EXODuS: Exploratory OLAP over Document Stores

OLAP has been extensively used for a couple of decades as a data analysis approach to support decision making on enterprise structured data. Now, with the wide diffusion of NoSQL databases holding semi-structured data, there is a growing need for enabling OLAP on document stores as well, to allow non-expert users to get new insights and make better decisions. Unfortunately, due to their schemaless nature, document stores are hardly accessible via direct OLAP querying. In this paper we propose EXODuS, an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP. To discover multidimensional hierarchies in document stores we adopt a data-driven approach based on the mining of approximate functional dependencies; to ensure good performances, we incrementally build local portions of hierarchies for the levels involved in the current user query. Users execute an analysis session by expressing well-formed multidimensional queries related by OLAP operations; these queries are then translated into the native query language of MongoDB, one of the most popular document-based DBMS. An experimental evaluation on real-world datasets shows the efficiency of our approach and its compatibility with a real-time setting.