Querying a Polynomial Object-Relational Constraint Database in Model-Based Diagnosis

Many papers related to Constraint Databases (CDBs) theories exist, including proposals that present frameworks for the treatment of constraints as a new data type. Our proposal presents a new way of storing and manipulating constraints as a usual data, and of making queries about the constraint variables derived from an Object-Relational Constraint Database (ORCDB). In this work, the constraints stored in an ORCDB are only polynomial equality constraints. The proposal is based on Gr¨obner bases, constraint consistency and constraint optimisation techniques. Most works in CDB use spatial-temporal data as a case study, however this work presents an emergent engineering domain, that of fault diagnosis.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0

Distributed Model-Based Diagnosis using Object-Relational Constraint Databases

This work presents a proposal to diagnose distributed systems utilizing model-based diagnosis using distributed databases. In order to improve aspects as versatility, persistence, easy composition and efficiency in the diagnosis process we use an Object Relational Constraint Database (ORCDB). Thereby we define a distributed architecture to store the behaviour of components as constraints in a relational database to diagnose a distributed system. This work proposes an algorithm to detect which components fail when their information is distributed in several databases, and all the information is not available in a global way. It is also offered a proposal to define, in execution time, the allocation of the sensors in a distributed system.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0

07212 Abstracts Collection -- Constraint Databases, Geometric Elimination ang Geographic Information Systems

From 20.05. to 25.05., the Dagstuhl Seminar 07212 ``Constraint Databases, Geometric Elimination and Geographic Information Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Developing a labelled object-relational constraint database architecture for the projection operator

Current relational databases have been developed in order to improve the handling of stored data, however, there are some types of information that have to be analysed for which no suitable tools are available. These new types of data can be represented and treated as constraints, allowing a set of data to be represented through equations, inequations and Boolean combinations of both. To this end, constraint databases were defined and some prototypes were developed. Since there are aspects that can be improved, we propose a new architecture called labelled object-relational constraint database (LORCDB). This provides more expressiveness, since the database is adapted in order to support more types of data, instead of the data having to be adapted to the database. In this paper, the projection operator of SQL is extended so that it works with linear and polynomial constraints and variables of constraints. In order to optimize query evaluation efficiency, some strategies and algorithms have been used to obtain an efficient query plan. Most work on constraint databases uses spatiotemporal data as case studies. However, this paper proposes model-based diagnosis since it is a highly potential research area, and model-based diagnosis permits more complicated queries than spatiotemporal examples. Our architecture permits the queries over constraints to be defined over different sets of variables by using symbolic substitution and elimination of variables.Ministerio de Ciencia y Tecnología DPI2006-15476-C02-0

Constraint Databases and Geographic Information Systems

Constraint databases and geographic information systems share many applications. However, constraint databases can go beyond geographic information systems in efficient spatial and spatiotemporal data handling methods and in advanced applications. This survey mainly describes ways that constraint databases go beyond geographic information systems. However, the survey points out that in some areas constraint databases can learn also from geographic information systems

CSP and Restricted Databases

Las Bases de Datos Restrictivas se originaron ante la necesidad de representar de forma m´as compacta y modular datos de gran tama˜no. De esta forma, y como medio para tratar datos continuos como es el caso de los espacio-temporales, se opt´o por tratar la informaci´on como restricciones almacenadas en una base de datos. Gracias a esta forma de tratar las restricciones, se facilita la construcci´on y el modelado de problemas de satisfacci´on de restricciones (CSP) y su posterior resoluci´on. En este art´ıculo, se realiza un recorrido por las distintas razones, metodolog´ıas y herramientas que han ayudado al desarrollo de las Bases de Datos Restrictivas. Junto a dicho estudio, se lleva a cabo un an´alisis de sus deficiencias y de los posibles aspectos a mejorar. Para aumentar la habilidad en la construcci´on de modelos, y ayudando a la resoluci´on de problemas de satisfacci´on de restricciones (CSP), se ofrece una arquitectura de implementaci´on modular, con las ventajas que eso conlleva. Para finalizar, se presenta un ejemplo que aclara las razones que han movido al desarrollo de nuestra propuesta.Constraint Databases were proposed because it was necessary to represent infinite relations in a more modular and compact way. In this way, Constraint Databases were used to handle continuous data, like spatio-temporal, and to enrich both the data model and the queries with constraints. It allows us to handle these constraints and it makes easier to construct and model the Constraint Satisfaction Problems (CSP) when we want to evaluate these queries. In this paper, we carry out a study of the different reasons, methodologies and tools that have helped to the development of Constraint Databases. Also, we present a study of some defects and how to improve them. To help us in the CSP construction, we show a modular framework, with all the advantages that it implies. Finally, we show an example to understand better the reasons that have helped to the development of our system.Ministerio de Ciencia y Tecnología DPI2000-0666-C02-0

Model-Driven Engineering for Constraint Database Query Evaluation

Data used in applications such as CAD, CAM or GIS are complex, but the techniques developed for their treatment and stor age are not adapted enough to their needs. Examples of these types of data are spatiotemporal, scientific, economic or industrial information, in which data has not a single value because is defined by parameters, variables, functions, equations . . .. These complex data cannot be repre sented nor evaluated with the relational algebra types, then a new, more complex, data type is needed, the Constraint type. Constraint Databases were defined and implemented in order to handle this type of constraint data. When a Constraint Database is implemented, different configura tion parameters can be set up, depending on which database manager is going to be used, which constraint programming tool is going to solve the query evaluation, or which type of constraints can be involved. When some of these parameters are changed, the implementation that supports the evaluation of queries over constraints have to be changed too. For this reason, we propose the use of Model-Driven Engineering to model the queries over Constraint Databases in an independent way of the im plementation and the techniques used to evaluate the queries.Junta de Andalucía P08-TIC-04095Ministerio de Ciencia y Tecnología TIN2009-13714Ministerio de Ciencia y Tecnología TIN2010- 21744-C02-0

Temporal data classification using linear classifiers

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality