D. Spiga

82.6k total citations
52 papers, 156 citations indexed

About

D. Spiga is a scholar working on Computer Networks and Communications, Information Systems and Management and Nuclear and High Energy Physics. According to data from OpenAlex, D. Spiga has authored 52 papers receiving a total of 156 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Networks and Communications, 17 papers in Information Systems and Management and 15 papers in Nuclear and High Energy Physics. Recurrent topics in D. Spiga's work include Distributed and Parallel Computing Systems (37 papers), Advanced Data Storage Technologies (32 papers) and Scientific Computing and Data Management (17 papers). D. Spiga is often cited by papers focused on Distributed and Parallel Computing Systems (37 papers), Advanced Data Storage Technologies (32 papers) and Scientific Computing and Data Management (17 papers). D. Spiga collaborates with scholars based in Italy, Switzerland and United States. D. Spiga's co-authors include M Cinquilli, T. Boccali, D. Ciangottini, Eric Wayne Vaandering, F. Fanzago, Fabio Farina, G. Codispoti, D. Bonacorsi, C. Grandi and M. Girone and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computer Physics Communications and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

In The Last Decade

D. Spiga

46 papers receiving 143 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. Spiga Italy 7 143 65 42 28 24 52 156
N. Magini Switzerland 7 108 0.8× 50 0.8× 35 0.8× 12 0.4× 22 0.9× 24 120
D. Cameron Norway 6 130 0.9× 63 1.0× 20 0.5× 29 1.0× 22 0.9× 16 136
P. Nilsson United States 5 109 0.8× 62 1.0× 32 0.8× 16 0.6× 16 0.7× 25 119
Dave Dykstra United States 7 111 0.8× 49 0.8× 30 0.7× 13 0.5× 17 0.7× 37 123
C. Serfon Switzerland 6 141 1.0× 72 1.1× 43 1.0× 13 0.5× 16 0.7× 35 145
R. Santinelli Switzerland 4 112 0.8× 57 0.9× 44 1.0× 17 0.6× 27 1.1× 10 140
J. Elmsheuser Switzerland 7 111 0.8× 64 1.0× 39 0.9× 12 0.4× 17 0.7× 32 131
S. Campana Switzerland 6 108 0.8× 62 1.0× 31 0.7× 13 0.5× 22 0.9× 30 123
Eric Wayne Vaandering United States 6 87 0.6× 46 0.7× 18 0.4× 21 0.8× 16 0.7× 20 92
A. Vaniachine United States 6 86 0.6× 35 0.5× 31 0.7× 21 0.8× 13 0.5× 16 100

Countries citing papers authored by D. Spiga

Since Specialization
Citations

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

Fields of papers citing papers by D. Spiga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

This figure shows the co-authorship network connecting the top 25 collaborators of D. Spiga. A scholar is included among the top collaborators of D. Spiga 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. Spiga. D. Spiga 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.
Spiga, D., et al.. (2025). Exploiting GPU Resources at VEGA for CMS Software Validation. EPJ Web of Conferences. 337. 1083–1083.
3.
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
4.
Aragão, Leonardo, Elisabetta Ronchieri, Giuseppe Ambrosio, et al.. (2024). Air quality changes during the COVID-19 pandemic guided by robust virus-spreading data in Italy. Air Quality Atmosphere & Health. 17(5). 1135–1153. 1 indexed citations
5.
Tedeschi, T., Marco Baioletti, D. Ciangottini, et al.. (2023). Smart Caching in a Data Lake for High Energy Physics Analysis. Journal of Grid Computing. 21(3).
6.
Boccali, T., et al.. (2023). Enabling CMS Experiment to the utilization of multiple hardware architectures: a Power9 Testbed at CINECA. Journal of Physics Conference Series. 2438(1). 12031–12031.
7.
Adelman-McCarthy, Jennifer, T. Boccali, René Caspart, et al.. (2023). Extending the distributed computing infrastructure of the CMS experiment with HPC resources. Journal of Physics Conference Series. 2438(1). 12039–12039. 1 indexed citations
8.
Tedeschi, T., et al.. (2023). Prototyping a ROOT-based distributed analysis workflow for HL-LHC: The CMS use case. Computer Physics Communications. 295. 108965–108965.
9.
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
10.
Giommi, L., et al.. (2022). Cloud native approach for Machine Learning as a Service for High Energy Physics. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 12–12. 2 indexed citations
11.
Boccali, T., et al.. (2021). A possible solution for HEP processing on network secluded Computing Nodes. 2–2. 3 indexed citations
12.
Spiga, D., T. Boccali, D. Ciangottini, et al.. (2019). Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS. SHILAP Revista de lepidopterología. 214. 7027–7027. 8 indexed citations
13.
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
14.
Alvioli, Massimiliano, D. Spiga, & Rex L. Baum. (2016). Evaluation of the parallel performance of the TRIGRS v2.1 model for rainfall-induced landslides. 1 indexed citations
15.
Cinquilli, M, D. Spiga, C. Grandi, et al.. (2012). CRAB3: Establishing a new generation of services for distributed analysis at CMS. Journal of Physics Conference Series. 396(3). 32026–32026. 13 indexed citations
16.
Cinquilli, M, et al.. (2012). Implementing data placement strategies for the CMS experiment based on a popularity model. Journal of Physics Conference Series. 396(3). 32047–32047. 13 indexed citations
17.
Gowdy, S., P. Kreuzer, J. A. Bakken, et al.. (2011). Large scale and low latency analysis facilities for the CMS experiment: development and operational aspects. Journal of Physics Conference Series. 331(7). 72030–72030. 3 indexed citations
18.
Colling, D., F. Fanzago, J. D’Hondt, et al.. (2010). CMS analysis operations. Journal of Physics Conference Series. 219(7). 72007–72007. 6 indexed citations
19.
Spiga, D., M Cinquilli, G. Codispoti, et al.. (2010). Automation of user analysis workflow in CMS. Journal of Physics Conference Series. 219(7). 72019–72019. 3 indexed citations
20.
Spiga, D., S. Lacaprara, M Cinquilli, et al.. (2008). CRAB: an Application for Distributed Scientific Analysis in Grid Projects. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 187–193. 2 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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