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Efficient subspace skyline query based on user preference using MapReduce

机译:使用MapReduce根据用户偏好进行有效的子空间天际线查询

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摘要

Subspace skyline, as an important variant of skyline, has been widely applied for multiple-criteria decisions, business planning. With the development of mobile internet, subspace skyline query in mobile distributed environments has recently attracted considerable attention. However, efficiently obtaining the meaningful subset of skyline points in any subspace remains a challenging task in the current mobile interne. For more and more mobile applications, subspace skyline query on mobile units is usually limited by big data and wireless bandwidth. To address this issue, in this paper, we propose a system model that can support subspace skyline query in mobile distributed environment. An efficient algorithm for processing the Subspace Skyline Query using MapReduce (SSQ) is also presented which can obtain the meaningful subset of points from the full set of skyline points in any subspace. The SSQ algorithm divides a subspace skyline query into two processing phases: the preprocess phase and the query phase. The preprocess phase includes the pruning process and constructing index process which is designed to reduce network delay and response time. Additionally, the query phase provides two filtering methods, SQM-filtering and epsilon-filtering, to filter the skyline points according to user preference and reduce network cost. Extensive experiments on real and synthetic data are conducted and the experimental results indicate that our algorithm is much efficient, meanwhile, the pruning strategy can further improve the efficiency of the algorithm. (C) 2015 Elsevier B.V. All rights reserved.
机译:子空间天际线是天际线的重要变体,已广泛应用于多标准决策和业务规划。随着移动互联网的发展,移动分布式环境中的子空间天际线查询近来引起了人们的广泛关注。然而,在当前的移动网络中,有效地获得任何子空间中有意义的天际点子集仍然是一项艰巨的任务。对于越来越多的移动应用程序,移动设备上的子空间天际线查询通常受大数据和无线带宽的限制。为了解决这个问题,本文提出了一种可以在移动分布式环境中支持子空间天际线查询的系统模型。还提出了一种使用MapReduce(SSQ)处理子空间天际线查询的有效算法,该算法可以从任何子空间中完整的天际点集合中获取有意义的点子集。 SSQ算法将子空间天际线查询分为两个处理阶段:预处理阶段和查询阶段。预处理阶段包括修剪过程和构建索引过程,旨在减少网络延迟和响应时间。此外,查询阶段提供了两种过滤方法,即SQM过滤和epsilon过滤,以根据用户的喜好过滤天际线点并降低网络成本。进行了真实和综合数据的大量实验,实验结果表明我们的算法效率很高,同时修剪策略可以进一步提高算法的效率。 (C)2015 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Ad hoc networks》 |2015年第12期|105-115|共11页
  • 作者单位

    Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian, Peoples R China|Dalian Jiaotong Univ, Sch Software, Dalian, Peoples R China;

    Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian, Peoples R China;

    Muroran Inst Technol, Dept Informat & Elect Engn, Muroran, Hokkaido, Japan;

    Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian, Peoples R China;

    Dalian Univ, Coll Phys Sci & Technol, Dalian 116012, Peoples R China;

    Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian, Peoples R China|Dalian Ocean Univ, Sch Informat Engn, Dalian, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Subspace skyline query; MapReduce; Pruning strategy; Grid; User preference;

    机译:子空间天际线查询;MapReduce;修剪策略;网格;用户偏好;

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