A Flexible Querying Framework (FQF): Some Implementation Issues Bert Callens, Guy de Tr´ e, J¨org Verstraete, and Axel Hallez Computer Science Laboratory, Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium Bert.Callens@ugent.be Abstract. Fuzzy data are a common concept in today’s information society. Some data can be unknown, other data may be inaccurate or uncertain. Still, this fuzzy data must be accounted for in modern busi- nesses and therefore must be stored. Fuzzy relational databases have been studied extensively over time, which resulted in numerous models and representation techniques, some of which have been implemented as software layers on top of database systems. Different query languages and end-user interfaces have been extended to perform flexible queries on both regular and fuzzy databases. In this paper, a framework is pre- sented that not only enables flexible querying on the relational model, but on other database models as well, of which the most important are object-oriented database models. This framework, called FQF or Flex- ible Querying Framework, is built on the recently developed Java Data Objects (JDO) standard. 1 Introduction In today’s information society, not all data that are considered are completely determined, accurate and certain [1]. Sometimes pieces of data can be missing or unknown, other data can be inaccurate or uncertain. At first sight, this kind of data may seem useless in modern businesses, but in their information processing activities such information must often be accounted for. This unknown or inac- curate data models uncertainty in our knowledge about the actual facts, and is called fuzzy data. As an example that storage of fuzzy data can be necessary, a database of people with their date of birth stored is considered. The age of a cer- tain person might be known, but the exact birthday might be unknown. So the date of birth can’t be precisely determined, although there is some incomplete information about it, which is desired to be stored in the database. Consider the same database for an example on data retrieval. Say one wants a list of al young people in the database, a relation between the linguistic term young and the date of birth must be modelled. In traditional database systems, fuzzy data is often represented in a simple way, as null or default values. Many efforts have been made to modify and extend existing database mod- els, query languages and database applications in order to apply them to fuzzy data. Fuzzy relational databases, as well as other systems, have been studied