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Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable(More)
In this age, big data applications are increasingly becoming the main focus of attention because of the enormous increment of data generation and storage that has taken place in the last years. This situation becomes a challenge when huge amounts of data are processed to extract knowledge because the data mining techniques are not adapted to the new space(More)
Classification with big data has become one of the latest trends when talking about learning from the available information. The data growth in the last years has rocketed the interest in effectively acquiring knowledge to analyze and predict trends. The variety and veracity that are related to big data introduce a degree of uncertainty that has to be(More)
The application of data mining and machine learning techniques to biological and biomedicine data continues to be an ubiquitous research theme in current bioinformatics. The rapid advances in biotechnology are allowing us to obtain and store large quantities of data about cells, proteins, genes, etc., that should be processed. Moreover, in many of these(More)
— Big data has become one of the emergent topics when learning from data is involved. The notorious increment in the data generation has directed the attention towards the obtaining of effective models that are able to analyze and extract knowledge from these colossal data sources. However, the vast amount of data, the variety of the sources and the need(More)
Cloud Computing is a new computational paradigm which has attracted a lot of interest within the business and research community. Its objective is to integrate a wide amount of heterogeneous resources in an online way to provide services under demand to different types of users, which are liberated from the details of the inner infrastructure, just(More)
Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog(More)
—The " big data " term has caught the attention of experts in the context of learning from data. This term is used to describe the exponential growth and availability of data (struc-tured and unstructured). The design of effective models that can process and extract useful knowledge from these data represents a immense challenge. Focusing on classification(More)
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