An approach to on-demand extension of multidimensional cubes in multi-model settings: Application to IoT-based agro-ecology

Abstract

Managing unstructured and heterogeneous data, integrating them, and enabling their analysis are among the key challenges in data ecosystems, together with the need to accommodate a progressive growth in these systems by seamlessly supporting extensibility. This is particularly relevant for OLAP analyses on multidimensional cubes stored in data warehouses (DWs), which naturally span large portions of heterogeneous data, possibly relying on different data models (relational, document-based, graph-based). While the management of model heterogeneity in DWs, using for instance multi-model databases, has already been investigated, not much has been done to support extensibility. In a previous paper we have investigated a schema-on-read scenario aimed at granting the extensibility of multidimensional cubes by proposing an architecture to support it and discussing the main open issues associated. This paper takes a step further by presenting xCube, an approach to provide on-demand extensibility of multidimensional cubes in a supply-driven fashion. xCube lets users choose a multidimensional element to be extended, using additional data, possibly uploaded from a data lake. Then, the multidimensional schema is extended by considering the functional dependencies implied by these additional data, and the extended multidimensional schema is made available to users for OLAP analyses. After explaining our approach with reference to a motivating case study in agro-ecology, we propose a proof-of-concept implementation using AgensGraph and Mondrian.