Jonas Dias

633 total citations
26 papers, 399 citations indexed

About

Jonas Dias is a scholar working on Information Systems and Management, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Jonas Dias has authored 26 papers receiving a total of 399 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Information Systems and Management, 18 papers in Computer Networks and Communications and 11 papers in Information Systems. Recurrent topics in Jonas Dias's work include Scientific Computing and Data Management (24 papers), Distributed and Parallel Computing Systems (17 papers) and Advanced Data Storage Technologies (8 papers). Jonas Dias is often cited by papers focused on Scientific Computing and Data Management (24 papers), Distributed and Parallel Computing Systems (17 papers) and Advanced Data Storage Technologies (8 papers). Jonas Dias collaborates with scholars based in Brazil, France and Russia. Jonas Dias's co-authors include Marta Mattoso, Daniel de Oliveira, Eduardo Ogasawara, Kary Ocaña, Fábio Porto, Patrick Valduriez, Vítor Silva, Álvaro L. G. A. Coutinho, Fernanda Baião and Fernando A. Rochinha and has published in prestigious journals such as Future Generation Computer Systems, Proceedings of the VLDB Endowment and Computational Geosciences.

In The Last Decade

Jonas Dias

26 papers receiving 378 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jonas Dias Brazil 12 336 322 245 25 23 26 399
Zoltán Farkas Hungary 10 271 0.8× 333 1.0× 141 0.6× 28 1.1× 21 0.9× 35 414
Gábor Gombás Hungary 9 267 0.8× 358 1.1× 174 0.7× 11 0.4× 19 0.8× 16 395
Michael Wan United States 12 170 0.5× 339 1.1× 146 0.6× 15 0.6× 36 1.6× 21 419
Jason Novotny Germany 8 284 0.8× 451 1.4× 88 0.4× 19 0.8× 53 2.3× 11 500
Martin Westhead United Kingdom 8 128 0.4× 279 0.9× 120 0.5× 20 0.8× 112 4.9× 17 401
Shalil Majithia United Kingdom 6 98 0.3× 173 0.5× 138 0.6× 10 0.4× 48 2.1× 8 224
Ákos Frohner Switzerland 8 182 0.5× 369 1.1× 112 0.5× 7 0.3× 45 2.0× 12 408
James Magowan United Kingdom 4 115 0.3× 254 0.8× 80 0.3× 13 0.5× 46 2.0× 6 300
Tak‐Lon Wu United States 7 64 0.2× 194 0.6× 203 0.8× 43 1.7× 57 2.5× 8 308
Upulee Kanewala United States 9 57 0.2× 72 0.2× 182 0.7× 11 0.4× 69 3.0× 22 341

Countries citing papers authored by Jonas Dias

Since Specialization
Citations

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

Fields of papers citing papers by Jonas Dias

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jonas Dias

This figure shows the co-authorship network connecting the top 25 collaborators of Jonas Dias. A scholar is included among the top collaborators of Jonas Dias 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 Jonas Dias. Jonas Dias 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.
Dias, Jonas, et al.. (2016). Uncertainty quantification in numerical simulation of particle-laden flows. Computational Geosciences. 20(1). 265–281. 7 indexed citations
2.
Mattoso, Marta, Jonas Dias, Kary Ocaña, et al.. (2014). Dynamic steering of HPC scientific workflows: A survey. Future Generation Computer Systems. 46. 100–113. 41 indexed citations
3.
Dias, Jonas, et al.. (2014). Data-centric iteration in dynamic workflows. Future Generation Computer Systems. 46. 114–126. 16 indexed citations
4.
Oliveira, Daniel de, et al.. (2013). Performance Analysis of Data Filtering in Scientific Workflows. Journal of Information and Data Management. 4(1). 17–26. 1 indexed citations
5.
Mattoso, Marta, Kary Ocaña, Jonas Dias, et al.. (2013). User-steering of HPC workflows. 1–6. 14 indexed citations
6.
Silva, Vítor, Daniel de Oliveira, Kary Ocaña, et al.. (2013). Provenance traces from Chiron parallel workflow engine. 337–338. 3 indexed citations
7.
Dias, Jonas, Eduardo Ogasawara, Daniel de Oliveira, et al.. (2013). Algebraic dataflows for big data analysis. HAL (Le Centre pour la Communication Scientifique Directe). 4. 150–155. 8 indexed citations
8.
Oliveira, Daniel de, Kary Ocaña, Eduardo Ogasawara, et al.. (2013). Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows. Future Generation Computer Systems. 29(7). 1816–1825. 26 indexed citations
9.
Ocaña, Kary, Daniel de Oliveira, Jonas Dias, Eduardo Ogasawara, & Marta Mattoso. (2013). Designing a parallel cloud based comparative genomics workflow to improve phylogenetic analyses. Future Generation Computer Systems. 29(8). 2205–2219. 14 indexed citations
10.
Oliveira, Daniel de, Eduardo Ogasawara, Jonas Dias, Fernanda Baião, & Marta Mattoso. (2012). Ontology-based Semi-automatic Workflow Composition. Cadernos de Linguística e Teoria da Literatura (Universidade Federal de Minas Gerais). 3 indexed citations
11.
Ocaña, Kary, Daniel de Oliveira, Jonas Dias, Eduardo Ogasawara, & Marta Mattoso. (2012). Discovering drug targets for neglected diseases using a pharmacophylogenomic cloud workflow. 1–8. 11 indexed citations
12.
Oliveira, Daniel de, et al.. (2012). Handling Failures in Parallel Scientific Workflows Using Clouds. 129–139. 5 indexed citations
13.
Silva, Vítor, Eduardo Ogasawara, Daniel de Oliveira, et al.. (2012). Evaluating parameter sweep workflows in high performance computing. 1–10. 9 indexed citations
14.
Rochinha, Fernando A., Renato N. Elias, Daniel de Oliveira, et al.. (2012). UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL PREDICTIVE MODELS FOR FLUID DYNAMICS USING A WORKFLOW MANAGEMENT ENGINE. International Journal for Uncertainty Quantification. 2(1). 53–71. 11 indexed citations
15.
Dias, Jonas, et al.. (2012). Abstract: Using Provenance to Visualize Data from Large-Scale Experiments. 1418–1419. 4 indexed citations
16.
Ocaña, Kary, Daniel de Oliveira, Jonas Dias, Eduardo Ogasawara, & Marta Mattoso. (2011). Optimizing Phylogenetic Analysis Using SciHmm Cloud-based Scientific Workflow. 39. 62–69. 19 indexed citations
17.
Dias, Jonas, Eduardo Ogasawara, Daniel de Oliveira, et al.. (2011). Supporting dynamic parameter sweep in adaptive and user-steered workflow. 31–36. 16 indexed citations
18.
Ogasawara, Eduardo, Jonas Dias, Daniel de Oliveira, et al.. (2011). An algebraic approach for data-centric scientific workflows. Proceedings of the VLDB Endowment. 4(12). 1328–1339. 70 indexed citations
19.
Oliveira, Daniel de, Kary Ocaña, Eduardo Ogasawara, et al.. (2011). A Performance Evaluation of X-Ray Crystallography Scientific Workflow Using SciCumulus. 708–715. 13 indexed citations
20.
Dias, Jonas, Eduardo Ogasawara, Daniel de Oliveira, Esther Pacitti, & Marta Mattoso. (2010). Improving Many-Task computing in scientific workflows using P2P techniques. 1–10. 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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