Adaptive algorithms for set containment joins
S Melnik, H Garcia-Molina - ACM Transactions on Database Systems …, 2003 - dl.acm.org
A set containment join is a join between set-valued attributes of two relations, whose join
condition is specified using the subset (⊆) operator. Set containment joins are deployed in
many database applications, even those that do not support set-valued attributes. In this
article, we propose two novel partitioning algorithms, called the Adaptive Pick-and-Sweep
Join (APSJ) and the Adaptive Divide-and-Conquer Join (ADCJ), which allow computing set
containment joins efficiently. We show that APSJ outperforms previously suggested …
condition is specified using the subset (⊆) operator. Set containment joins are deployed in
many database applications, even those that do not support set-valued attributes. In this
article, we propose two novel partitioning algorithms, called the Adaptive Pick-and-Sweep
Join (APSJ) and the Adaptive Divide-and-Conquer Join (ADCJ), which allow computing set
containment joins efficiently. We show that APSJ outperforms previously suggested …
Adaptive Algorithms for Set Containment Joins (Technical Report)
S Melnik, H Garcia-Molina - 2001 - ilpubs.stanford.edu
A set containment join is a join between set-valued attributes of two relations, whose join
condition is specified using the subset operator. Set containment joins are used in a variety
of database applications. In this paper, we propose two partitioning algorithms, called the
Adaptive Pick-and-Sweep Join and the Adaptive Divide-and-Conquer Join, for computing
set containment joins efficiently. We show that our algorithms outperform previously
suggested algorithms over a wide range of data sets. We present a detailed analysis of the …
condition is specified using the subset operator. Set containment joins are used in a variety
of database applications. In this paper, we propose two partitioning algorithms, called the
Adaptive Pick-and-Sweep Join and the Adaptive Divide-and-Conquer Join, for computing
set containment joins efficiently. We show that our algorithms outperform previously
suggested algorithms over a wide range of data sets. We present a detailed analysis of the …
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