STREAMER 3.0: Towards Online Monitoring and Distributed Learning

Applications that generate continuous data have proliferated in recent years, and thus the challenge of processing those data streams has emerged. This requires Data Stream Processing frameworks with monitoring capabilities able to detect and react to any non-desired situation. Many streaming use cases deal with distributed sources of data which, for privacy and communication saving purposes, need to be tackled in a distributed manner. Based on the mentioned challenges, this paper presents STREAMER 3.0, an improvement on the former data stream framework with two new modules: (i) a monitoring manager with detection algorithms, alert raising and automatic model updater; and (ii) a distributed learning module relying on federated learning. We showcase these new functionalities with an example of remaining useful life estimation of turbofan engines using an LSTM.