Carlos D. Barranco

Retrieving images in fuzzy object-relational databases using dominant color descriptors

In this paper a fuzzy approach for image retrieval on the basis of color features is presented. The proposal deals with vagueness in the color description and introduces the use of fuzzy database models to store and retrieve imprecise data. To face the color description, the concept of dominant fuzzy color is proposed, using linguistic labels for representing the color information in terms of hue, saturation and intensity. To deal with fuzzy data in our database model, we use a general approach which can support the manipulation of fuzzy objects in an object-relational database system. This allows the retrieval of images by performing flexible queries on the database.

A B+-tree based indexing technique for fuzzy numerical data

This paper proposes an indexing technique for fuzzy numerical data which increases the performance of query processing when the query involves an atomic possibility measured flexible condition. The proposal is based on a classical indexing mechanism for numerical crisp data, B^+-tree, which is implemented in most commercial database management systems (DBMS). This makes the proposed technique a good candidate for integration in a fuzzy DBMS when it is developed as an extension of a crisp DBMS. The efficiency of the proposal is contrasted with another indexing method for similar data and queries, G-tree, which is specifically designed to index multidimensional data. Results show that the proposal performance is similar to and more stable than the measured for G-tree when used for indexing fuzzy numbers.

On Storing Ontologies including Fuzzy Datatypes in Relational Databases

This work deals with the need for managing large amounts of fuzzy data in the context of the Semantic Web. A schema to store ontologies with fuzzy datatypes into a database is presented as part of a framework designed to perform tasks of fuzzy information extraction and publishing. The database schema allows the storage of an ontology along with its instances preserving all information. Ontology and instances are stored in different schemas in order to improve the access to instances while retaining the capacity of reasoning over the ontology. This sets the foundations of a research opportunity on the definition of a ontology reasoner over these structures. The paper also presents a brief description of the framework on which the database is included, and the structures conforming the storage schema proposed.

ImmoSoftWeb: a Web Based Fuzzy Application for Real Estate Management

The paper describes ImmoSoftWeb, a web based application which takes advantage of fuzzy sets to apply them on the area of real estate management. ImmoSoftWeb is built on a FRDB, initially using a prototype called FSQL Server, which provides application capabilities for fuzzy handling, but ImmoSoftWeb is independent from FRDB by means of XML technologies usage. Moreover, the paper shows the way real estate attributes can be expressed using fuzzy data, and how fuzzy queries can be used to apply typical real estate customer requirements on a fuzzy real estate database.

A Fuzzy Temporal Object-Relational Database: Model and Implementation

In real world, some data have a specific temporal validity that must be appropiately managed. To deal with this kind of data, several proposals of temporal databases have been introduced. Moreover, time can also be affected by imprecision, vagueness, and/or uncertainty, since human beings manage time using temporal indications and temporal notions, which may also be imprecise. For this reason, information systems require appropriate support to accomplish this task. In this work, we present a novel possibilistic valid time model for fuzzy databases including the data structures, the integrity constraints, and the DML. Together with this model, we also present its implementation by means of a fuzzy valid time support module on top of a fuzzy object‐relational database system. The integration of these modules allows to perform queries that combines fuzzy valid time constraints together with fuzzy predicates. Besides, the model and implementation proposed support the crisp valid time model as a particular case of the fuzzy valid time support provided.

On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images

This paper introduces a novel approach to medical image retrieval using a fuzzy object-relational database management system (FORDBMS). The system stores medical images along with information about the content of the image, such as the presence or absence of certain indicators of pathologies. It allows us to flexibly retrieve them on the basis of these indicators, making it possible to obtain images from patients with similar diagnosis and thus, following a common visual pattern. To illustrate the capabilities of the FORDBMS, this paper focuses on X-ray images of patients suffering from scoliosis (a medical condition in which the patients spine is curved) from which spine descriptions are obtained. Then queries are performed to obtain a set of images with a certain curvature pattern. Results show high accuracy when evaluated by medical experts. Compared with other ad hoc content-based image retrieval systems, the one presented here is easily adaptable to other application domains, customizable, and very scalable.

INDEXING FUZZY NUMERICAL DATA WITH A B+ TREE FOR FAST RETRIEVAL USING NECESSITY-MEASURED FLEXIBLE CONDITIONS

This paper proposes an indexing procedure for improving the performance of query processing on a fuzzy database. It focuses on the case when a necessity-measured atomic flexible condition is imposed on the values of a fuzzy numerical attribute. The proposal is to apply a classical indexing structure for numerical crisp data, a B+-tree combined with a Hilbert curve. The use of such a common indexing technique makes its incorporation into current systems straightforward. The efficiency of the proposal is compared with that of another indexing procedure for similar fuzzy data and flexible query types. Experimental results reveal that the performance of the proposed method is similar and more stable than that of its competitor.

An impact ordering approach for indexing fuzzy sets

We propose an approach for indexing fuzzy data based on inverted files that speeds up retrieval considerably by stopping the traversal of postings lists early. This is possible because the entries in the postings lists are organized in a way that guarantees that there are no matching items beyond a certain point in a list. Consequently, we can reduce the number of false positives significantly, leading to an increase in retrieval performance. We have implemented our approach and evaluated it experimentally, including a test on skewed and real-world data, comparing it to an approach that has previously been shown to be superior to other methods.

Evaluation of Indexing Strategies for Possibilistic Queries Based on Indexing Techniques Available in Traditional RDBMS

A common way to implement a fuzzy database is on top of a classical relational database management systems (RDBMS). Given that almost all RDBMS provide indexing mechanisms to enhance classical query processing performance, finding ways to use these mechanisms to enhance the performance of flexible query processing is of enormous interest. This work proposes and evaluates a set of indexing strategies, implemented exclusively on top of classical RDBMS indexing structures, designed to improve flexible query processing performance, focusing in the case of possibilities queries. Results show the best indexing strategies for different data a query scenarios, offering effective ways to implement fuzzy data indexes on top of a classical RDBMS.

Incorporating biological knowledge for construction of fuzzy networks of gene associations

Graphical abstractDisplay Omitted HighlightsA novel fuzzy-based approach to generate gene association networks is developed.It is able to deal with data noise by incorporating biological.Its performance was proved by means of four thorough experiments.The method is able is able to obtain a high number of edges with biological meaning. Gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information.We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations.The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks