Ontology-based Data Federation (Extended Abstract)

Zhenzhen Gu, Davide Lanti, Alessandro Mosca, Guohui Xiao, Jing Xiong, and Diego Calvanese

Proc. of the 35th Int. Workshop on Description Logics (DL 2022). CEUR Workshop Proceedings, http://ceur-ws.org/. 2022. To appear.

We formally introduce ontology-based data federation (OBDF), to denote a framework combining ontology-based data access (OBDA) with a data federation layer, which virtually exposes multiple heterogeneous sources as a single relational database. In this setting, the SQL queries generated by the OBDA component by translating user SPARQL queries are further transformed by the data federation layer so as to be efficiently executed over the data sources. The structure of these SQL queries directly affects their execution time in the data federation layer and their optimization is crucial for performance. We propose here novel optimizations specific for OBDF, which are based on "hints" about existing data redundancies in the sources, empty join operations, and the need for materialized views. Such hints can be systematically inferred by analyzing the OBDA mappings and ontology and exploited to simplify the query structure. We also carry out an experimental evaluation in which we show the effectiveness of our optimizations.


@inproceedings{DL-2022-federation,
   title = "Ontology-based Data Federation (Extended Abstract)",
   year = "2022",
   author = "Zhenzhen Gu and Davide Lanti and Alessandro Mosca and
Guohui Xiao and Jing Xiong and Diego Calvanese",
   booktitle = "Proc. of the 35th Int. Workshop on Description Logics
(DL 2022)",
   publisher = "CEUR-WS.org",
   series = "CEUR Workshop Proceedings, http://ceur-ws.org/",
   note = "To appear",
}
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