Abstract-In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.
REF use multi-view fuzzy querying, in which fuzzy sets may be defined on a hierarchical domain of values called an ontology (values of the domain are connected using the a kind of semantic link), to query incomplete, imprecise and heterogeneously structured data stored in a relational database with fuzzy selection criteria.
Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Computer Science