Skyline and reverse skyline query processing in SpatialHadoop

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In this paper, we study the problem of skyline and reverse skyline computation using SpatialHadoop, an extension of Hadoop that enhances its capabilities with spatial awareness. The exploitation of spatial indexing structures and the spatial properties of data can exploit MapReduce-based methods by reducing the reading, writing, computational and communicational overhead. Through our study, we propose two methods for skyline and reverse skyline computation, which operates in the spatial aware environment that SpatialHadoop provides. This environment allows for performing filtering on the initial dataset to retrieve an answer efficiently by using existing state-of-the-art indexing approaches. The proposed algorithms make use of the full capabilities of the indexing mechanisms provided by the SpatialHadoop and have been tested against large-scale datasets including a real-life, large-scale OpenStreetMap dataset. To the best of our knowledge, this is the first work that studies reverse skyline over SpatialHadoop.

论文关键词:Skyline queries,MapReduce,Computational geometry,SpatialHadoop,Big data,Database management,Methodologies and tools

论文评审过程:Received 16 February 2018, Revised 22 April 2019, Accepted 27 April 2019, Available online 10 May 2019, Version of Record 25 July 2019.

论文官网地址:https://doi.org/10.1016/j.datak.2019.04.004