Over the past decade the rapid rise of creating data in all domains of knowledge such as traffic, medicine, social network, industry, etc., has highlighted the need for enhancing the process of analyzing large data volumes, in order to be able to manage them with more easiness and in addition, discover new relationships which are hidden in them
Optimization problems, which are commonly found in current industry, are not unrelated to this trend, therefore Multi-Objective Optimization Algorithms (MOA) should bear in mind this new scenario. This means that, MOAs have to deal with problems, which have either various data sources (typically streaming) of huge amount of data. Indeed these features, in particular, are found in Dynamic Multi-Objective Problems (DMOPs), which are related to Big Data optimization problems. Mostly with regards to velocity and variability. When dealing with DMOPs, whenever there exist changes in the environment that affect the solutions of the problem (i.e., the Pareto set, the Pareto front, or both), therefore in the fitness landscape, the optimization algorithm must react to adapt the search to the new features of the problem.
Big Data analytics are long and complex processes therefore, with the aim of simplify them, a series of steps are carried out through. A typical analysis is composed of data collection, data manipulation, data analysis and finally result visualization.
In the process of creating a Big Data workflow the analyst should bear in mind the semantics involving the problem domain knowledge and its data. Ontology is the standard way for describing the knowledge about a domain.
As a global target of this PhD Thesis, we are interested in investigating the use of the semantic in the process of Big Data analysis, not only focused on machine learning analysis, but also in optimization.
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