Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/info10040137 | INFORMATION |
Keywords | Field | DocType |
scientific big data, consistency degree, creep testing, data consistency | Data mining,Creep testing,Computer science,Weak consistency,Test data,Strong consistency,Big data,Semantics,Data consistency | Journal |
Volume | Issue | ISSN |
10 | 4 | 2078-2489 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Shi | 1 | 20 | 5.79 |
| Yulin Cui | 2 | 0 | 0.34 |
| Kangming Xu | 3 | 0 | 0.34 |
| Mingmei Zhang | 4 | 0 | 0.34 |
| Lianhong Ding | 5 | 11 | 4.98 |