Proceedings of the 17th International Conference on Data Engineering
Progressive skyline computation in database systems
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
Algorithms and analyses for maximal vector computation
The VLDB Journal — The International Journal on Very Large Data Bases
Shooting stars in the sky: an online algorithm for skyline queries
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Strategies for lifelong knowledge extraction from the web
Proceedings of the 4th international conference on Knowledge capture
XSEarch: a semantic search engine for XML
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Declarative information extraction using datalog with embedded extraction predicates
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Web-Based Measure of Semantic Relatedness
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Flexible query answering on graph-modeled data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
SOFIE: a self-organizing framework for information extraction
Proceedings of the 18th international conference on World wide web
NAGA: Searching and Ranking Knowledge
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Eliciting matters: controlling skyline sizes by incremental integration of user preferences
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Efficiently evaluating skyline queries on RDF databases
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Semantic Web
Efficiently Producing the K Nearest Neighbors in the Skyline on Vertically Partitioned Tables
International Journal of Information Retrieval Research
Hi-index | 0.00 |
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.