A Calculation Method for Key Data of Data Stream Based on ρ-dominant and n-of-Nρ-dominant Skylines

BA Jianmin;GUO Yonghong;PENG Long;ZHAO Dongyang;SHAO Pengzhi;DU Hongbo

Acta Armamentarii ›› 2021, Vol. 42 ›› Issue (5) : 1004-1015. DOI: 10.3969/j.issn.1000-1093.2021.05.013
Paper

A Calculation Method for Key Data of Data Stream Based on ρ-dominant and n-of-Nρ-dominant Skylines

  • BA Jianmin, GUO Yonghong, PENG Long, ZHAO Dongyang, SHAO Pengzhi, DU Hongbo
Author information +
History +

Abstract

The data collection method has constantly been enriched and developed, but the network in the data collection area is often limited, such as intermittent network and small network bandwidth, so the collected data is difficult to be accurately transmitted to the application side in real time. It is very important to ensure how to calculate the key data to reduce the network usage during data transmission. Based on the state data transmission of armored vehicles,the nature of the ρ-dominant relationship in the data stream is reanalyzed,and the query algorithm of ρ-dominant skyline in the data stream is changed and expanded. On this basis, a query algorithm of n-of-Nρ-dominant skyline in the data stream is proposed to further meet the requirements of the selection and transmission of key data in the network-restricted environment. Through the experiment, it is found that the improved ρ-dominant skyline query algorithm and the n-of-Nρ-dominant skyline query algorithm can calculate the relatively critical data, thereby reducing the network cost of data transmission,and n-of-Nρ-dominant skyline query has wider application than ρ-dominant skyline query in the data stream.

Key words

datastream / skylinequery / ρ-dominantskyline / n-of-Nρ-dominantskyline / datatransmission

Cite this article

Download Citations
BA Jianmin, GUO Yonghong, PENG Long, ZHAO Dongyang, SHAO Pengzhi, DU Hongbo. A Calculation Method for Key Data of Data Stream Based on ρ-dominant and n-of-Nρ-dominant Skylines. Acta Armamentarii. 2021, 42(5): 1004-1015 https://doi.org/10.3969/j.issn.1000-1093.2021.05.013

References


[1]TATBULN, ETINTEMEL U, ZDONIK SB, et al. Load shedding in a data stream manager[C]∥Proceedings of the 29th International Conference on Very Large Data Bases.Berlin, Germany: VLDBEndowment, 2003: 309-320.
[2]DEASSUNCAO M D, VEITH A D S, BUYYA R. Distributed data stream processing and edge computing: asurvey on resource elasticity and future directions[J]. Journal of Network & Computer Applications, 2018, 103:1-17.
[3]BORZSONYS, KOSSMANN D, STOCKER K. The skyline operator [C]∥Proceedings of 17th International Conference on Data Engineering. Heidelberg,Germany: IEEE, 2001: 421-430.
[4]PAPADIASD, TAO Y F, FU G, et al. An optimal and progressive algorithm for skyline queries[C]∥Proceedings of the 2003 ACMSIGMOD International Conference on Management of Data. San Diego, CA,US: ACM, 2003: 467-478.
[5]DINGL L, XIN J C, WANG G R, et al. Efficient skyline query processing of massive data based on map-reduce[J]. Chinese Journal of Computers, 2011, 34(10):1785-1796.
[6]余靖,刘盼盼. MapReduce框架下k-支配轮廓查询算法[J]. 燕山大学学报,2014, 38(6):532-537.
YU J, LIU P P. k-dominant skyline query algorithm based on Map Reduce framework[J]. Journal of Yanshan University, 2014, 38(6): 532-537. (in Chinese)
[7]LAIC C, AKBAR Z F, LIU C M, et al. Distributed continuous range-skyline query monitoring over the internet of mobile things[J]. IEEE Internet of Things Journal, 2019, 6(4):6652-6667.
[8]TIANH, SIDDIQUE M A, MORIMOTO Y, et al. An efficient processing of k-dominant skyline query in MapReduce[C]∥Proceedings of International Workshop on Bringing the Value of Big Data to Users. Hangzhou, China:ACM, 2014:29-34.
[9]ZHENGB, LEE K C, LEE W C. Location-dependent skyline query[C]∥Proceedings of International Conference on Mobile Data Management. Beijing, China: IEEE, 2008: 148-155.
[10]LIUX, YANG D N, YE M, et al. U-Skyline: a new skyline query for uncertain databases[J]. IEEE Transactions on Know- ledgeand Data Engineering, 2013, 25(4):945-960.
[11]RENW L, LIAN X, GHAZINOUR K. Skyline queries over incomplete data streams[J]. The VLDB Journal, 2019, 28(6):961-985.
[12]XIEM, WONG C W, LAL L A. An experimental survey of regret minimization query and variants: bridging the best worlds between top-k query and skyline query[J]. The VLDB Journal, 2020, 29(1):147-175.
[13]CHENL, LIAN X. Dynamic skyline queries in metric spaces[C] ∥Proceedings of the 11th International Conference on Extending Database Technology:Advances in Database Technology. Nantes, France: ACM, 2008: 333-343.
[14]周剑刚, 秦小麟, 张珂珩,等.基于道路网的多移动用户动态Skyline查询[J]. 计算机科学,2019, 46 (9): 73-78.
ZHOU J G, QIN X L, ZHANF K H, et al. Dynamic skyline query for multiple mobile users based on road network[J]. Computer Science, 2019, 46 (9): 73-78.(in Chinese)
[15]ZOUL, CHEN L, ZSU M T, et al. Dynamic skyline queries in large graphs[C]∥Proceedings of International Conference on Database Systems for Advanced Applications. Tsukuba, Japan: Springer, 2010:62-78.
[16]LIH, YOO J. An efficient scheme for continuous skyline query processing over dynamic data set[C]∥Proceedings of 2014 International Conference on Big Data and Smart Computing. Bangkok, Thailand: IEEE, 2014:1197-1206.
[17]张丽,邹鹏,贾焰,等.数据流上连续动态skyline查询研究[J]. 计算机研究与发展,2015, 48(1): 77-85.
ZHANG L, ZOU P, JIA Y, et al. Continuous dynamic skyline queries over data stream[J]. Journal of Computer Research and Development, 2015, 48(1): 77-85.(in Chinese)
[18]BAIM, XIN J C, WANG G R, et al. Research on dynamic skyline query processing over data streams[J]. Chinese Journal of Computers, 2011, 34(10): 1876-1884.
[19]信俊昌, 白梅, 东韩, 等. 一种ρ-支配轮廓查询的高效处理算法[J]. 计算机学报, 2011, 34(10): 1876-1884.
XIN J C, BAI M, DONG H, et al. An efficient processing algorithm for ρ-dominant skyline query[J]. Chinese Journal of Computers, 2011, 34(10): 1876-1884.(in Chinese)
[20]王之琼, 霸建民, 黄达,等. 数据流中ρ-支配轮廓查询算法[J]. 计算机科学与探索, 2017, 11(7): 1080-1091.
WANG Z Q, BA J M, HUANG D, et al. ρ-dominant skyline computation on data streams[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(7): 1080-1091.(in Chinese)

Accesses

Citation

Detail

Sections
Recommended

/