Inconsistencies in big data

Author

Du Zhang

Author_Institution

Dept. of Comput. Sci., California State Univ., Sacramento, CA, USA

fYear

2013

fDate

16-18 July 2013

Firstpage

61

Lastpage

67

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.

Keywords

data analysis; learning (artificial intelligence); big data analysis; big data dimensions; big data inconsistencies issue; big data phenomenon; data asset; financial asset; human capital; human endeavors; inconsistency-induced learning; physical asset; scientific pursuits; Cognition; Data handling; Data storage systems; Information management; Media; Medical services; Semantics; big data; big data analysis; inconsistencies in big data; inconsistency-induced learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Cognitive Informatics & Cognitive Computing (ICCI*CC), 2013 12th IEEE International Conference on

Conference_Location

New York, NY

Print_ISBN

978-1-4799-0781-6

Type

conf

DOI

10.1109/ICCI-CC.2013.6622226

Filename

6622226