Javier Conejero

446 total citations
19 papers, 229 citations indexed

About

Javier Conejero is a scholar working on Computer Networks and Communications, Information Systems and Information Systems and Management. According to data from OpenAlex, Javier Conejero has authored 19 papers receiving a total of 229 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Networks and Communications, 12 papers in Information Systems and 5 papers in Information Systems and Management. Recurrent topics in Javier Conejero's work include Cloud Computing and Resource Management (10 papers), Distributed and Parallel Computing Systems (9 papers) and Parallel Computing and Optimization Techniques (5 papers). Javier Conejero is often cited by papers focused on Cloud Computing and Resource Management (10 papers), Distributed and Parallel Computing Systems (9 papers) and Parallel Computing and Optimization Techniques (5 papers). Javier Conejero collaborates with scholars based in Spain, United Kingdom and Sweden. Javier Conejero's co-authors include Rosa M. Badía, Jeffrey Morgan, Pete Burnap, Omer Rana, Daniele Lezzi, Jorge Ejarque, Carmen Carrión, Blanca Caminero, Jesús Labarta and Raül Sirvent and has published in prestigious journals such as Future Generation Computer Systems, Scientific Data and Computing in Science & Engineering.

In The Last Decade

Javier Conejero

18 papers receiving 222 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Javier Conejero Spain 7 116 106 38 36 34 19 229
Philip Healy Ireland 9 161 1.4× 162 1.5× 47 1.2× 20 0.6× 19 0.6× 23 253
Sujoy Basu United States 9 234 2.0× 138 1.3× 63 1.7× 16 0.4× 26 0.8× 24 315
Luisa Massari Italy 7 153 1.3× 175 1.7× 62 1.6× 12 0.3× 19 0.6× 20 262
C. Mic Bowman United States 3 260 2.2× 230 2.2× 146 3.8× 14 0.4× 5 0.1× 3 434
Aditya Devarakonda United States 4 146 1.3× 157 1.5× 32 0.8× 31 0.9× 13 0.4× 7 217
Beth Trushkowsky United States 7 118 1.0× 139 1.3× 133 3.5× 16 0.4× 14 0.4× 11 306
Rod Johnson United Kingdom 6 102 0.9× 155 1.5× 104 2.7× 21 0.6× 10 0.3× 6 254
Vassiliki Andronikou Greece 6 95 0.8× 104 1.0× 37 1.0× 23 0.6× 19 0.6× 26 207
Shalil Majithia United Kingdom 6 173 1.5× 138 1.3× 48 1.3× 98 2.7× 9 0.3× 8 224
Marshall T. Rose United States 8 202 1.7× 80 0.8× 43 1.1× 6 0.2× 34 1.0× 27 279

Countries citing papers authored by Javier Conejero

Since Specialization
Citations

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

Fields of papers citing papers by Javier Conejero

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Javier Conejero

This figure shows the co-authorship network connecting the top 25 collaborators of Javier Conejero. A scholar is included among the top collaborators of Javier Conejero 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 Javier Conejero. Javier Conejero is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Conejero, Javier, et al.. (2025). Orchestrating Quantum-HPC Workflows with Distributed Quantum Circuit Cutting. QRU Quaderns de Recerca en Urbanisme. 1898–1906.
2.
Sirvent, Raül, Javier Conejero, Francesc Lordan, et al.. (2022). Automatic, Efficient and Scalable Provenance Registration for FAIR HPC Workflows. QRU Quaderns de Recerca en Urbanisme. 1–9. 1 indexed citations
3.
Badía, Rosa M., Javier Conejero, Jorge Ejarque, Daniele Lezzi, & Francesc Lordan. (2022). PyCOMPSs as an Instrument for Translational Computer Science. Computing in Science & Engineering. 24(2). 79–84. 3 indexed citations
4.
Andrio, Pau, Javier Conejero, Laia Codó, et al.. (2019). BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows. Scientific Data. 6(1). 169–169. 39 indexed citations
5.
Bautista-Gomez, Leonardo, et al.. (2019). Accelerating Hyperparameter Optimisation with PyCOMPSs. QRU Quaderns de Recerca en Urbanisme. 1–8. 2 indexed citations
6.
Ejarque, Jorge, et al.. (2018). Executing linear algebra kernels in heterogeneous distributed infrastructures with PyCOMPSs. Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles. 73. 47–47. 3 indexed citations
7.
Conejero, Javier, et al.. (2018). Boosting Atmospheric Dust Forecast with PyCOMPSs. 34. 464–474. 2 indexed citations
8.
Ejarque, Jorge, et al.. (2017). Enabling Python to execute efficiently in heterogeneous distributed infrastructures with PyCOMPSs. QRU Quaderns de Recerca en Urbanisme. 1–10. 4 indexed citations
9.
Conejero, Javier, et al.. (2017). Task-based programming in COMPSs to converge from HPC to big data. The International Journal of High Performance Computing Applications. 32(1). 45–60. 24 indexed citations
10.
Conejero, Javier, Omer Rana, Pete Burnap, et al.. (2015). Analyzing Hadoop power consumption and impact on application QoS. Future Generation Computer Systems. 55. 213–223. 23 indexed citations
11.
Badía, Rosa M., Javier Conejero, Jorge Ejarque, et al.. (2015). COMP Superscalar, an interoperable programming framework. SoftwareX. 3-4. 32–36. 46 indexed citations
12.
Ruiz, M. Carmen, Diego Cazorla, Diego Pérez, & Javier Conejero. (2015). Formal performance evaluation of the Map/Reduce framework within cloud computing. The Journal of Supercomputing. 72(8). 3136–3155. 12 indexed citations
13.
Burnap, Pete, Omer Rana, Matthew Williams, et al.. (2014). COSMOS: Towards an integrated and scalable service for analysing social media on demand. International Journal of Parallel Emergent and Distributed Systems. 30(2). 80–100. 42 indexed citations
14.
Conejero, Javier, Blanca Caminero, & Carmen Carrión. (2014). Analysing Hadoop performance in a multi-user IaaS Cloud. 399–406. 3 indexed citations
15.
Conejero, Javier, Omer Rana, Pete Burnap, et al.. (2013). Characterising the Power Consumption of Hadoop Clouds - A Social Media Analysis Case Study. ORCA Online Research @Cardiff (Cardiff University). 233–243. 1 indexed citations
16.
Conejero, Javier, Pete Burnap, Omer Rana, & Jeffrey Morgan. (2013). Scaling Archived Social Media Data Analysis Using a Hadoop Cloud. 685–692. 11 indexed citations
17.
Conejero, Javier, Blanca Caminero, Carmen Carrión, & Luis Tomás. (2013). From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments. Future Generation Computer Systems. 34. 76–93. 5 indexed citations
18.
Conejero, Javier, Luis Tomás, Blanca Caminero, & Carmen Carrión. (2012). QoS Provisioning by Meta-Scheduling in Advance within SLA-Based Grid Environments. Computing and Informatics / Computers and Artificial Intelligence. 31(1). 73–88. 5 indexed citations
19.
Conejero, Javier, et al.. (2012). Multilevel SLA-based QoS Support in Grids. 239–246. 3 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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