Abstract
Skyline query processing has recently received a lot of attention in the big data analysis community. However, in most real applications, the skyline result can not satisfy the needs of users. In this paper, we propose a novel type of skyEXP query to more efficiently analyze and explore the data. The skyEXP query on the subspace V divides the input data M into w separate subsets SE1 (M, V),…, SEw (M, V) such that an object p belongs to SEi (M, V) if it is not dominated by any other objects on V except for those in SE1 (M, V),…, SEi−1(M, V) where i ∈ [1, w]. In order to fast implement the proposed query over big data, an efficient parallel algorithm SQMRM (the SkyEXP Query using Map-Reduce Model) which utilizes the map-reduce framework is presented. Detailed theoretical analyses and extensive experiments demonstrate that our SQMRM algorithm is both efficient and effective.
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Acknowledgments
This work is supported by the Shanghai Rising-Star Program (No. 15QA1403900), the Natural Science Foundation of Shanghai (No. 17ZR1445900), the National Natural Science Foundation of China (No. 61772366), the Fok Ying-Tong Education Foundation (142002) and the Fundamental Research Funds for the Central Universities.
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Huang, Z., Yu, C., Tang, Y., Chen, Y., Zhang, S., Zheng, Z. (2018). Efficient Processing of the SkyEXP Query Over Big Data. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_40
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DOI: https://doi.org/10.1007/978-981-13-0893-2_40
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