Joaquín Pérez Ortega

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This paper deals with heuristic algorithm selection, which can be stated as follows: given a set of solved instances of a NP-hard problem, for a new instance to predict which algorithm solves it better. For this problem, there are two main selection approaches. The first one consists of developing functions to relate performance to problem size. In the(More)
We present a method for creating natural language interfaces to databases (NLIDB) that allow for translating natural language queries into SQL. The method is domain independent, i.e., it avoids the tedious process of configuring the NLIDB for a given domain. We automatically generate the domain dictionary for query translation using semantic metadata of the(More)
In this paper we introduce a redesign of the conjugate gradient method for the iterative solution of sparse linear systems on heterogeneous systems accelerated by graphics processing units (GPUs). Reshaping the GPU kernels induced by the classical formulation of the CG method into algorithm-specific routines results in a slight increase of performance and,(More)
Clustering problems arise in many different applications: machine learning data mining and knowledge discovery, data compression and vector quantization, pattern recognition and pattern classification. It is considered that the k-means algorithm is the best-known squared error-based clustering algorithm, is very simple and can be easily implemented in(More)
The power of k-means algorithm is due to its computational efficiency and the nature of ease at which it can be used. Distance metrics are used to find similar data objects that lead to develop robust algorithms for the data mining functionalities such as classification and clustering. In this paper, the results obtained by implementing the k-means(More)