Morph-Skyline: Virtual Ontology-Based Data Access for Skyline Queries

Skyline queries are being used in decision-making applications to help stakeholders find the set of data that satisfies certain criteria, whose weight may not be assigned beforehand. Given the wide availability of heterogeneous datasets that are being published following Open Data initiatives, combining skyline queries with query processing approaches such as Ontology-Based Data Access (OBDA), may help stakeholders to improve their decisions exploiting and integrating multiple and heterogeneous data sources. In this paper, we address the problem of evaluating SPARQL skyline queries over an OBDA approach. Our approach implements two different techniques: rewriting skyline queries into SPARQL 1.0 and then translating to SQL, or translating them directly into queries that can be evaluated by the relational database. Our experimental results suggest that the execution time can be reduced by up two orders of magnitude in comparison to current approaches scaling up to larger datasets while identifying precisely the skyline set.