Recent years have seen theoretical and practical efforts on temporalizing and streamifying ontology-based data access (OBDA). This paper contributes to the practical efforts with a description/evaluation of a prototype implementation for the stream-temporal query language framework STARQL. STARQL serves the needs for industrially motivated scenarios, providing the same interface for querying historical data (reactive diagnostics) and for querying streamed data (continuous monitoring, predictive analytics). We show how to transform STARQL queries w.r.t. mappings into standard SQL queries, the difference between historical and continuous querying relying only in the use of a static window table vs. an incrementally updated window table. Experiments with a STARQL prototype engine using the PostgreSQL DBMS show the implementability and feasibility of our approach.
|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||14|
|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