Fuzzy SQL queries in standard SQL database

Uncertain queries are very common in many areas of human activity. The problem can be seen particularly in medicine, where expressions like ”very high”, ”low”, ”normal” are commonly used in order to describe different information. However, the most popular data repositories do not allow to form imprecise queries in order to filter information. Therefore, the paper proposes an extension to the standard SQL language allowing anybody to profit from fuzzy database using any SQL engine. The existing approaches employ different mechanisms in order to allow the user to perform fuzzy queries on a database. The most complex solutions modify the database engine itself. However, such approach is strongly bound to the modified server version and must be updated with any development of the original server. Nevertheless, there is possible to store fuzzy information using for instance columns of regular relational database. Therefore, this approach proposes extensions to the query language allowing to use fuzzy information in a query and provides a parser transforming a fuzzy query into a standard SQL. Thus, the database server version is irrelevant. The solution is provided as a module written in multi-platform Java language using popular JDBC database connection

Construcción de un sistema de información y de ayuda a la decisión mediante lógica difusa para el cultivo del olivar en Andalucía

In Southern Spain, olive (Olea europaea L.) growing is an important part of the economy, especially in the provinces of Jaén, Córdoba and Granada. This work proposes the first stages of an Information and Decision-Support System (IDSS) for providing different types of users (farmers, agricultural engineers, public services, etc.) with information on olive growing and the environment, and also assisting in decision-making. The main purposes of the project reported in this paper are to process uncertain or imprecise data, such as those concerning the environment or crops, and combine user data with other scientific-experimental data. The possibility of storing agricultural and ecological information in fuzzy relational databases, vital to the development of an IDSS is described. The information will be processed using knowledge extraction tools (fuzzy data-mining) that will allow rules on expert knowledge for assessing suitability of land to be developed and making thematic maps with the aid of Geographic Information Systems. Flexible querying will allow the users to collect information interactively from databases, while user information is constantly added. Flexible querying of databases, land suitability and thematic maps may be used to help in decisionmaking.El cultivo del olivo (Olea europaea L.) tiene una enorme importancia económica en la zona sur de España y concretamente en las provincias de Jaén, Córdoba y Granada. En este trabajo se propone la construcción de un sistema de información y ayuda a la toma de decisión (IDSS) que permita en el futuro a distintos tipos de usuarios (agricultores, agrónomos, administraciones públicas, etc.) obtener y manejar información sobre el cultivo de olivar y el soporte ambiental del mismo, así como ayudar en la toma de decisiones. Los principales objetivos desarrollados en este trabajo son el tratamiento de datos inciertos e imprecisos, como es el caso de la información ambiental y sobre cultivos, y la fusión de datos sobre cultivo y otros de carácter científico-experimental. Se describe la posibilidad de almacenar la información de carácter agronómico y ecológico en bases de datos relacionales, que es vital para el desarrollo de un IDSS. La información será procesada a través de herramientas de extracción de conocimiento (minería de datos difusa) y permitirá sobre la base del conocimiento experto el desarrollo de reglas para la clasificación de aptitud del terreno y para la obtención de mapas temáticos con la ayuda de Sistemas de Información Geográfica. La consulta flexible permitirá a los distintos usuarios la consulta interactiva de toda la información almacenada en las bases de datos, así como una implementación constante de las mismas. La consulta flexible de bases de datos, la idoneidad de los terrenos y los mapas temáticos pueden ser de gran utilidad en la toma de decisiones.This work is part of the research projects 1FD97-0244-CO3-2 (financed with FEDER funds) and CGL2004-02282BTE (Spanish Ministry of Education and Science)

A Knowledge Representation Example of a Fuzzy Database Implemented in PostgreSQL, with FIRST-2 and FSQL

In this article we present how to implement fuzzy databases based on the relational model. This approach includes many fuzzy attribute types, which can express the most of fuzzy knowledge types. These fuzzy attribute types include imprecise attributes, fuzzy attributes associated with one or more attributes, or with an independent meaning. In order to represent such fuzzy information we must study two aspects of fuzzy information: how to represent fuzzy data and how to represent fuzzy metaknowledge data. This second information is very important and it must be considered in any fuzzy database. This article studies the fuzzy metaknowledge data for any fuzzy attribute and how to represent both in a relational database. Finally, we apply all of this in a real example in the context of medical appointments.Sociedad Argentina de Informática e Investigación Operativ

A fuzzy approach to similarity in Case-Based Reasoning suitable to SQL implementation

The aim of this paper is to formally introduce a notion of acceptance and similarity, based on fuzzy logic, among case features in a case retrieval system. This is pursued by rst reviewing the relationships between distance-based similarity (i.e. the standard approach in CBR) and fuzzy-based similarity, with particular attention to the formalization of a case retrieval process based on fuzzy query specication. In particular, we present an approach where local acceptance relative to a feature can be expressed through fuzzy distributions on its domain, abstracting the actual values to linguistic terms. Furthermore, global acceptance is completely grounded on fuzzy logic, by means of the usual combinations of local distributions through specic dened norms. We propose a retrieval architecture, based on the above notions and realized through a fuzzy extension of SQL, directly implemented on a standard relational DBMS. The advantage of this approach is that the whole power of an SQL engine can be fully exploited, with no need of implementing specic retrieval algorithms. The approach is illustrated by means of some examples from a recommender system called MyWine, aimed at recommending the suitable wine bottles to a customer providing her requirements in both crisp and fuzzy way

Evaluation of Quantified Statements using Gradual Numbers - 64

Dr. Ludovic Liétard is currently assistant professor at the University of Rennes 1 (IUT Lannion) in France. His research mainly concerns flexible querying of relational databases using fuzzy set theory and various applications of fuzzy set theory in databases. Dr. Daniel Rocacher is currently assistant professor at the University of Rennes 1 (ENSSAT Lannion) in France. He has proposed new directions to define gradual numbers in the framework of fuzzy set theory. His current research concerns their applications in databases. Evaluation of Quantified Statements using Gradual Numbers -2 -Abstract. This paper is devoted to the evaluation of quantified statements which can be found in many applications as decision-making, expert systems or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type "Q X are A" and "Q B X are A". In this context, quantified statements are interpreted using an arithmetic on gradual numbers from ℕ f , ℤ f and ℚ f . It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches

Optimisation techniques for flexible SPARQL queries

RDF datasets can be queried using the SPARQL language but are often irregularly structured and incomplete, which may make precise query formulation hard for users. The SPARQL$^{AR}$ language extends SPARQL 1.1 with two operators - APPROX and RELAX - so as to allow flexible querying over property paths. These operators encapsulate different dimensions of query flexibility, namely approximation and generalisation, and they allow users to query complex, heterogeneous knowledge graphs without needing to know precisely how the data is structured. Earlier work has described the syntax, semantics and complexity of SPARQL$^{AR}$, has demonstrated its practical feasibility, but has also highlighted the need for improving the speed of query evaluation. In the present paper, we focus on the design of two optimisation techniques targeted at speeding up the execution of SPARQL$^{AR}$ queries and on their empirical evaluation on three knowledge graphs: LUBM, DBpedia and YAGO. We show that applying these optimisations can result in substantial improvements in the execution times of longer-running queries (sometimes by one or more orders of magnitude) without incurring significant performance penalties for fast queries

Applications of flexible querying to graph data

Graph data models provide flexibility and extensibility that makes them well-suited to modelling data that may be irregular, complex, and evolving in structure and content. However, a consequence of this is that users may not be familiar with the full structure of the data, which itself may be changing over time, making it hard for users to formulate queries that precisely match the data graph and meet their information seeking requirements. There is a need therefore for flexible querying systems over graph data that can automatically make changes to the user's query so as to find additional or different answers, and so help the user to retrieve information of relevance to them. This chapter describes recent work in this area, looking at a variety of graph query languages, applications, flexible querying techniques and implementations