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- 53 creator andreas-wagner.
- 53 creator thanh-tran.
- 53 creator veli-bicer.
- 53 type InProceedings.
- 53 label "Pay-as-you-go Approximate Join Top-k Processing for the Web of Data".
- 53 sameAs 53.
- 53 abstract "For effectively searching the Web of data, ranking results is a crucial. Top-k processing strategies have been proposed to allow an efficient processing of such ranked queries. These strategies aim at computing k top-ranked results without complete result materialization. However, for many applications result computation time is much more important than result accuracy and completeness. Thus, there is a strong need for approximated ranked results. Unfortunately, previous work on approximate top-k processing is not well-suited for the Web of data. In this paper, we propose the first approximate top-k join framework for Web data and queries. Our approach is very lightweight – neces- sary statistics are learned at runtime in a pay-as-you-go manner. We conducted extensive experiments on state-of-art RDF benchmarks. Our results are very promising: we could achieve up to 65% time savings, while maintaining a high precision/recall.".
- 53 hasAuthorList authorList.
- 53 isPartOf proceedings.
- 53 keyword "Approximate Query Processing".
- 53 keyword "Rank-aware Query Processing".
- 53 keyword "Top-k Join Processing".
- 53 title "Pay-as-you-go Approximate Join Top-k Processing for the Web of Data".