Abstract
Data changes worldwide in size and over time and when new data arrives rapidly from different sources, an easy access to dynamic data becomes a keyfactor. Therefore, temporalizing and streamifying ontology-based data access (OBDA) is a very important topic today, where the industry still relies on algebraic queries. We contribute to the practical efforts in this field by showing how a specific ontology-based stream querying language can be transformed with respect to mappings into standard SQL queries. For that purpose we choose the stream and temporal reasoning query language STARQL. STARQL is motivated by industrial usecases and evaluated in the European research project Optique. It offers access to temporal and streaming data as well for reactive diagnosis or continous monitoring.
| Original language | English |
|---|---|
| Title of host publication | HiDeSt '15---Proceedings of the First Workshop on High-Level Declarative Stream Processing (co-located with KI 2015) |
| Editors | Daniela Nicklas, Özgür L. Özçep |
| Number of pages | 6 |
| Volume | 1447 |
| Publisher | CEUR-WS.org |
| Publication date | 01.09.2015 |
| Pages | 70-75 |
| Publication status | Published - 01.09.2015 |
| Event | 1st Workshop on High-Level Declarative Stream Processing, HiDeSt 2015 - co-located with the 38th German AI Conference, KI 2015 - Dresden, Germany Duration: 21.09.2015 → 25.09.2015 Conference number: 115610 |
DFG Research Classification Scheme
- 409-06 Information Systems, Process and Knowledge Management
