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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. Top-k query processing has been dealt with in different contexts. One line of research targets the so-called top-k join
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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. 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 to be joined are located in different sources and can
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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 source lookups. We show how existing work on top-k join processing can be adapted to the Linked Data setting. We elaborate on strategies for a better estimation of scores of unprocessed
+
 
join result (to obtain tighter bounds for early termination) and for a more aggressive pruning of results. Based on experiments on real-world Linked Data, we show that the proposed top-k join processing technique substantially improves runtime performance.
 
 
|Download=Tr-ldtopk-2011.pdf
 
|Download=Tr-ldtopk-2011.pdf
 
|Projekt=CollabCloud
 
|Projekt=CollabCloud

Aktuelle Version vom 9. März 2012, 19:05 Uhr


Top-k Linked Data Query Processing




Published: 2011 Dezember
Institution: Institut AIFB, KIT
Archivierungsnummer:3022

BibTeX



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

Projekt

CollabCloud



Forschungsgruppe

Wissensmanagement


Forschungsgebiet

Semantic Web