FOrTÉ: A Federated Ontology and Timeseries Query Engine

Abstract

The adoption of the Internet of things and cloud-connected objects promoted the proliferation of high-level applications aiming to analyze IoT generated data in order to propose value-added services. Such applications distinguish between at least two types of data: contextual information and timeseries. The contextual one, or graph, captures specific information regarding the connected things and their environment, while timeseries provide sampled values over time. Different storage technologies have emerged targeting exclusively graphs and ontologies or massive data which is more suited to timeseries data. However, combining the two worlds in order to provide a semantic scalable data storage seems to be required. We introduce FOrTE, a scalable federated query engine capable of bridging the gap between both storage technologies.