Abstract
Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.
| Originalsprache | Englisch |
|---|---|
| Titel | AI 2015: Advances in Artificial Intelligence |
| Redakteure/-innen | Bernhard Pfahringer, Jochen Renz |
| Seitenumfang | 7 |
| Band | 9457 |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer International Publishing |
| Erscheinungsdatum | 22.11.2015 |
| Seiten | 457-463 |
| ISBN (Print) | 978-3-319-26349-6 |
| ISBN (elektronisch) | 978-3-319-26350-2 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 22.11.2015 |
| Veranstaltung | 28th Australasian Joint Conference on Artificial Intelligence - Canberra, Australien Dauer: 30.11.2015 → 04.12.2015 Konferenznummer: 157849 |
DFG-Fachsystematik
- 4.43-01 Theoretische Informatik
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
