Approximate OLAP of document-oriented databases: A variety-aware approach. (November 2019)

• Record Type: Journal Article
• Authors: Gallinucci, Enrico; Golfarelli, Matteo; Rizzi, Stefano
• Is Part Of: Information systems. Volume 85(2019)
• Page Start: 114
• Page End: 130
• Publication Date: 2019-11
• Subjects: NoSQL -- Document-oriented databases -- Multidimensional modeling -- OLAP; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2019.02.004
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: 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. Highlights: The inherent variety of documents hinders proper OLAP analyses. We propose an approximated OLAP approach that captures and exploits schema variety. A multidimensional view is given by detecting approximated functional dependencies. We propose indicators to predict and evaluate the quality of OLAP queries. We show that the approach improves the coverage and precision of OLAP queries.