大数据价值期望探讨

doi:10.11959/j.issn.2096-0271.2017045

Abstract

Abstract:

People from all social circles are concerned about the big data,because all of them think that big data is valuable.However,different people have different value expectation,all of which are the goals when implementing big data project.Multiple kinds of definitions and understandings of big data were attempted to indicate,and then different research perspectives and different value expectations from different people were introduced.Furthermore,some practical and feasible methods,ideas and strategies were given after briefly expressing the relevant research status and industrialization status.

Key words: big data, data science, value expectations

CLC Number:

TP391

Chongjun WANG. Discussions of the value expectations of big data[J]. Big Data Research, 2017, 3(4): 91-103.

Figures/Tables 2

References 57

[1]	王崇骏 . 大数据思维与应用攻略[M]. 北京: 机械工业出版社, 2016.
	WANG C J . Big data thinking and application raiders[M]. Beijing: China Machine Press, 2016.
[2]	SCARDAMALIA M , BEREITE C . Computer support for knowledge-building communities[J]. Journal of the Learning Sciences, 1994,3(3): 265-283.
[3]	李国杰, 程学旗 . 大数据研究:未来科技及经济社会发展的重大战略领域[J]. 中国科学院院刊, 2012(6): 647-657.
	LI G J , CHENG X Q . Research status and scientific thinking of big data[J]. Bulletin of the Chinese Academy of Sciences, 2012(6): 647-657.
[4]	维克托·迈尔·舍恩伯, 肯尼思·库克耶 . 大数据时代:生活、工作与思维的大变革[M].盛杨燕,周涛,译. 杭州: 浙江人民出版社, 2013.
	MAYER-SCH?NBERGER V , CUKIER K . Big data:a revolution that will transform how we live,work and think[M]. Translated by SHENG Y Y,ZHOU T. Hangzhou: Zhejiang People’s Publishing House, 2013.
[5]	郭华东, 王力哲, 陈方 ,等. 科学大数据与数字地球[J]. 科学通报, 2014(12): 1047-1054.
	GUO H D , WANG L Z , CHEN F ,et al. Scientific big data and digital earth[J]. Chinese Science Bulletin, 2014(12): 1047-1054.
[6]	陈刚 . 科学研究大数据挑战[J]. 科学通报, 2015(5): 439-444.
	CHEN G . Challenges of big data in science researches[J]. Chinese Science Bulletin, 2015(5): 439-444.
[7]	刘言, 蔡文生, 邵学广 . 大数据与化学数据挖掘[J]. 科学通报, 2015(8): 694-703.
	LIU Y , CAI W S , SHAO X G . Big data and chemical data mining[J]. Chinese Science Bulletin, 2015(8): 694-703.
[8]	KAISLER S , ARMOUR F , ESPINOSA J A ,et al. Big data:issues and challenges moving forward[C]// 46th Hawaii International Conference on System Sciences (HICSS),Jan 7-10,2013,Wailea,Maui,HI,USA. New Jersey:IEEE Press, 2013: 995-1004.
[9]	JIN X , WAH B W , CHENG X ,et al. Significance and challenges of big data research[J]. Big Data Research, 2015,2(2): 59-64.
[10]	WANG H , XU Z , FUJITA H ,et al. Towards felicitous decision making:an overview on challenges and trends of big data[J]. Information Sciences, 2016(s367): 747-765.
[11]	李学龙, 龚海刚 . 大数据系统综述[J]. 中国科学:信息科学, 2015,45(1): 1-44.
	LI X L , GONG H G . Summary on big data system[J]. SCIENTIA SINICA Informationis, 2015,45(1): 1-44.
[12]	程学旗, 靳小龙, 王元卓 ,等. 大数据系统和分析技术综述[J]. 软件学报, 2014,25(9): 1889-1908.
	CHENG X Q , JIN X L , WANG Y Z ,et al. Survey on big data system and analytic technology[J]. Journal of Software, 2014,25(9): 1889-1908.
[13]	HUANG S J , CHEN S , ZHOU Z H . Multilabel active learning:query type matters[C]// 24th International Conference on Artificial Intelligence,July 25-31,2015,Buenos Aires,Argentina. New York:ACM Press, 2015: 946-952.
[14]	ZHU Y , GAO W , ZHOU Z H . Onepass multi-view learning[C]// 7th Asian Conference on Machine Learning,November 20-22,2015,Hong Kong,China. New York:ACM Press, 2015: 407-422.
[15]	HUANG S J , CHEN S , ZHOU Z H . Multi-label active learning:query type matters[C]// 24th International Conference on Artificial Intelligence,July 25-31,2015,Kyoto,Japan. New York:ACM Press, 2015: 946-952.
[16]	ZHOU Z H . Ensemble learning[M]. Beijing: Tsinghua University Press, 2015: 411-416.
[17]	ZHANG M L , ZHOU Z H . A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014,26(8): 1819-1837.
[18]	NGUYEN C T , WANG X L , LIU J ,et al. Labeling complicated objects:multi-view multi-instance multi-label learning[C]// 28th AAAI Conference on Artificial Intelligence,July 27-31,2014,Québec City,Québec,Canada. New York:ACM Press, 2014: 2013-2019.
[19]	WEI X S , WU J , ZHOU Z H . Scalable algorithms for multi-instance learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017,28(4): 975-987.
[20]	ZHU Y , TING K M , ZHOU Z H . Discover multiple novel labels in multi-instance multi-label learning[C]// 31st AAAI Conference on Artificial Intelligence,February 4-9,2017,San Francisco,USA.[S.l.:s.n.], 2017: 2977-2984
[21]	WEI X S , WU J , ZHOU Z H . Scalable multi-instance learning[C]// IEEE International Conference on Data Mining,Dec 14-17,2014,Québec,Canada. New Jersey:IEEE Press, 2014: 1037-1042.
[22]	STEWART R , ERMON S . Label-free supervision of neural networks with physics and domain knowledge[C]// 31st AAAI Conference on Artificial Intelligence,February 4-9,2017,San Francisco,USA.[S.l.:s.n.], 2017: 2576-2582.
[23]	JOUPPI N P , YOUNG C , PATIL N ,et al. In-datacenter performance analysis of a tensor processing unit[C]// 44th Annual International Symposium on Computer Architecture,June 24 - 28,2017,Toronto,Canada. New York:ACM Press, 2017: 1-12.
[24]	SAYAR A . Hadoop optimization for massive image processing:case study face detection[J]. International Journal of Computers Communications ＆ Control, 2014,9(6): 664-671.
[25]	GU R , WANG S , WANG F ,et al. Cichlid:efficient large scale RDFS/OWL reasoning with spark[C]// 2015 IEEE International Parallel and Distributed Processing Symposium,May 25-29,2015,Orlando,USA. Washington,DC:IEEE Computer Society, 2015: 700-709.
[26]	GU R , YANG X , YAN J ,et al. SHadoop:improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters[J]. Journal of Parallel and Distributed Computing, 2014,74(3): 2166-2179.
[27]	顾荣, 严金双, 杨晓亮 ,等. Hadoop MapReduce短作业执行性能优化[J]. 计算机研究与发展, 2014,51(6): 1270-1280.
	GU R , YAN J S , YANG X L ,et al. Performance optimization for short job execution in Hadoop MapReduce[J]. Journal of Computer Research and Development, 2014,51(6): 1270-1280.
[28]	YANG M , MA R T B . Smooth task migration in Apache storm[C]// 2015 ACM SIGMOD International Conference on Management of Data,May 31-June 4,2015,Melbourne,Australia. New York:ACM Press, 2015: 2067-2068.
[29]	ARMBRUST M , DAS T , DAVIDSON A ,et al. Scaling spark in the real world:performance and usability[J]. Proceedings of the VLDB Endowment, 2015,8(12): 1840-1843.
[30]	ARMBRUST M , XIN R S , LIAN C ,et al. Spark sql:relational data processing in spark[C]// 2015 ACM SIGMOD International Conference on Management of Data,May 31-June 4,2015,Melbourne,Australia. New York:ACM Press, 2015: 1383-1394.
[31]	VAN DER VEEN J S , VAN DER WAAIJ B , LAZOVIK E ,et al. Dynamically scaling apache storm for the analysis of streaming data[C]// 1st International Conference on Big Data Computing Service and Applications,March 30-April 2,2015,San Francisco,USA. New Jersey:IEEE Press, 2015: 154-161.
[32]	SCHAEFER C , MANOJ P M . Enabling privacy mechanisms in apache storm[C]// 1st International Conference on Big Data Computing Service and Applications,March 30-April 2,2015,San Francisco,USA. New Jersey:IEEE Press, 2015: 102-109.
[33]	BOSAGH ZADEH R , MENG X , ULANOV A ,et al. Matrix computations and optimization in apache spark[C]// 2016 ACM SIGKDD Knowledge Discovery and Data Mining (SIGKDD-16),August 13-17,2016,San Francisco,USA. New York:ACM Press, 2016: 31-38.
[34]	ARMBRUST M , XIN R S , LIAN C ,et al. Spark sql:Relational data processing in spark[C]// 2015 ACM SIGMOD International Conference on Management of Data,May 31-June 4,2015,Melbourne,Australia. New York:ACM Press, 2015: 1383-1394.
[35]	ZHAO S Y , XIANG R , SHI Y H ,et al. SCOPE:scalable composite optimization for learning on spark[C]// 31st AAAI Conference on Artificial Intelligence(AAAI-17),February 4-9,2017,San Francisco,USA.[S.l.:s.n.], 2017: 2928-2934.
[36]	中国电子信息产业发展研究院. 2016中国大数据产业生态地图[D]. 北京:中国电子信息产业发展研究院, 2016.
	China Center for Information Industry Development. 2016 Chinese big data industrial ecology map[D]. Beijing:China Center for Information Industry Development, 2016.
[37]	王叁寿 . 大数据商业应用场景[M]. 北京: 机械工业出版社, 2016.
	WANG S S . Big data commercial application scenarios[M]. Beijing: China Machine PressPress, 2016.
[38]	DEAN J , GHEMAWAT S . MapReduce:simplified data processing on large clusters[J]. Communications, 2008,51(1): 107-113.
[39]	GHEMAWAT S , GOBIOFF H , LEUNG S T . The Google file system[C]// 19th ACM Symposium on Operating Systems Principles(SOSP-03),October 19-22,2003,Bolton Landing,USA. New York:ACM Press, 2003,37(5): 29-43.
[40]	CHANG F , DEAN J , GHEMAWAT S ,et al. Bigtable:a distributed storage system for structured data[J]. Transactions on Computer Systems, 2006,26(2): 4.
[41]	SHVACHKO K , KUANG H , RADIA S ,et al. The Hadoop distributed file system[C]// IEEE 26th Symposium on Mass Storage Systems and Technologies(MSST-10),May 3-7,2010,Nevada,USA. New Jersey:IEEE Press, 2010: 1-10.
[42]	BORTHAKUR D . The Hadoop distributed file system:architecture and design[J]. Hadoop Project Website, 2007,11(11): 1-10.
[43]	雷军, 叶航军, 武泽胜 ,等. 基于开源生态系统的大数据平台研究[J]. 计算机研究与发展, 2017,54(1): 80-93.
	LEI J , YE H J , WU Z S ,et al. Big data platform based on open source ecosystem[J]. Journal of Computer Research and Development, 2017,54(1): 80-93.
[44]	唐斯斯, 刘叶婷 . 我国大数据交易亟待突破[J]. 中国发展观察, 2016(13): 19-21.
	TANG S S , LIU Y T . Chinese big data transactions need to break through[J]. China Development Observation, 2016(13): 19-21.
[45]	GOLDBERG A V , HARTLINE J D , WRIGHT A . Competitive auctions and digital goods[C]// ACM-SIAM Symposium on Discrete Algorithms(SODA-10),January 17-19,2010,Austin,USA.[S.l.:s.n.], 2001: 735-744.
[46]	GOLDBERG A V , HARTLINE J D , KARLIN A R ,et al. Competitive auctions[J]. Games and Economic Behavior, 2006,55(2): 242-269.
[47]	CHEN N , GRAVIN N , LU P . Optimal competitive auctions[C]// 46th Annual ACM Symposium on Theory of Computing(STOC-14),May 31-June 3,2014,New York,USA.[S.l.:s.n.], 2014: 253-262.
[48]	LAVI R , NISAN N . Competitive analysis of incentive compatible on-line auctions[J]. Theoretical Computer Science, 2000,310(1): 159-180.
[49]	LAVI R , NISAN N . Online ascending auctions for gradually expiring items[C]// 6th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA-05),January 23-25,2005,Vancouver,Canada. Philadelphia:Society for Industrial and Applied Mathematics, 2005: 1146-1155.
[50]	FRIEDMAN E J , PARKES D C . Pricing wifi at starbucks:issues in online mechanism design[C]// 4th ACM Conference on Electronic Commerce(EC-03),June 9 - 12,2003,San Diego,CA,USA. New York:ACM Press, 2003: 240-241.
[51]	MASHAYEKHY L , NEJAD M M , GROSU D ,et al. An online mechanism for resource allocation and pricing in clouds[J]. IEEE Transactions on Computers, 2016,65(4): 1172-1184.
[52]	MYERSON R B . Optimal auction design[J]. Mathematics of Operations Research, 1981,6(1): 58-73.
[53]	唐平中 . 计算经济学与最优机制设计问题[J]. 中国计算机学会通讯, 2013,9(10): 18-23.
	TANG P Z . Computational economics and optimal mechanism design[J]. Communications of the CCF, 2013,9(10): 18-23.
[54]	SUN J , QU H , CHAKRABARTI D ,et al. Neighborhood formation and anomaly detection in bipartite graphs[C]// 5th IEEE International Conference on Data Mining(ICDM-05),Nov 27-30,2005,Houston,TX,USA. New Jersey:IEEE Press, 2005: 1-8.
[55]	RAZ O , KOOPMAN P , SHAW M . Semantic anomaly detection in online data sources[C]// 24rd International Conference on Software Engineering(ICSE-02),May 25,2002,Orlando,USA. New Jersey:IEEE Press, 2002: 302-312.
[56]	汤琪 . 大数据交易中的产权问题研究[J]. 图书与情报, 2016(4): 38-45.
	TANG Q . Study on the property right issues in big data trade[J]. Library and Information, 2016(4): 38-45.
[57]	REICHMAN J H , SAMUELSON P . Intellectual property rights in data[J]. Vanderbilt Law Review, 1997,50(4): 337-348.

Metrics

Recommended 0

No Suggested Reading articles found!

Discussions of the value expectations of big data

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 2

References 57

Related Articles 15

Metrics

Recommended 0

[1]	Haihong QIAN, Maoyi WANG, Yun XIONG. Digital transformation in higher education:a systematic review [J]. Big Data Research, 2023, 9(3): 56-70.
[2]	Hong MEI, Xiaoyong DU, Hai JIN, Xueqi CHENG, Yunpeng CHAI, Xuanhua SHI, Xiaolong JIN, Yasha WANG, Chi LIU. Big data technologies forward-looking [J]. Big Data Research, 2023, 9(1): 1-20.
[3]	Yang SHEN, Menglong YU. Metaverse and big data: data insight and value connection in spatio-temporal intelligence [J]. Big Data Research, 2023, 9(1): 103-110.
[4]	Tongzheheng ZHENG, Bin LI, Minxuan FENG, Bolin CHANG, Dongbo WANG. Explore the structuration of historical books:the construction and quantitative analysis of digital humanities database of the Biographies of the Shiji [J]. Big Data Research, 2022, 8(6): 40-55.
[5]	Jing CHEN. Humanities big data and its application in the field of digital humanities [J]. Big Data Research, 2022, 8(6): 3-14.
[6]	Yuchu LUO, Hao WU, Yuhan GUO, Shaocong TAN, Can LIU, Ruike JIANG, Xiaoru YUAN. Visualization in digital humanities [J]. Big Data Research, 2022, 8(6): 74-93.
[7]	Wenlong LI, Yuan YUAN, Xiaopeng AN. Modus operandi of big data governance: some preliminary observations [J]. Big Data Research, 2022, 8(4): 34-45.
[8]	Qifeng TANG, Zhiqing SHAO, Yazhen YE. Authenticating and licensing architecture of data rights in data trade [J]. Big Data Research, 2022, 8(3): 40-53.
[9]	Chenhuizi WANG, Wei CAI. Digital economics in metaverse: state-of-the-art, characteristics, and vision [J]. Big Data Research, 2022, 8(3): 140-150.
[10]	Mei YANG, Wei LI, Siyuan QIAO, Wei LIU. Research on calculation method of China’s big data industry output value [J]. Big Data Research, 2022, 8(3): 151-160.
[11]	Deren LI, Guo ZHANG, Yonghua JIANG, Xin SHEN, Weiling LIU. Opportunities and challenges of geo-spatial information science from the perspective of big data [J]. Big Data Research, 2022, 8(2): 3-14.
[12]	Xiaolan QIU, Yuxin HU, Songtao SHANGGUAN, Kun FU. Remote sensing satellite big data high-recision integration processing technology [J]. Big Data Research, 2022, 8(2): 15-27.
[13]	Weiquan LIU, Cheng WANG, Yu ZANG, Qian HU, Shangshu YU, Baiqi LAI. A survey on information extraction technology based on remote sensing big data [J]. Big Data Research, 2022, 8(2): 28-57.
[14]	Jianqiang LIU, Xiaomin YE, Youguo LAN. Remote sensing big data from Chinese ocean satellites and its application service [J]. Big Data Research, 2022, 8(2): 75-88.
[15]	Hequn YANG, Xiaofeng WANG, Yanqing GAO, Yiwen LU, Bingxin MA, Xinyao WANG. Analysis of satellite big data requirements in numerical weather prediction [J]. Big Data Research, 2022, 8(2): 89-102.