메뉴 건너뛰기




Volumn 32, Issue 3, 2015, Pages 200-209

Augmenting Data Warehouses with Big Data

Author keywords

big data; data warehouses; databases; MapReduce

Indexed keywords

DATA WAREHOUSES; DATABASE SYSTEMS;

EID: 84933513051     PISSN: 10580530     EISSN: None     Source Type: Journal    
DOI: 10.1080/10580530.2015.1044338     Document Type: Article
Times cited : (46)

References (19)
  • 1
    • 84907580576 scopus 로고    scopus 로고
    • Editorial—Big data, data science, and analytics: The opportunity and challenge for is research
    • R.Agarwal, & V.Dhar, (2014). Editorial—Big data, data science, and analytics: The opportunity and challenge for is research. Information Systems Research, 25(3), 443–448. doi:10.1287/isre.2014.0546
    • (2014) Information Systems Research , vol.25 , Issue.3 , pp. 443-448
    • Agarwal, R.1    Dhar, V.2
  • 2
    • 84933541704 scopus 로고    scopus 로고
    • The missing V’s in big data: Viability and value. Wired. Retrieved January 10, 2015, from
    • N.Biehn, (2013). The missing V’s in big data: Viability and value. Wired. Retrieved January 10, 2015, from http://www.wired.com
    • (2013)
    • Biehn, N.1
  • 5
    • 84933541705 scopus 로고    scopus 로고
    • Will data warehousing survive the advent of big data? O’Reilly Radar. Retrieved January 10, 2015, from
    • B.Devlin, (2012). Will data warehousing survive the advent of big data? O’Reilly Radar. Retrieved January 10, 2015, from http://radar.oreilly.com
    • (2012)
    • Devlin, B.1
  • 8
    • 84933541707 scopus 로고    scopus 로고
    • Big data and is research
    • P.B.Goes, (2014). Big data and is research. MIS Quarterly, 38(3), iii–viii.
    • (2014) MIS Quarterly , vol.38 , Issue.3
    • Goes, P.B.1
  • 9
    • 84933541708 scopus 로고    scopus 로고
    • Big data: Avoid ‘Wanna V’ confusion. Information Week. Retrieved January 10, 2015
    • S.Grimes, (2013). Big data: Avoid ‘Wanna V’ confusion. Information Week. Retrieved January 10, 2015, http://www.informationweek.com
    • (2013)
    • Grimes, S.1
  • 10
    • 84889594536 scopus 로고    scopus 로고
    • Linked data, big data, and the 4th paradigm
    • P.Hitzler, & K.Janowicz, (2013). Linked data, big data, and the 4th paradigm. Semantic Web, 4, 233–235.
    • (2013) Semantic Web , vol.4 , pp. 233-235
    • Hitzler, P.1    Janowicz, K.2
  • 13
    • 84933541709 scopus 로고    scopus 로고
    • New emerging best practices for big data—A Kimball group white paper. Kimball Group. Retrieved January 10, 2015, from
    • R.Kimball, (2012). New emerging best practices for big data—A Kimball group white paper. Kimball Group. Retrieved January 10, 2015, from http://www.kimballgroup.com
    • (2012)
    • Kimball, R.1
  • 14
    • 84933541710 scopus 로고    scopus 로고
    • The 5 V’s of big data. Avnet Technology Solutions. Retrieved January 10, 2015, from
    • E.Knilans, (2014). The 5 V’s of big data. Avnet Technology Solutions. Retrieved January 10, 2015, from http://www.ats.avnet.com
    • (2014)
    • Knilans, E.1
  • 15
    • 84861113715 scopus 로고    scopus 로고
    • 3-D data management: Controlling data volume, velocity, and variety
    • Stamford, CT: META Group Inc
    • D.Laney, (2001). 3-D data management: Controlling data volume, velocity, and variety. META Group Report, File 949, February 2001. Stamford, CT: META Group Inc.
    • (2001) META Group Report, File 949, February 2001
    • Laney, D.1
  • 16
    • 85006605524 scopus 로고    scopus 로고
    • The modern data warehouse—How big data impacts analytics architecture
    • K.Lopez, & J.D’Antoni, (2014). The modern data warehouse—How big data impacts analytics architecture. BI Journal, 19(3), 8–15.
    • (2014) BI Journal , vol.19 , Issue.3 , pp. 8-15
    • Lopez, K.1    D’Antoni, J.2
  • 17
    • 84871857828 scopus 로고    scopus 로고
    • Big data: The management revolution
    • A.McAfee, & E.Brynjolfsson, (2012). Big data: The management revolution. Harvard Business Review, 90(10), 61–68.
    • (2012) Harvard Business Review , vol.90 , Issue.10 , pp. 61-68
    • McAfee, A.1    Brynjolfsson, E.2
  • 18
    • 84933541712 scopus 로고    scopus 로고
    • Beyond volume, variety, and velocity is the issue of big data veracity. Inside Big Data. Retrieved January 10, 2015, from
    • K.Normandeau, (2013). Beyond volume, variety, and velocity is the issue of big data veracity. Inside Big Data. Retrieved January 10, 2015, from http://insidebigdata.com
    • (2013)
    • Normandeau, K.1
  • 19
    • 84933541713 scopus 로고    scopus 로고
    • Gartner’s big data definition consists of three parts, not to be confused with three “V”s. Forbes. Retrieved January 10, 2015, from
    • S.Sicular, (2013). Gartner’s big data definition consists of three parts, not to be confused with three “V”s. Forbes. Retrieved January 10, 2015, from http://www.forbes.com
    • (2013)
    • Sicular, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.