Roger Barga, Sudipto Guha, and Kapil Chhabra explain how unsupervised learning with the robust random cut forest (RRCF) algorithm enables insights into streaming data and share new applications to impute missing values, forecast future values, detect hotspots, and perform classification tasks. They also demonstrate how to implement unsupervised learning over massive data streams.

James Dreiss discusses the challenges in building a content recommendation system for one of the largest news sites in the world, Reuters.com. The particularities of the system include developing a scrolling newsfeed and the use of document vectors for semantic representation of content.

The StreamDM library provides the largest collection of data stream mining algorithms for Spark. Heitor Murilo Gomes and Albert Bifet explain how to use StreamDM and Structured Streaming to develop, apply, and evaluate learning models specially for nonstationary streams (i.e., those with concept drifts).

Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.

Roger Barga, Nina Mishra, Sudipto Guha, and Ryan Nienhuis detail continuous machine learning algorithms that discover useful information in streaming data. They focus on explainable machine learning, including anomaly detection with attribution, the ability to reduce false positives through user feedback, and the detection of anomalies in directed graphs.