A new multidimensional model with text dimensions: definition and implementation

We present a new multidimensional model with textual dimensions based on a knowledge structure extracted from the texts, where any textual attribute in a database can be processed, and not only XML texts. This dimension allows to treat the textual data in the same way as the non-textual one in an automatic way, without user’s intervention, so all the classical operations in the multidimensional model can been defined for this textual dimension. While most of the models dealing with texts that can be found in the literature are not implemented, in this proposal, the multidimensional model and the OLAP system have been implemented in a software tool, so it can be tested on real data. A case study with medical data is included in this work.Junta de Andalucia P07-TIC02786 P10-TIC6109 P11-TIC746

Systems of possibilistic regressions: a case study in ecological inference

This work introduces how possibilistic regression can be used in the case of non symmetrical triangular membership functions, building a system of regressions, so that suitable restrictions for each particular problem can be incorporated. We apply this methodology to the problem of ecological inference, in particular to the estimation of the electoral transition matrix. An experimentation with several examples shows the benefits of the new approach

A first approach to the multipurpose relational database server

In this paper, an architecture and an implementation of a multipurpose relational database server are proposed. This architecture enables classical queries to be executed, deductions to be made, and data mining operations to be performed on fuzzy or classical data. The proposal of this integration is to combine several ways of querying different types of data. In order to achieve this, a combination of existing metaknowledge bases and new data catalog elements is presented. We also introduce a language for handling all these data coherently and uniformly on the basis of classical SQL sentences

Fuzzy cardinality based evaluation of quanti®ed sentences

Quantified statements are used in the resolution of a great variety of problems. Several methods have been proposed to evaluate statements of types I and II. The objective of this paper is to study these methods, by comparing and generalizing them. In order to do so, we propose a set of properties that must be fulfilled by any method of evaluation of quantified statements, we discuss some existing methods from this point of view and we describe a general approach for the evaluation of quantified statements based on the fuzzy cardinality and fuzzy relative cardinality of fuzzy sets. In addition, we discuss some concrete methods derived from the mentioned approach. These new methods fulfill all the properties proposed and, in some cases, they provide an interpretation or generalization of existing methods

Using Classification Techniques for Assigning Work Descriptions to Task Groups on the Basis of Construction Vocabulary

Construction project management produces a huge amount of documents in a variety of formats. The efficient use of the data contained in these documents is crucial to enhance control and to improve performance. A central pillar throughout the project life cycle is the Bill of Quantities (BoQ) document. It provides economic information and details a collection of work descriptions describing the nature of the different works needed to be done to achieve the project goal. In this work, we focus on the problem of automatically classifying such work descriptions into a predefined task organization hierarchy, so that it can be possible to store them in a common data repository. We describe a methodology for preprocessing the text associated to work descriptions to build training and test data sets and carry out a complete experimentation with several well-known machine learning algorithms.Programa Juan de la Cierva. Grant Number: FJCI-2015-24093Ministry of Economy, Industry and Competitiveness. European Regional Development Fund—ERDF. Grant Number: TIN2014-58227-

Evolutionary Approach for Building, Exploring and Recommending Complex Items With Application in Nutritional Interventions

Over the last few years, the ability of recommender systems to help us in different environments has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition, housing, or traveling. In this paper, we present a recommendation system capable of using different input sources (data and knowledge-based) and producing a complex structured output. We have used an evolutionary approach to combine several unitary items within a flexible structure and have built an initial set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these candidates to offer a final recommendation.We conclude with the application of this approach to the healthy diet recommendation problem, addressing its strengths in this domain.Over the last few years, the ability of recommender systems to help us in different environments has been increasing. Several systems try to offer solutions in highly complex environments such as nutrition, housing, or traveling. In this paper, we present a recommendation system capable of using different input sources (data and knowledge-based) and producing a complex structured output. We have used an evolutionary approach to combine several unitary items within a flexible structure and have built an initial set of complex configurable items. Then, a content-based approach refines (in terms of preferences) these candidates to offer a final recommendation.We conclude with the application of this approach to the healthy diet recommendation problem, addressing its strengths in this domainEuropean Union (Stance4Health) under Grant 816303Ministerio de Ciencia e Innovación under Grant PID2021-123960OB-I00MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/501100011033ERDF (European Regional Development Fund)A way of making Europe. And in part under Grant TED2021-129402B-C21 funded by MCIN (Ministerio de Ciencia e Innovación)/AEI (Agencia estatal de Investigacion)/10.13039/501100011033European Union NextGenerationEU/PRTR (Plan de Recuperación, Transformación y Resiliencia)‘Program of Information and Communication technologies’’ at the University of Granad

A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity

In this paper we have addressed the extraction of hidden knowledge from medical records using data mining techniques such as association rules in conjunction with fuzzy logic in a distributed environment. A significant challenge in this domain is that although there are a lot of studies devoted to analysing health data, very few focus on the understanding and interpretability of the data and the hidden patterns present within the data. A major challenge in this area is that many health data analysis studies have focussed on classification, prediction or knowledge extraction and end users find little interpretability or understanding of the results. This is due to the use of black-box algorithms or because the nature of the data is not represented correctly. This is why it is necessary to focus the analysis not only on knowledge extraction but also on the transformation and processing of the data to improve the modelling of the nature of the data. Techniques such as association rule mining and fuzzy logic help to improve the interpretability of the data and treat it with the inherent uncertainty of real-world data. To this end, we propose a system that automatically: a) pre-processes the database by transforming and adapting the data for the data mining process and enriching the data to generate more interesting patterns, b) performs the fuzzification of the medical database to represent and analyse real-world medical data with its inherent uncertainty, c) discovers interrelations and patterns amongst different features (diagnostic, hospital discharge, etc.), and d) visualizes the obtained results efficiently to facilitate the analysis and improve the interpretability of the information extracted. Our proposed system yields a significant increase in the compression and interpretability of medical data for end-users, allowing them to analyse the data correctly and make the right decisions. We present one practical case using two health-related datasets to demonstrate the feasibility of our proposal for real data.Junta de Andalucia P18-RT-1765Ministry of Universities through the E

Segmenting colour images on the basis of a fuzzy hierarchical approach

In this paper we deal with two problems related to imprecision in colour image segmentation processes: to decide whether a set of pixels verify the property "to be homogeneously coloured", and to represent the set of possible segmentations of an image at different precision levels. In order to solve the first problem we introduce a measure of distance between colours in the CIE L*a*b* space, that allows us to measure the degree of homogeneity of two pixels p and q on the basis of the maximum distance between the colours of consecutive pairs of pixels in any path linking p and q . Since homogeneity is a matter of degree, we define a (fuzzy) segmentation of an image as a set of fuzzy regions, each of them being a fuzzy subset of pixels, that we obtain by using a region growing technique. The membership degree of each pixel to each region is calculated on the basis of our homogeneity measure. The second problem is solved by introducing a fuzzy similarity relation between the fuzzy regions in this initial segmentation. The different α-cuts of the similarity relation define the set of precision levels, from which a nested hierarchy of fuzzy segmentations is finally obtained