  Stream-reasoning query languages such as CQELS and C-SPARQL enable query
answering over RDF streams. Unfortunately, there currently is a lack of
efficient RDF stream generators to feed RDF stream reasoners. State-of-the-art
RDF stream generators are limited with regard to the velocity and volume of
streaming data they can handle. To efficiently generate RDF streams in a
scalable way, we extended the RMLStreamer to also generate RDF streams from
dynamic heterogeneous data streams. This paper introduces a scalable solution
that relies on a dynamic window approach to generate RDF streams with low
latency and high throughput from multiple heterogeneous data streams. Our
evaluation shows that our solution outperforms the state-of-the-art by
achieving millisecond latency (compared to seconds that state-of-the-art
solutions need), constant memory usage for all workloads, and sustainable
throughput of around 70,000 records/s (compared to 10,000 records/s that
state-of-the-art solutions take). This opens up the access to numerous data
streams for integration with the semantic web.
