Malleability-Aware Skyline Computation on Linked Open Data

In recent years, the skyline query paradigm has been established as a reliable and efficient method for database query personalization. While early efficiency problems have been approached, new challenges in its effectiveness continuously arise. Especially, the rise of the Semantic Web and linked open data leads to personalization issues where skyline queries cannot be applied easily. In fact, the special challenges presented by linked open data establish the need for a new definition of object dominance that is able to cope with the lack of strict schema definitions. However, this new view on dominance in turn has serious implications on the efficiency of the actual skyline computation, since transitivity of the dominance relationships is no longer granted. Therefore, our contributions in this paper can be summarized as a) we design a novel, yet intuitive skyline query paradigm to deal with linked open data b) we provide an effective dominance definition and establish its theoretical properties c) we develop innovative skyline algorithms to deal with the resulting challenges and extensively evaluate the our new algorithms with respect to performance and the enriched skyline semantics.