Recording data changes in RDF systems is a crucial capability, needed to support auditing, incremental backups, database replication, and event-driven workflows. In large-scale and low-latency RDF applications, the high volume and frequency of updates can cause performance bottlenecks in the serialization and transmission of changes. To alleviate this, we propose Jelly-Patch – a high-performance, compressed binary serialization format for changes in RDF datasets. To evaluate its performance, we benchmark Jelly-Patch against existing RDF Patch formats, using two datasets representing different use cases (change data capture and IoT streams). Jelly-Patch is shown to achieve 3.5–8.9x better compression, and up to 2.5x and 4.6x higher throughput in serialization and parsing, respectively. These significant advancements in throughput and compression are expected to improve the performance of large-scale and low-latency RDF systems.

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