Big data challenge: a data management perspective

被引:181
作者
Chen, Jinchuan [1 ]
Chen, Yueguo [1 ]
Du, Xiaoyong [1 ]
Li, Cuiping [1 ]
Lu, Jiaheng [1 ]
Zhao, Suyun [1 ]
Zhou, Xuan [1 ]
机构
[1] Renmin Univ China, Key Lab Data Engn & Knowledge Engn, Sch Informat, Beijing 100872, Peoples R China
基金
北京市自然科学基金;
关键词
big data; performance; databases; PROVENANCE;
D O I
10.1007/s11704-013-3903-7
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
There is a trend that, virtually everyone, ranging from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either already experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new opportunities and great untapped value. This paper reviews big data challenges from a data management respective. In particular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and finally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.
引用
收藏
页码:157 / 164
页数:8
相关论文
共 34 条
[1]  
Aggarwal CC, 2010, ADV DATABASE SYST, V40, P1, DOI 10.1007/978-1-4419-6045-0
[2]   Foundations of Uncertain-Data Integration [J].
Agrawal, Parag ;
Das Sarma, Anish ;
Ullman, Jeffrey ;
Widom, Jennifer .
PROCEEDINGS OF THE VLDB ENDOWMENT, 2010, 3 (01) :1080-1090
[3]  
Aguilera MK, 2008, PROC VLDB ENDOW, V1, P598
[4]  
[Anonymous], 2010, SIGMOD Conference
[5]  
[Anonymous], P 2010 ACM SIGMOD IN, DOI [DOI 10.1145/1807167.1807184, 10.1145/1807167.1807184]
[6]  
[Anonymous], 2012, SIGMOD, DOI DOI 10.1145/2213836.2213946
[7]  
[Anonymous], 2008, P 2008 ACM SIGMOD IN
[8]  
[Anonymous], 2009, Proc. VLDB Endow., DOI DOI 10.14778/1687627.1687674
[9]   Accessing the deep Web [J].
IBM Almaden Research Center, San Jose, CA ;
不详 ;
不详 ;
不详 .
Commun ACM, 2007, 5 (94-101) :94-101
[10]  
BRADSKI G., 2007, NIPS, P281