# RMLStreamer-SISO: an RDF stream generator from streaming heterogeneous data

arXiv: https://arxiv.org/abs/2210.14599 · HTML: https://mlsanity.com/paper/2210.14599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2022.

## Built on by (most cited first, 3 of 3)

- [SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration](https://mlsanity.com/paper/2608.25149/) (2026)
- [RDF Stream Taxonomy: Systematizing RDF Stream Types in Research and Practice](https://mlsanity.com/paper/2311.14540/) (2023)
- [Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)](https://mlsanity.com/paper/2412.17159/) (2024)

## Abstract (arXiv)

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.