RMLStreamer-SISO : an RDF stream generator from streaming heterogeneous data
- Author
- Sitt Min Oo (UGent) , Gerald Haesendonck (UGent) , Ben De Meester (UGent) and Anastasia Dimou (UGent)
- Organization
- Abstract
- 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.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GJMMR9KE5M0745M73RZMNRKN
- MLA
- Min Oo, Sitt, et al. “RMLStreamer-SISO : An RDF Stream Generator from Streaming Heterogeneous Data.” SEMANTIC WEB – ISWC 2022, vol. 13489, Springer, 2022, pp. 697–713, doi:10.1007/978-3-031-19433-7_40.
- APA
- Min Oo, S., Haesendonck, G., De Meester, B., & Dimou, A. (2022). RMLStreamer-SISO : an RDF stream generator from streaming heterogeneous data. SEMANTIC WEB – ISWC 2022, 13489, 697–713. https://doi.org/10.1007/978-3-031-19433-7_40
- Chicago author-date
- Min Oo, Sitt, Gerald Haesendonck, Ben De Meester, and Anastasia Dimou. 2022. “RMLStreamer-SISO : An RDF Stream Generator from Streaming Heterogeneous Data.” In SEMANTIC WEB – ISWC 2022, 13489:697–713. Springer. https://doi.org/10.1007/978-3-031-19433-7_40.
- Chicago author-date (all authors)
- Min Oo, Sitt, Gerald Haesendonck, Ben De Meester, and Anastasia Dimou. 2022. “RMLStreamer-SISO : An RDF Stream Generator from Streaming Heterogeneous Data.” In SEMANTIC WEB – ISWC 2022, 13489:697–713. Springer. doi:10.1007/978-3-031-19433-7_40.
- Vancouver
- 1.Min Oo S, Haesendonck G, De Meester B, Dimou A. RMLStreamer-SISO : an RDF stream generator from streaming heterogeneous data. In: SEMANTIC WEB – ISWC 2022. Springer; 2022. p. 697–713.
- IEEE
- [1]S. Min Oo, G. Haesendonck, B. De Meester, and A. Dimou, “RMLStreamer-SISO : an RDF stream generator from streaming heterogeneous data,” in SEMANTIC WEB – ISWC 2022, Online, 2022, vol. 13489, pp. 697–713.
@inproceedings{01GJMMR9KE5M0745M73RZMNRKN,
abstract = {{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.}},
author = {{Min Oo, Sitt and Haesendonck, Gerald and De Meester, Ben and Dimou, Anastasia}},
booktitle = {{SEMANTIC WEB – ISWC 2022}},
isbn = {{9783031194320}},
issn = {{0302-9743}},
language = {{eng}},
location = {{Online}},
pages = {{697--713}},
publisher = {{Springer}},
title = {{RMLStreamer-SISO : an RDF stream generator from streaming heterogeneous data}},
url = {{http://doi.org/10.1007/978-3-031-19433-7_40}},
volume = {{13489}},
year = {{2022}},
}
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