Making Sense of Sensors: Semantic Access to Time Series Data
summary
Combines TDengine (one table per device with supertables) and Ontop to translate SPARQL to optimized SQL so queries like “humidity > 46%” run live without ETL, merging results from many vendor schemas into the SOSA ontology vocabulary; demonstration shows unions handled by mappings, supports aggregation and averages, and keeps data in-place for fresh reads. Works with open source stacks but Ontop’s TDengine support is new and still needs optimization.
AI-written summary · about
chapters (9)
- 0:00Introduction
- 0:45IoT data interoperability challenge
- 2:50System architecture overview
- 4:15TD engine time series database
- 5:40Super tables and tags
- 7:28Semantic access with SA ontology
- 9:17Sparkle query example
- 10:42Benefits and challenges
- 12:13Q&A on user complexity
related talks
07mTime Series Databases
49mThinking in time series, the new solution to your pro...
47mOpen Source for Energy Transition via Data Management - Eyes on a Major Prize
29mBuilding up the Bosch Semantic Data Lake
10mPaco Nathan (Liber 118) Interview - OSCON 2014
09mInterview – Yodit Stanton
32mDataEngBytes 2023 - PER-T1-11 - Mark Kitson
32mIntelligent Data Platform : Powered by Semantic Layer, MCP & Agents