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− | |Abstract= | + | |Abstract=For effectively searching the Web of data, ranking of results is a crucial. Top-k processing strategies have been proposed to allow an efficient processing of such ranked queries. Top-k 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 – necessary statistics are learned at runtime in a pay-as-you-go manner. We conducted extensive experiments on state-of-art SPARQL benchmarks. Our | |
− | + | results are very promising: we could achieve up to 65% time savings, while maintaining a high precision/recall. | |
|Download=awa-approxtopk-tr.pdf | |Download=awa-approxtopk-tr.pdf | ||
|Projekt=IZEUS | |Projekt=IZEUS |
Version vom 15. Januar 2014, 14:49 Uhr
Published: 2013
Oktober
Institution: Institut AIFB, KIT
Erscheinungsort / Ort: Karlsruhe
Archivierungsnummer:3040
Kurzfassung
For effectively searching the Web of data, ranking of results is a crucial. Top-k processing strategies have been proposed to allow an efficient processing of such ranked queries. Top-k 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 – necessary statistics are learned at runtime in a pay-as-you-go manner. We conducted extensive experiments on state-of-art SPARQL benchmarks. Our
results are very promising: we could achieve up to 65% time savings, while maintaining a high precision/recall.
Download: Media:awa-approxtopk-tr.pdf