Ontology-based Data Access to Big Data

Recent approaches to ontology-based data access (OBDA) have extended the focus from relational database systems to other types of backends such as cluster frameworks in order to cope with the four Vs associated with big data: volume, veracity, variety and velocity (stream processing). The abstraction that an ontology provides is a benefit from the enduser point of view, but it represents a challenge for developers because high-level queries must be transformed into queries executable on the backend level. In this paper we discuss and evaluate an OBDA system that uses STARQL (Streaming and Temporal ontology Access with a Reasoning-based Query Language), as a highlevel query language to access data stored in a SPARK cluster framework. The development of the STARQL-SPARK engine show that there is a need to provide a homogeneous interface to access both, static, and temporal as well as streaming data because, usually, cluster frameworks lack such an interface. The experimental evaluations show that building a scalable OBDA system that runs with SPARK is more than plug-and-play as one needs to know quite well the data formats and the data organisation in the cluster framework.