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Pablo Basanta-Val

Researcher at Charles III University of Madrid

Publications -  66
Citations -  1395

Pablo Basanta-Val is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Real time Java & Java. The author has an hindex of 20, co-authored 66 publications receiving 1249 citations. Previous affiliations of Pablo Basanta-Val include Carlos III Health Institute & Complutense University of Madrid.

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Journal ArticleDOI

Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

TL;DR: Recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data are reviewed.
Proceedings Article

Proportional Justified Representation

TL;DR: In this article, a relaxation of justified representation, called Proportional Justified Representation (PJR), is proposed, which is more demanding than justified representation but compatible with perfect representation.
Journal ArticleDOI

QoS-Aware Real-Time Composition Algorithms for Service-Based Applications

TL;DR: Two algorithms are proposed for the composition of QoS-aware service-based applications with temporal requirements: an exhaustive algorithm that computes the optimal service combination in terms of a figure of merit, suitable for offline composition; and an improved algorithm based on heuristics and partial figures of merit suitable for online composition.
Journal ArticleDOI

Architecting Time-Critical Big-Data Systems

TL;DR: This paper deals with the definition of a time-critical big- data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications and proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance.
Journal ArticleDOI

Improving the predictability of distributed stream processors

TL;DR: This article provides a set of improvements to a computational model called distributed stream processing in order to formalize it as a real-time infrastructure and proposes some extensions to Storm, one of the most popular stream processors.