Abstract
We are faced with a torrent of data generated and captured in digital form as a result of the advancement of sciences, engineering and technologies, and various social, economical and human activities. This big data phenomenon ushers in a new era where human endeavors and scientific pursuits will be aided by not only human capital, and physical and financial assets, but also data assets. Research issues in big data and big data analysis are embedded in multi-dimensional scientific and technological spaces. In this paper, we first take a close look at the dimensions in big data and big data analysis, and then focus our attention on the issue of inconsistencies in big data and the impact of inconsistencies in big data analysis. We offer classifications of four types of inconsistencies in big data and point out the utility of inconsistency-induced learning as a tool for big data analysis.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
