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首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Cost-Aware Rank Join with Random and Sorted Access
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Cost-Aware Rank Join with Random and Sorted Access

机译：具有成本意识的排名加入，具有随机访问和排序访问

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摘要

In this paper, we address the problem of joining ranked results produced by two or more services on the web. We consider services endowed with two kinds of access that are often available: 1) sorted access, which returns tuples sorted by score; 2) random access, which returns tuples matching a given join attribute value. Rank join operators combine objects of two or more relations and output the k combinations with the highest aggregate score. While the past literature has studied suitable bounding schemes for this setting, in this paper we focus on the definition of a pulling strategy, which determines the order of invocation of the joined services. We propose the Cost-Aware with Random and Sorted access (CARS) pulling strategy, which is derived at compile-time and is oblivious of the query-dependent score distributions. We cast CARS as the solution of an optimization problem based on a small set of parameters characterizing the joined services. We validate the proposed strategy with experiments on both real and synthetic data sets. We show that CARS outperforms prior proposals and that its overall access cost is always within a very short margin from that of an oracle-based optimal strategy. In addition, CARS is shown to be robust w.r.t. the uncertainty that may characterize the estimated parameters.
机译：在本文中，我们解决了将两个或多个服务在网络上产生的排名结果进行合并的问题。我们认为服务具有两种经常可用的访问权限：1）有序访问，它返回按分数排序的元组； 2）随机访问，它返回与给定联接属性值匹配的元组。等级联接运算符组合两个或更多关系的对象，并输出具有最高总得分的k个组合。尽管过去的文献已经研究了适合此设置的边界方案，但在本文中，我们着重于拉策略的定义，该策略确定了联接服务的调用顺序。我们提出了具有随机和排序访问（CARS）的成本感知拉动策略，该策略在编译时派生，并且忽略了与查询相关的分数分布。我们基于一小部分表征联合服务的参数，将CARS视为优化问题的解决方案。我们通过对真实数据集和综合数据集进行实验来验证所提出的策略。我们表明，CARS的性能优于先前的建议，并且其总体访问成本始终比基于Oracle的最佳策略的成本低很多。此外，CARS被证明具有强大的续航能力。可能表征估计参数的不确定性。

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