Techreport3022: Unterschied zwischen den Versionen
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|Author=Andreas Harth | |Author=Andreas Harth | ||
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− | |Abstract=In recent years, top-k query processing has attracted much attention in large-scale scenarios, where computing only the k “best” results is often sufficient | + | |Abstract= In recent years, top-k query processing has attracted much attention in large-scale scenarios, where computing only the k “best” results is often sufficient. One line of research targets the so-called top-k join problem, where the k best final results are obtained through joining |
− | problem, where the k best final results are obtained through joining partial results. In this paper, we study top-k join in a Linked Data setting, where partial results | + | partial results. In this paper, we study the top-k join problem in a Linked Data setting, where partial results are located at different sources and can only be accessed via URI lookups. We show how existing work on top-k join processing can be adapted to the Linked Data setting. Further, we elaborate on strategies for a better estimation of scores of unprocessed join results (to obtain tighter bounds for early termination) and for an aggressive pruning of partial results. Based on experiments on real-world Linked Data, we show that the proposed top-k join processing technique substantially improves runtime performance. |
− | only be accessed via URI | + | |
− | join | ||
|Download=Tr-ldtopk-2011.pdf | |Download=Tr-ldtopk-2011.pdf | ||
|Projekt=CollabCloud | |Projekt=CollabCloud |
Aktuelle Version vom 9. März 2012, 19:05 Uhr
Published: 2011
Dezember
Institution: Institut AIFB, KIT
Archivierungsnummer:3022
Kurzfassung
In recent years, top-k query processing has attracted much attention in large-scale scenarios, where computing only the k “best” results is often sufficient. One line of research targets the so-called top-k join problem, where the k best final results are obtained through joining
partial results. In this paper, we study the top-k join problem in a Linked Data setting, where partial results are located at different sources and can only be accessed via URI lookups. We show how existing work on top-k join processing can be adapted to the Linked Data setting. Further, we elaborate on strategies for a better estimation of scores of unprocessed join results (to obtain tighter bounds for early termination) and for an aggressive pruning of partial results. Based on experiments on real-world Linked Data, we show that the proposed top-k join processing technique substantially improves runtime performance.
Download: Media:Tr-ldtopk-2011.pdf