Automatic aggregation using explicit metadata

The paper presents a logical data model for statistical data with an explicit modeling of metadata, which allows to perform automatic aggregation. The data are stored in standard relations from the relational model, while the metadata, defining the semantics of the relations, are represented by numerical dependencies which specify the way the summary values are defined in terms of micro-data, as well as the interrelationships among summary values. The present model supports standard relational languages such as SQL. Relations with numerical dependencies are then seen as statistical views over initial relations of micro-data. Queries can be asked either against the views or directly against the initial relations, and in this later case answered, when possible, using the views. The numerical dependencies of the views are run together with the query to compute the answer to the query. This is handled in a completely automatic manner with no need for the user to deal with the intricacy of metadata. The mechanism has been tested by an implementation in Prolog of meaningful examples of queries and dependencies. It is shown in particular that various classical problems in the realm of statistical and multidimensional databases can be easily modeled and solved in the present framework. Finally, the proposed formalism is shown to be useful for statistical database schema design