  Big Data architectures allow to flexibly store and process heterogeneous
data, from multiple sources, in their original format. The structure of those
data, commonly supplied by means of REST APIs, is continuously evolving. Thus
data analysts need to adapt their analytical processes after each API release.
This gets more challenging when performing an integrated or historical
analysis. To cope with such complexity, in this paper, we present the Big Data
Integration ontology, the core construct to govern the data integration process
under schema evolution by systematically annotating it with information
regarding the schema of the sources. We present a query rewriting algorithm
that, using the annotated ontology, converts queries posed over the ontology to
queries over the sources. To cope with syntactic evolution in the sources, we
present an algorithm that semi-automatically adapts the ontology upon new
releases. This guarantees ontology-mediated queries to correctly retrieve data
from the most recent schema version as well as correctness in historical
queries. A functional and performance evaluation on real-world APIs is
performed to validate our approach.
