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首页> 外文会议>2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining >A novel design to support skyline query in key-value stores
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A novel design to support skyline query in key-value stores

机译：支持键值存储中天际线查询的新颖设计

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摘要

Skyline query processing (SQP) is an essential technology for data processing in decision making. Due to the complexity of software development, it is a challenge to implement SQP in key-value stores which have been deployed before. Previous work proposed either to rebuild a new underlying infrastructure or to operate SQP over a centralized management. However, the cost to build a new system from scratch is the obstacle of software development and the single point of failure in a centralized system reduces the system availability. In this paper, we propose two designs of SQP by only using two standard operations in key-value stores, namely, PUT() and GET(), so that our proposal can be built on top of any deployed key-value stores. We use PHTs [1], a distributed index data structure, to exploit the functionalities of range query and k-nearest neighbors query in key-value stores. Our simulations show that RQ-SkyIDX provides excellent performance in a uniform data distribution. On the other hand, KNN-SkyIDX shows lower message overhead than RQ-SkyIDX and exhibits load balance on message overhead in non-uniform data distributions. Through the experiments, we also identify that our proposals can be optimized by a random sampling data set.
机译：Skyline查询处理（SQP）是决策中数据处理的一项必不可少的技术。由于软件开发的复杂性，在以前已部署的键值存储中实施SQP是一个挑战。先前的工作建议重建新的基础结构或通过集中式管理来运行SQP。但是，从头开始构建新系统的成本是软件开发的障碍，而集中式系统中的单点故障会降低系统可用性。在本文中，我们仅通过在键值存储中使用两个标准操作（即PUT（）和GET（））来提出两种SQP设计，以便可以在任何已部署的键值存储之上构建我们的建议。我们使用分布式索引数据结构PHT [1]来利用键值存储区中的范围查询和k最近邻查询功能。我们的仿真表明，RQ-SkyIDX在统一的数据分布中提供了出色的性能。另一方面，KNN-SkyIDX显示的消息开销低于RQ-SkyIDX，并且在非均匀数据分发中显示了消息开销的负载平衡。通过实验，我们还确定可以通过随机抽样数据集优化我们的建议。

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