Session Program

Abstract - Imprecision in queries has being managed using fuzzy logic techniques in the last few decades. Fuzzy logic techniques represent uncertainty formally and it allows to manage imprecision on scalar values in an easy and accurate way. The problem arises when users want to deal with semantics and non-scalar data at once. In this situation, fuzzy logic helps us to manage uncertainty, but it lacks of the flexibility that the semantic properties of words imply. Nowadays, ontologies have addressed this problem by the establishment of the semantic relationships among terms. Here, we present a software that allows us to combine fuzzy and semantic queries on non-scalar data. As a proof of concept, we will show some examples of queries performed on a real database about olive trees plantations.

Abstract - IEEE Std 1855 is the first IEEE standard technology developed in the area of fuzzy logic. Its main characteristic is the interoperability, a design feature that enables system designers to develop fuzzy inference engines without taking into account the hardware/software constraints imposed by the specific architecture on which the system will be deployed. Thanks to this feature, a fuzzy system can be integrated into different types of architectures without any need to carry out porting strategies. This feature is particularly crucial in the area of embedded systems where, for each kind of device, a variety of applications, communication protocols, software libraries and programming tools, exists. In this context, ArduinoTM technology represents one of the most popular architectures, thanks to its ease of development and prototyping. This paper shows how the native extendability feature of IEEE Std 1855 enables the design of a rule-based fuzzy systems in fully interoperable fashion on ArduinoTM architectures and, as a consequence, allows designers to focus on fuzzy concepts, without any need to consider the hardware/software details related to the specific ArduinoTM system.

Abstract - The generation of text reports from numerical and symbolic data is getting the attention of many researchers. This paper presents an R package useful to develop computational systems able to generate linguistic descriptions of complex phenomena. It generates text reports from numerical and symbolic data related to the phenomena under consideration. This is an implementation of our previous research work that is supported by the computational theory of perceptions grounded in the fuzzy sets theory. Developing open source software while we follow the key issues (novelty, usability, interoperability, and relevance) will facilitate the adoption of this new discipline in the research community and industry. We present illustrative examples that show how to use this new R package. The examples reveal that the package is ready to become a relevant tool in the research field of text generation from data.

Abstract - This paper introduces a new way of interaction in JFCS Software where users can view and navigate easily between different combinations of fuzzy colors and pixels in images. Fuzzy colors allow introducing semantics in the description of color by using linguistic labels, filling the semantic gap between the color representation in computers and the subjective human perception. JFCS (Java Fuzzy Color Space) Software, an open source (GPLv3) software with a user-friendly interface, provides an easy framework to design and automatically obtain customized fuzzy color spaces on the basis of an approach proposed by the authors in previous work. This new functionality in JFCS endows it with a very useful tool to visually analyze correspondences between pixels and fuzzy colors in images. Users can obtain combined mapped images where regions, corresponding to a combination of color concepts, are highlighted. In addition, users can describe and interact, in a simple and visual way, with colors present on a given image in terms of fuzzy colors.

Abstract - We propose an approach to integrate the KEEL software tool for knowledge discovery within the KNIME Analytics platform. The integration approach is non-invasive as it does not require the modification of source code in neither of the tools. As a result of the integration, it is possible to use the algorithms provided with KEEL --- including many fuzzy methods --- directly in KNIME workflows, thus taking the advantages of both tools. We report two simple integration examples, which show the effectiveness of the proposed approach in building data analysis workflows involving KEEL methods, possibly along with methods provided by other knowledge discovery tools like WEKA.

Abstract - The discovery of fuzzy associations comprises a collection of data mining methods used to extract knowledge from large data sets. Although there is an extensive catalog of specialized algorithms that cover different aspects of the problem, the most recent approaches are not yet packaged in mainstream software environments. This makes it difficult to incorporate novel association rules methods to the data mining workflow. In this paper an extension of the RKEEL package is described that allows calling from the programming language R to those association rules methods contained in KEEL, which is one of the most comprehensive open source software suites. The potential of the proposed tool is illustrated through a case study comprising seven real-world datasets.