. Klimentov United States 10 232 1.1× 75 0.9× 126 2.4× 14 0.4× 57 1.6× 70 281
L. Magnoni Switzerland 8 125 0.6× 21 0.3× 43 0.8× 24 0.6× 29 0.8× 30 168
Gabriele Garzoglio United States 9 245 1.2× 9 0.1× 84 1.6× 15 0.4× 159 4.4× 45 287
Florian Schintke Germany 8 289 1.4× 4 0.0× 59 1.1× 23 0.6× 104 2.9× 37 318
Cătălin Meiroşu Sweden 11 302 1.5× 8 0.1× 3 0.1× 30 0.8× 97 2.7× 41 333
Michael Wong United States 6 92 0.4× 4 0.0× 15 0.3× 16 0.4× 73 2.0× 10 152
Ian Willers Switzerland 7 119 0.6× 3 0.0× 13 0.2× 14 0.4× 69 1.9× 17 148
Jonathan Geisler United States 9 273 1.3× 3 0.0× 62 1.2× 15 0.4× 77 2.1× 16 291
Matthieu Dorier United States 9 280 1.4× 2 0.0× 57 1.1× 33 0.9× 126 3.5× 32 322
Thorsten Schütt Germany 7 193 0.9× 4 0.0× 7 0.1× 24 0.6× 75 2.1× 30 215

Countries citing papers authored by D. Bonacorsi

Since Specialization
Citations

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

Fields of papers citing papers by D. Bonacorsi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of D. Bonacorsi

This figure shows the co-authorship network connecting the top 25 collaborators of D. Bonacorsi. A scholar is included among the top collaborators of D. Bonacorsi 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 D. Bonacorsi. D. Bonacorsi 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
1.
Giommi, L., et al.. (2025). Developments on the “Machine Learning as a Service for High Energy Physics” Framework and Related Cloud Native Solution. IEEE Transactions on Cloud Computing. 13(1). 429–440. 1 indexed citations
2.
Giommi, L., D. Spiga, В. Е. Кузнецов, & D. Bonacorsi. (2024). Progress on cloud native solution of Machine Learning as a Service for HEP. SHILAP Revista de lepidopterología. 295. 7040–7040. 1 indexed citations
3.
Bonacorsi, D., et al.. (2023). Joint Variational Auto-Encoder for Anomaly Detection in High Energy Physics. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 2 indexed citations
4.
Bonacorsi, D., et al.. (2022). Machine Learning inference using PYNQ environment in a AWS EC2 F1 Instance. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 1–1.
5.
Giommi, L., D. Spiga, В. Е. Кузнецов, & D. Bonacorsi. (2022). Prototype of a cloud native solution of Machine Learning as Service for HEP. Proceedings of 41st International Conference on High Energy physics — PoS(ICHEP2022). 968–968. 1 indexed citations
6.
Leite, Daniel, et al.. (2022). Explainable Log Parsing and Online Interval Granular Classification from Streams of Words. 1–8. 1 indexed citations
7.
Diotalevi, T., et al.. (2021). Deep Learning fast inference on FPGA for CMS Muon Level-1 Trigger studies. CERN Document Server (European Organization for Nuclear Research). 5–5. 3 indexed citations
8.
Vale, T. Dias Do, F. Legger, J. Schovancova, et al.. (2020). Operational Intelligence for Distributed Computing Systems for Exascale Science. SHILAP Revista de lepidopterología. 245. 3017–3017. 3 indexed citations
9.
Giommi, L., et al.. (2019). Big Data Analysis for Predictive Maintenance at the INFN-CNAF Data Center using Machine Learning Approaches. SHILAP Revista de lepidopterología. 448–451. 5 indexed citations
10.
Boccali, T., D. Bonacorsi, C. Bozzi, et al.. (2019). Extension of the INFN Tier-1 on a HPC system. Springer Link (Chiba Institute of Technology). 5 indexed citations
11.
Bonacorsi, D., Valentin Kuznetsov, L. Giommi, et al.. (2018). Progress on Machine and Deep Learning applications in CMS Computing. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 22–22. 1 indexed citations
12.
Bonacorsi, D.. (2016). Containerization of CMS Applications with Docker. CERN Document Server (European Organization for Nuclear Research). 7–7. 2 indexed citations
13.
Кузнецов, В. Е., et al.. (2016). Predicting dataset popularity for the CMS experiment. Journal of Physics Conference Series. 762. 12048–12048. 11 indexed citations
14.
Codispoti, G., R. Di Maria, D. Bonacorsi, et al.. (2016). Elastic Extension of a CMS Computing Centre Resources on External Clouds. Journal of Physics Conference Series. 762. 12013–12013. 1 indexed citations
15.
Bonacorsi, D., et al.. (2015). 21st International Conference on Computing in High Energy and Nuclear Physics (CHEP2015). Journal of Physics Conference Series. 664(0). 1001–1001. 1 indexed citations
16.
Boccali, T., Giacinto Donvito, Domenico Diacono, et al.. (2014). An Xrootd Italian Federation. Journal of Physics Conference Series. 513(4). 42013–42013. 1 indexed citations
17.
Bonacorsi, D., et al.. (2012). Performance studies and improvements of CMS distributed data transfers. Journal of Physics Conference Series. 396(3). 32040–32040. 7 indexed citations
18.
Bonacorsi, D.. (2012). CMS storage federations. 197. 2012–2015. 4 indexed citations
19.
Bonacorsi, D., P. Capiluppi, A. Fanfani, & C. Grandi. (2005). CMS The Computing Project: Technical Design Report. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 4 indexed citations
20.
Giommi, L., et al.. (1998). Real-time anomaly detection in data centers for log-based predictive maintenance using an evolving fuzzy-rule-based approach. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 27 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.

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