The rapid growth of IoT and smart city applications is generating an ever-increasing volume of sensor data, creating a demand for efficient storage, management, and retrieval. Much of this data is structured as time series — continuous streams of timestamped measurements. Traditional relational databases are not optimized for handling high-throughput, sequential time series data, leading to the rise of specialized systems such as TDengine, a high-performance time series database.
In parallel, ontologies such as SOSA (Sensor, Observation, Sample, and Actuator) and SSN (Semantic Sensor Network) provide a rich semantic framework for describing sensor data, facilitating interoperability and advanced reasoning. In this presentation, we will explore how time series data can be semantically enriched by combining TDengine with Ontop — a powerful open-source tool that enables meaningful, ontology-based access to data.
