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Artículo
BIGOWL: Knowledge centered Big Data analytics
(Elsevier, 2019)
Knowledge extraction and incorporation is currently considered to be beneficial for efficient Big Data an- alytics. Knowledge can take part in workflow design, constraint definition, parameter selection and con- figuration, ...
Artículo
A multi-objective interactive dynamic particle swarm optimizer
(Springer, 2020)
Multi-objective optimization deals with problems having two or more conflicting objectives that have to be optimized simulta-neously. When the objectives change somehow with time, the problems become dynamic, and if the ...
Artículo
jMetalSP: A framework for dynamic multi-objective big data optimization
(Elsevier, 2018)
Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be ...
Artículo
Optimizing ligand conformations in flexible protein targets: amulti-objective strategy
(Springer, 2020)
Finding the orientation of a ligand (small molecule) with the lowest binding energy to the macromolecule (receptor) is a complex optimization problem, commonly called ligand–protein docking. This problem has been usually ...
Artículo
Ontology-driven approach for KPI meta-modelling, selection and reasoning
(Elsevier, 2021)
A key challenge in current Business Analytics (BA) is the selection of suitable indicators for business objectives. This requires the exploration of business data through data-driven approaches, while modelling business ...
Ponencia
Multi-Objective Big Data Optimization with jMetal and Spark
(Springer, 2017)
Big Data Optimization is the term used to refer to optimiza- tion problems which have to manage very large amounts of data. In this paper, we focus on the parallelization of metaheuristics with the Apache Spark cluster ...
Artículo
VIGLA-M: visual gene expression data analytics
(BMC, 2019)
Background: The analysis of gene expression levels is used in many clinical studies to know how patients evolve or to find new genetic biomarkers that could help in clinical decision making. However, the techniques and ...
Ponencia
DisMatch results for OAEI 2016
(CEUR-WS.Org, 2016)
DisMatch is an experimental ontology matching system based on the use of corpus based distributional measure for approximating se- mantic relatedness. Through the use of a domain-related corpus, the measure can be applied ...
Ponencia
About Designing an Observer Pattern-Based Architecture for a Multi-objective Metaheuristic Optimization Framework
(Springer, 2018)
Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and ...
Artículo
On the design of a framework integrating an optimization engine with streaming technologies
(Elsevier, 2020)
A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available ...