
Carlos Baquero
My research interests cover data management in eventual consistent settings, distributed data aggregation and causality tracking. In the last years I have collaborated with my co-authors in the development of data summary mechanisms such as Scalable Bloom Filters, causality tracking for dynamic settings with Interval Tree Clocks and Dotted Version Vectors and in predictable eventual consistency with Conflict-Free Replicated Data Types. My recent work has been applied in the Riak distributed database and in Akka distributed data, and is running in production systems serving millions of users worldwide.
Publications
Pondering the Ugly Underbelly, and Whether Images Are Real
Hill, RK;Baquero, C;
2024
Commun. ACM
Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection
Rufino, J;Ramírez, JM;Aguilar, J;Baquero, C;Champati, J;Frey, D;Lillo, RE;Fernández Anta, A;
2024
HELIYON
A Year Embedded in the Crypto-NFT Space
Baquero, C;
2023
COMMUNICATIONS OF THE ACM
Consistent comparison of symptom-based methods for COVID-19 infection detection
Rufino, J;Ramirez, JM;Aguilar, J;Baquero, C;Champati, J;Frey, D;Lillo, RE;Fernandez Anta, A;
2023
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
Supervised Theses
Sistema de perceção visual de baixo custo considerando tecnologia de smartphones
Sara Raquel Monteiro da Silva Pereira
M - 2023
IPP-ISEP

