Abstract Data streams which are now massively and constantly arriving from Internet of Things devices, sensors and social media, require efficient processing, querying and reasoning within a given timeframe. With this in mind, the RDF data model, the cornerstone of the Web of Data, supports a feature-rich stream processing ecosystem that takes into account the temporal dimension associated with events. These timestamped streams support advanced temporal analysis ranging from time-based queries, temporal anomaly detection to temporal reasoning. This survey is the first to provide a comprehensive overview of the field of RDF stream processing, focusing on (query) languages, systems, and benchmarks. For each of these areas, we present salient dimensions, propose a taxonomy of existing work, detail the concepts at the core of each approach and describe their main technical aspects and implementation. We hope that the survey will help readers understand this scientifically rich field and identify the most relevant method for various usage scenarios.

The emergence of increasingly powerful and inexpensive single-board computers has motivated a great deal of work in the field of edge computing. We believe that knowledge graphs will contribute to intelligent edge computing. This requires the ability to efficiently answer queries requiring inferences performed with minimal knowledge accessible on a device at the edge of the network. In this work, we determine the minimum size of the knowledge graph that an edge device needs based on the analysis of its query workload. In the context of a succinct data structures-based store, we also present an incremental update of this knowledge graph when new queries are introduced into the environment. We demonstrate the effectiveness of our solution on real use cases encountered by our industrial partner.