Analyzing FD Inference in Relational Databases

Imprecise inference models the ability to infer sets of values or information chunks. Imprecise database inference is just as important as precise inference. In fact, it is more prevalent than its precise counterpart even in precise databases. Analyzing the extent of imprecise inference is important in knowledge discovery and database security. Imprecise inference analysis can be used to \mine" rule-based knowledge from database data. In database security, imprecise inference analysis can help determine whether or not a system is safe from imprecise inference attacks. This paper deals with the general problem of analyzing fuzzy inference based on functional dependencies (FDs) in database relations. Fuzzy inference, the ability to infer fuzzy set values, generalizes imprecise (set-valued) inference and precise inference. Likewise, fuzzy relational databases generalize their classical and imprecise counterparts by supporting fuzzy information storage and retrieval. Inference analysis is performed using a special abstract model which maintains vital links to classical, imprecise and fuzzy relational database models. These links increase the utility of the inference formalism in practical applications involving \catalytic inference analysis," including knowledge discovery and database security control.