streaming-rsp

Name: streaming-rsp
Availability: InStock
Author: sparq-org

Execute continuous SPARQL queries on live RDF triple streams.

8|1|Updated Jun 7, 2026

Search Tags:#stream processing #sparql #rdf #continuous query #rstream

Ready to run

npx skills add https://github.com/sparq-org/sparq --skill streaming-rsp

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: streaming-rsp
Source: https://github.com/sparq-org/sparq/tree/main/skills/streaming-rsp
Command: npx skills add https://github.com/sparq-org/sparq --skill streaming-rsp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sparq-rsp, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of performing real-time SPARQL queries on streaming RDF data, enabling users to process live RDF triple streams in a continuous manner.

Core Features & Use Cases

Continuous SPARQL Queries: Execute SPARQL queries on streaming RDF data, providing results as windows close.
Time Windows: Support sliding/tumbling time windows for efficient streaming query processing.
RSP-QL Syntax: Use RSP-QL, a surface syntax for RDF Stream Processing, for defining queries.
Use Case: Imagine you are analyzing real-time sensor data. This Skill allows you to define and execute SPARQL queries on the data stream, such as counting the number of readings within a specific time window.

Quick Start

Use the streaming-rsp skill to run a continuous SPARQL query on a live RDF stream.

Frequently Asked Questions about streaming-rsp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run continuous SPARQL queries on live RDF streams?▼

To run continuous SPARQL queries on live RDF streams, use streaming-rsp to execute RSP-QL syntax over streaming RDF triples. It processes real-time data by evaluating queries continuously as time windows close, yielding immediate analytical results.

What is RDF stream processing and when do I need it?▼

RDF stream processing is the continuous execution of queries on live RDF data. You need it for real-time data analytics, such as processing sensor data or event-driven streams, where you must query incoming triples dynamically rather than querying static datasets.

Does streaming-rsp support sliding or tumbling time windows for sensor data?▼

Yes, streaming-rsp supports sliding and tumbling time windows for streaming query processing. This allows you to efficiently process real-time sensor data by defining specific time intervals to aggregate and evaluate incoming RDF triples.

Do I need the sparq-rsp dependency to process live RDF triple streams?▼

Yes, you need the sparq-rsp dependency. The streaming-rsp Skill requires the sparq-rsp crate to execute continuous queries on streaming RDF data, acting as the core engine for evaluating RSP-QL syntax over live triples.

What's the best way to count sensor readings within a specific time window using RSP-QL?▼

The best way to count sensor readings within a time window is to define an RSP-QL query using streaming-rsp. This applies continuous SPARQL query processing to the live RDF stream, calculating aggregates as sliding or tumbling windows close.

Can I use standard SPARQL syntax for event-driven RDF stream processing?▼

For event-driven RDF stream processing, you use RSP-QL syntax rather than standard SPARQL. RSP-QL is a surface syntax designed specifically for RDF Stream Processing, enabling continuous query execution over live data streams.