MapReduce-based computation of area skyline query for selecting good locations in a map
MapReduce-based computation of area skyline query for selecting good locations in a map
复制标题
DOI：
10.1109/bigdata.2017.8258540
复制
发表时间：
2017-12
期刊：
影响因子：
--
通讯作者：
Chen Li;Annisa;Asif Zaman;Y. Morimoto
中科院分区：
文献类型：
--
作者：
Chen Li;Annisa;Asif Zaman;Y. Morimoto
在地图中选择好的位置是许多应用程序中不可或缺的功能。为了选择具体的地点，我们必须指定详细的选择标准。然而，这并不容易，特别是对移动设备用户来说。因此，我们使用了Skyline查询的想法，众所周知，这种查询可以轻松有效地从数据库中检索感兴趣的数据。在我们以前的工作中，我们提出了区域天际线查询来选择地图中的好位置。然而，查询速度还不够快，无法处理“大数据”。本文利用MapReduce框架对查询算法进行了简化和修改，使之适用于大数据查询。实验结果表明，该算法的性能和可扩展性均优于以往的区域天际线算法，能够处理大数据。
Selection of good locations in a map is an indispensable function in many applications. In order to select specific locations, we have to specify detailed selection criteria. However, it is not easy especially for users of mobile devices. Therefore, we used an idea of skyline queries, which are known to be easy and effective to retrieve interesting data from a database. In our previous work, we have proposed area skyline query that selects good locations in a map. However, the query is not fast enough for handling “big data”. We simplify and revise the algorithm of the query in this paper by using MapReduce framework so that we can use it for big data. Experiments' results demonstrate that the performance and scalability are superior to previous area skyline algorithm and are able to handle big data.