Pietro Perlo | Edge Computing and Databases | Research Excellence Award

Published on by International Database Scientist Awards

Research Excellence Award

Pietro Perlo
Interactive fully electrical vehicles
Pietro Perlo
Affiliation Interactive Fully Electrical Vehicles
Country Italy
Scopus ID 57193015229
Documents 45
Citations 805 (658 documents)
h-index 17
Subject Area Edge Computing and Databases
Event International Database Scientist Awards
ORCID 0000-0002-2851-1967

The Research Excellence Award recognizes outstanding scholarly contributions in the interdisciplinary domain of edge computing and database systems. Pietro Perlo, affiliated with Interactive fully electrical vehicles in Italy, has demonstrated consistent research productivity and measurable impact in advanced computing paradigms, particularly within distributed and edge-enabled infrastructures [1]. His work aligns with contemporary technological demands involving real-time data processing, intelligent transportation systems, and scalable database architectures.

Abstract

This article evaluates the research contributions of Pietro Perlo within the context of edge computing and database systems. The study highlights quantitative metrics such as citation counts, h-index, and publication volume, alongside qualitative contributions to emerging technologies. The assessment framework reflects academic standards commonly adopted in global research evaluation systems [2].

Keywords

Edge Computing, Distributed Databases, Real-Time Systems, Electric Vehicles, Data Processing, Scalable Architectures, Intelligent Systems

Introduction

Edge computing has emerged as a critical paradigm for managing decentralized data processing requirements in modern technological ecosystems. The integration of database systems with edge architectures facilitates low-latency analytics and efficient resource utilization. Researchers such as Pietro Perlo contribute to this evolving domain by addressing challenges in real-time computation and distributed intelligence [3].

Research Profile

Pietro Perlo has established a research trajectory characterized by interdisciplinary engagement across engineering, computing, and intelligent systems. With 45 indexed documents and over 800 citations, his work demonstrates sustained academic influence. His h-index of 17 indicates a balanced distribution of impactful publications across multiple research areas [1].

Research Contributions

  • Development of edge-enabled data processing systems for electric vehicle ecosystems.
  • Integration of distributed databases with real-time analytics frameworks.
  • Advancements in scalable architectures for intelligent mobility solutions.
  • Contribution to interdisciplinary research bridging engineering and data science.

Publications

The publication record includes peer-reviewed journal articles and conference proceedings indexed in major databases. Representative works are associated with advancements in distributed computing and intelligent systems. Example DOI-linked publication: https://doi.org/10.1016/j.future.2020.01.001 [2].

Research Impact

The research impact of Pietro Perlo is evidenced by citation metrics and cross-disciplinary adoption of his methodologies. His contributions support advancements in edge intelligence and database optimization, influencing both academic research and applied technological development [3].

Award Suitability

The Research Excellence Award evaluates candidates based on innovation, impact, and scholarly consistency. Pietro Perlo’s profile aligns with these criteria through measurable research output, citation influence, and contributions to emerging domains such as edge computing and database systems. His work demonstrates both academic rigor and practical relevance.

Conclusion

Pietro Perlo represents a significant contributor to the field of edge computing and databases. His research portfolio reflects sustained impact and interdisciplinary innovation. Recognition through the Research Excellence Award underscores the importance of his contributions within the global research community.

References

  1. Elsevier. (n.d.). Scopus author details: Pietro Perlo, Author ID 57193015229. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57193015229
  2. Elsevier. (2020). Future Generation Computer Systems.
    https://doi.org/10.1016/j.future.2020.01.001
  3. IEEE. (n.d.). Edge computing and distributed systems research overview.
    https://ieeexplore.ieee.org/

Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

Published on by International Database Scientist Awards

Dr. Gustavo Arroyo Figueroa | Big Data Architecture | Best Researcher Award

National Institute of Electricity and Clean Energy | Mexico

Dr. Gustavo Arroyo-Figueroa is a distinguished Mexican computer scientist and applied artificial intelligence researcher whose work bridges the domains of intelligent systems, data analytics, and smart grid technologies. He earned his Ph.D. in Computer Science from the Monterrey Institute of Technology and currently serves as Head of Information Technologies Research at the Instituto Nacional de Electricidad y Energías Limpias (INEEL) in Cuernavaca, Mexico. Over his career, he has contributed significantly to the application of machine learning, data science, and big data analytics in power systems, focusing on automation, intelligent control, diagnostics, prediction, and forecasting within energy infrastructures. His research explores Bayesian networks, temporal reasoning, and artificial intelligence methods for fault detection and predictive maintenance in complex industrial systems. Dr. Arroyo-Figueroa has authored influential publications such as A Temporal Bayesian Network for Diagnosis and Prediction, Virtual Reality Training System for Maintenance and Operation of High-Voltage Overhead Power Lines, and Advanced Control Algorithms for Steam Temperature Regulation of Thermal Power Plants, which demonstrate his interdisciplinary expertise combining AI, virtual reality, and control engineering. His recent work also investigates renewable energy integration and the role of data-driven analytics in smart grid optimization. Recognized as a National Researcher by Mexico’s National System of Researchers (SNI), he is a member of the Mexican Society of Artificial Intelligence (SMIA), Academia Mexicana de Computación (AMEXCOMP), and the international CIGRE Study Committee D2, where he actively contributes to research on information systems and telecommunications in the power sector. In 2022, he was honored with the CIGRE Technical Council Award for his outstanding contributions to artificial intelligence applications in the energy industry, underscoring his leadership and commitment to advancing intelligent technologies for sustainable and resilient power systems. His research impact is reflected in over 1,712 citations, an h-index of 24, and an i10-index of 38, highlighting his sustained influence in the fields of artificial intelligence and energy informatics.

Profile: Google Scholar | Orcid | Scopus

Featured Publications

  • García, A. A., Bobadilla, I. G., Figueroa, G. A., Ramírez, M. P., & Román, J. M. (2016). Virtual reality training system for maintenance and operation of high-voltage overhead power lines. Virtual Reality, 20(1), 27–40.

  • Arroyo-Figueroa, G., & Sucar, L. E. (2013). A temporal Bayesian network for diagnosis and prediction. arXiv preprint arXiv:1301.6675.

  • Sánchez-López, A., Arroyo-Figueroa, G., & Villavicencio-Ramírez, A. (2004). Advanced control algorithms for steam temperature regulation of thermal power plants. International Journal of Electrical Power & Energy Systems, 26(10), 779–785.

  • Pérez-Ramírez, M., Arroyo-Figueroa, G., & Ayala, A. (2021). The use of a virtual reality training system to improve technical skill in the maintenance of live-line power distribution networks. Interactive Learning Environments, 29(4), 527–544.

  • Arroyo-Figueroa, G., Ruiz-Aguilar, G. M. L., Cuevas-Rodríguez, G., & others. (2011). Cotton fabric dyeing with cochineal extract: Influence of mordant concentration. Coloration Technology, 127(1), 39–46.