Abstract
Skyline operators are fascinating concepts that let the users extend and evolve a database system. SKY-MR+ algorithm is an efficient framework implemented for skyline operators and queries which uses quad-tree-based histogram, but faces serious limitations and provides inconsistent execution time especially for High datasets. In such cases, it also reports higher processing time with the increase of number of machines in the system. In this paper, an effective framework for skyline queries using principal component analysis (EFSQ-PCA) is proposed and developed which reduces the execution time for High datasets even in the cases of increase in number of machines in the system. The proposed mechanism finds the “Points in Region” using principal component analysis and this forms the base to increase the processing capabilities of skyline queries on various synthetic datasets. Experimental results show improvement in execution time of the proposed EFSQ-PCA as compared to current state of the art under different numbers of dimensions for dataset.
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References
B̈orzs̈onyi, S., Kossman, D., Creature K.: The skyline operator. In: Proceedings of the ICDE, pp. 421–430 (2001)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: Proceedings of ICDE, pp. 717–816. IEEE Laptop Society (2003)
Huang Z., Jensen, C.S., Lu, H., Ooi, B.C.: Skyline queries against mobile light-weight devices in MANETs. In: Proceedings of ICDE’06. IEEE Laptop Society (2006)
Kossmann, D., Ramsak, F., Rost, S.: Shooting stars among the sky: a web algorithmic program for skyline queries. In: Proceedings of VLDB’02, pp. 275–286 (2002)
Lin, X., Yuan, Y., Wang, W., Lu, H.: Stabbing the sky: economical skyline computation over slippery windows. In: Proceedings of ICDE’05, pp. 502–513 (2005)
Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in information systems. ACM Trans. Info Syst. 30(1), 41–82 (2005)
Tan, K.-L., Eng, P.-K., Ooi, B.C.: Economical progressive skyline computation. In: Proceedings of VLDB’01, pp. 301–310 (2001)
Park, Y., Min, J., Wedge, K.: Economical method of skyline queries exploitation map reduce. IEEE Trans. Inf. Knowl. Eng. (2016)
Zou, L., Chen, L., Zsu, M.T.O., Zhao, D.: Dynamic skyline queries in large graphs. In: DASFAA, pp. 62–78 (2010)
Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting: theory and optimizations. In: ICDE, pp. 717–719 (2003)
Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: VLDB, pp. 751–762 (2006)
Deng, K., Zhou, X., Shen, H.T.: Multi-source skyline query processing in road networks. In: Proceedings of ICDE conference, pp. 796–805 (2007)
Chen, L., Lian, X.: Dynamic skyline queries in metric areas. In: EDBT, pp. 333–343 (2008)
Gao, Y., Qin, X., Zheng, B., Chen, G.: Efficient reverse top-k boolean spatial keyword queries on road networks. IEEE Trans. Knowl. Data Eng. 27(5), 1205–1218 (2015)
Ganesan, P., Yang, B., Garcia-Molina, H.: One torus to rule them all: multidimensional queries in p2p systems. In: Proceedings of WebDB workshop, pp. 19–24 (2004)
Lin, X., Xu, J., Hu, H.: Range-based skyline queries in mobile environments. IEEE Trans. Knowl. Data Eng. 25(4), 835–849 (2013)
Wang, Z., Jin, S., Gong, K.: Energy-efficient skycube question method in wireless detector networks. Telkomnika 11(10), 6240–6249 (2013)
Bartolini, I., Ciaccia, P., Patella, M.: Efficient sort-based skyline analysis. ACM Trans. Info. Syst. 33(4), 1–49 (2008)
Kossmann, D., Ramsak, F., Rost, S.: Shooting stars among the sky: a web algorithmic program for skyline queries. In: Proceedings of the twenty eighth VLDB conference, Hong Kong, China (2002)
Saxena, M., Saurabh, P., Verma, B.: A new hashing scheme to overcome the problem of overloading of articles in Usenet, pp. 967–975. Springer, AISC (2012)
Mishra, B.K., Saurabh, P., Verma, B.: A novel approach to classify high dimensional datasets using supervised manifold learning, pp. 22–30. Springer, CCIS (2012)
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Karsh, M., Rai, S., Boghey, R., Saurabh, P. (2020). An Effective Framework for Skyline Queries Using Principal Component Analysis. In: Das, H., Pattnaik, P., Rautaray, S., Li, KC. (eds) Progress in Computing, Analytics and Networking. Advances in Intelligent Systems and Computing, vol 1119. Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_4
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DOI: https://doi.org/10.1007/978-981-15-2414-1_4
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