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Data Consistency Theory and Case Study for Scientific Big Data

1
National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
2
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
3
School of Information, Beijing Wuzi University, Beijing 101149, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(4), 137; https://doi.org/10.3390/info10040137
Received: 21 March 2019 / Revised: 3 April 2019 / Accepted: 8 April 2019 / Published: 12 April 2019
(This article belongs to the Special Issue Big Data Analytics and Computational Intelligence)
Big data technique is a series of novel technologies to deal with large amounts of data from various sources. Unfortunately, it is inevitable that the data from different sources conflict with each other from the aspects of format, semantics, and value. To solve the problem of conflicts, the paper proposes data consistency theory for scientific big data, including the basic concepts, properties, and quantitative evaluation method. Data consistency can be divided into different grades as complete consistency, strong consistency, weak consistency, and conditional consistency according to consistency degree and application demand. The case study is executed on material creep testing data. The analysis results show that the theory can solve the problem of conflicts in scientific big data. View Full-Text
Keywords: scientific big data; consistency degree; creep testing; data consistency scientific big data; consistency degree; creep testing; data consistency
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Shi, P.; Cui, Y.; Xu, K.; Zhang, M.; Ding, L. Data Consistency Theory and Case Study for Scientific Big Data. Information 2019, 10, 137.

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