Abstract: Sensor data is of crucial importance in many IoT scenarios. It is used for online monitoring as well as long term data analytics, enabling countless use cases from damage prevention to predictive maintenance. Multivariate sensor time series data is acquired and initially stored close to the sensor, at the edge. It is also beneficial to summarize this data in
windowed aggregations at different resolutions. A subset of the resulting aggregation hierarchy is typically sent to a fog or cloud infrastructure, often via intermittent or low bandwidth connections. Consequently, different views on the data exist on different nodes in the edge-to-cloud continuum. However, when querying this data, users are interested in a fast response and a complete, unified view on the data, regardless of which part in the infrastructure continuum they send the query to and where the data is physically stored.
In this paper, we present a loosely coupled approach that enables fast range queries on a distributed and hierarchical sensor database. Our system only assumes the possibility of fast local range queries on a hierarchical sensor database. It does not require any shared state between nodes and thus degrades gracefully in case certain parts of the hierarchy
We show that our system is suitable for driving interactive data exploration sessions on terabytes of data while unifying the different views on the data. Thus, our system can improve the data analysis experience in many geo-distributed scenarios.
The work is partly sponsored by BFS (https://forschungsstiftung.de/).