  We demonstrate Castor, a cloud-based system for contextual IoT time series
data and model management at scale. Castor is designed to assist Data
Scientists in (a) exploring and retrieving all relevant time series and
contextual information that is required for their predictive modelling tasks;
(b) seamlessly storing and deploying their predictive models in a cloud
production environment; (c) monitoring the performance of all predictive models
in production and (semi-)automatically retraining them in case of performance
deterioration. The main features of Castor are: (1) an efficient pipeline for
ingesting IoT time series data in real time; (2) a scalable, hybrid data
management service for both time series and contextual data; (3) a versatile
semantic model for contextual information which can be easily adopted to
different application domains; (4) an abstract framework for developing and
storing predictive models in R or Python; (5) deployment services which
automatically train and/or score predictive models upon user-defined
conditions. We demonstrate Castor for a real-world Smart Grid use case and
discuss how it can be adopted to other application domains such as Smart
Buildings, Telecommunication, Retail or Manufacturing.
