Diego Didona

858 total citations
29 papers, 474 citations indexed

About

Diego Didona is a scholar working on Computer Networks and Communications, Information Systems and Hardware and Architecture. According to data from OpenAlex, Diego Didona has authored 29 papers receiving a total of 474 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Networks and Communications, 15 papers in Information Systems and 7 papers in Hardware and Architecture. Recurrent topics in Diego Didona's work include Distributed systems and fault tolerance (18 papers), Advanced Data Storage Technologies (16 papers) and Cloud Computing and Resource Management (15 papers). Diego Didona is often cited by papers focused on Distributed systems and fault tolerance (18 papers), Advanced Data Storage Technologies (16 papers) and Cloud Computing and Resource Management (15 papers). Diego Didona collaborates with scholars based in Portugal, Switzerland and Italy. Diego Didona's co-authors include Willy Zwaenepoel, Paolo Romano, Francesco Quaglia, Florin Dinu, Pamela Delgado, Sebastiano Peluso, Karan Gupta, Oana Balmau, Rachid Guerraoui and Nikolas Ioannou and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, Proceedings of the VLDB Endowment and ACM Transactions on Computer Systems.

In The Last Decade

Diego Didona

29 papers receiving 448 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diego Didona Portugal 14 423 272 101 62 28 29 474
Ramón Nou Spain 10 444 1.0× 362 1.3× 114 1.1× 41 0.7× 13 0.5× 29 497
Zhuoyao Zhang United States 12 336 0.8× 356 1.3× 46 0.5× 80 1.3× 17 0.6× 27 410
Simon Loesing Switzerland 6 399 0.9× 366 1.3× 27 0.3× 44 0.7× 16 0.6× 7 440
Sethuraman Subbiah United States 5 582 1.4× 569 2.1× 55 0.5× 84 1.4× 13 0.5× 7 644
Ana Gainaru United States 10 290 0.7× 132 0.5× 77 0.8× 65 1.0× 14 0.5× 28 342
Morris A. Jette United States 8 305 0.7× 184 0.7× 126 1.2× 50 0.8× 11 0.4× 11 366
Ferran Julià Spain 10 440 1.0× 413 1.5× 77 0.8× 36 0.6× 8 0.3× 20 497
Geoffroy Vallée United States 13 456 1.1× 287 1.1× 182 1.8× 31 0.5× 9 0.3× 41 517
Shuang Chen United States 7 270 0.6× 224 0.8× 79 0.8× 50 0.8× 9 0.3× 17 331
Guilherme Galante Brazil 6 285 0.7× 276 1.0× 37 0.4× 44 0.7× 9 0.3× 22 346

Countries citing papers authored by Diego Didona

Since Specialization
Citations

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

Fields of papers citing papers by Diego Didona

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Diego Didona

This figure shows the co-authorship network connecting the top 25 collaborators of Diego Didona. A scholar is included among the top collaborators of Diego Didona 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 Diego Didona. Diego Didona 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.
Didona, Diego, Jonas Pfefferle, Nikolas Ioannou, Bernard Metzler, & Animesh Trivedi. (2022). Understanding modern storage APIs. VU Research Portal. 120–127. 20 indexed citations
2.
Didona, Diego, et al.. (2020). The Impossibility of Fast Transactions. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 12. 1143–1154. 1 indexed citations
3.
Didona, Diego, et al.. (2020). Optimistic Causal Consistency for Geo-Replicated Key-Value Stores. IEEE Transactions on Parallel and Distributed Systems. 32(3). 527–542. 9 indexed citations
4.
Balmau, Oana, et al.. (2019). SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores.. 753–766. 35 indexed citations
5.
Didona, Diego & Willy Zwaenepoel. (2018). Size-aware Sharding For Improving Tail Latencies in In-memory Key-value Stores. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 79–94. 13 indexed citations
6.
Didona, Diego, Rachid Guerraoui, Jingjing Wang, & Willy Zwaenepoel. (2018). Causal consistency and latency optimality. Proceedings of the VLDB Endowment. 11(11). 1618–1632. 17 indexed citations
7.
Didona, Diego, Rachid Guerraoui, Jingjing Wang, & Willy Zwaenepoel. (2018). Causal Consistency and Latency Optimality: Friend or Foe? [Extended Version]. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations
8.
Delgado, Pamela, Diego Didona, Florin Dinu, & Willy Zwaenepoel. (2018). Kairos. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 135–148. 29 indexed citations
9.
Didona, Diego, et al.. (2018). Wren: Nonblocking Reads in a Partitioned Transactional Causally Consistent Data Store. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–12. 15 indexed citations
10.
Romano, Paolo, et al.. (2017). An Analytical Model of Hardware Transactional Memory. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 12. 221–231. 3 indexed citations
11.
Delgado, Pamela, Diego Didona, Florin Dinu, & Willy Zwaenepoel. (2016). Job-aware Scheduling in Eagle. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 497–509. 56 indexed citations
12.
Didona, Diego, et al.. (2016). ProteusTM. ACM SIGOPS Operating Systems Review. 50(2). 757–771. 2 indexed citations
13.
Sanzo, Pierangelo Di, Francesco Quaglia, Bruno Ciciani, et al.. (2015). A flexible framework for accurate simulation of cloud in-memory data stores. Simulation Modelling Practice and Theory. 58. 219–238. 10 indexed citations
14.
Didona, Diego & Paolo Romano. (2015). Hybrid Machine Learning/Analytical Models for Performance Prediction. 341–344. 8 indexed citations
15.
Didona, Diego & Paolo Romano. (2014). Performance Modelling of Partially Replicated In-Memory Transactional Stores. i. 265–274. 6 indexed citations
16.
Didona, Diego, Paolo Romano, Sebastiano Peluso, & Francesco Quaglia. (2014). Transactional Auto Scaler. ACM Transactions on Autonomous and Adaptive Systems. 9(2). 1–32. 13 indexed citations
17.
Didona, Diego, et al.. (2013). Identifying the optimal level of parallelism in transactional memory applications. Computing. 97(9). 939–959. 17 indexed citations
18.
Ciciani, Bruno, Diego Didona, Pierangelo Di Sanzo, et al.. (2012). Automated Workload Characterization in Cloud-based Transactional Data Grids. IRIS Research product catalog (Sapienza University of Rome). 1. 1525–1533. 17 indexed citations
19.
Didona, Diego, Paolo Romano, Sebastiano Peluso, & Francesco Quaglia. (2012). Transactional auto scaler. IRIS Research product catalog (Sapienza University of Rome). 125–134. 30 indexed citations
20.
Didona, Diego, D. Carnevale, Sergio Galeani, & Paolo Romano. (2012). An Extremum Seeking Algorithm for Message Batching in Total Order Protocols. Cineca Institutional Research Information System (Tor Vergata University). 42. 89–98. 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.

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