Augmented Access for Querying and Exploring a Polystore

The huge diversity of database technologies in use inside organizations pose today new challenges of data management and integration. Polystores provide a solution to this scenario based on a loosely coupled integration of data sources and the direct access, with the local language, to each storage engine for exploiting its distinctive features. However, given the absence of a global schema, it is hard to know if a query to one system can be satisfied with data stored elsewhere in the polystore. We address this issue by introducing query augmentation, a data manipulation operator for polystores based on the automatic enrichment of the answer to a local query with related data in the rest of the polystore. Augmentation can be used to implement two effective methods for data access in polystores: augmented search and augmented exploration. We show that they provide effective tools for information discovery in polystores that avoid middleware layers, abstract query languages, and shared data models. We also illustrate the design of QUEPA, a system that fully implements our approach in an efficient way. A comprehensive campaign of experiments done with QUEPA shows that our approach is feasible and, unlike other approaches, scales nicely as the polystore grows in the number of stores and size of databases.