Inverting the Database

We wish to propose a database architecture combining a general view of bioinformatics data as a graph of nodes (data objects) and edges (data relationships), with the efficiency and robustness of data management and query provided by indexing and generic programming techniques. We refer to this architecture as “inverting the database” because it replaces tabular schema—the primary interface of relational databases —with the ability to query relationships via indexes, which are ordinarily hidden in relational query languages. In most database systems the index has second-class status: an index cannot be explicitly referenced in a query. This treatment of indexes has been adopted for decades, apparently as a reaction to the complexities introduced by explicit access paths and navigational queries. In many applications today, however, the existence of an index is vital for retrieving information. The second-class status of indexing stands in contrast to its increasing importance. In this paper we invert the role of the index, and make it a first-class citizen in the query language. It is possible to do this in a structured way, allowing users to mention indexes explicitly without yielding to a procedural query model, by converting functional relations into indexes (explicit functions). In the limit, the database becomes a graph, in which the edges are these indexes. Function composition can be specified either explicitly or implicitly as path queries. The net effect of the inversion is to convert the database into a hyperdatabase: a database of databases, connected by indexes or functions. The inversion approach was motivated by our work in biological databases, for which hyperdatabases are a good model. The need for a good model has slowed progress in bioinformatics.