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Link to original content: https://api.crossref.org/works/10.1155/2014/401618
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However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-k<\/mml:mi><\/mml:mrow><\/mml:math>query processing can be applied to facilitate this task. The top-k<\/mml:mi><\/mml:mrow><\/mml:math>queries retrievek<\/mml:mi><\/mml:mrow><\/mml:math>tuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-k<\/mml:mi><\/mml:mrow><\/mml:math>queries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-k<\/mml:mi><\/mml:mrow><\/mml:math>queries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. 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