Approximate OLAP of Document-Oriented Databases: a Variety-Aware Approach

Schemaless databases, and document-oriented databases in particular, are preferred to relational ones for storing heterogeneous data with variable schemas and structural forms. However, the absence of a unique schema adds complexity to analytical applications, in which a single analysis often involves large sets of data with different schemas. In this paper we propose an original approach to OLAP on collections stored in document-oriented databases. The basic idea is to stop fighting against schema variety and welcome it as an inherent source of information wealth in schemaless sources. Our approach builds on four stages: schema extraction, schema integration, FD enrichment, and querying; these stages are discussed in detail in the paper. To make users aware of the impact of schema variety, we propose a set of indicators inspired by the definition of attribute density. Finally, we experimentally evaluate our approach in terms of efficiency and effectiveness.