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
