Name
Abstract | ||
|---|---|---|
With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBDATA.2016.2601934 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Big data,Econometrics,Feature extraction,Distributed databases,Analytical models,Economic indicators | Data science,Data mining,Data set,Feature selection,Computer science,Economic indicator,Econometric model,Feature extraction,Economic data,Distributed database,Big data | Journal |
Volume | Issue | ISSN |
4 | 2 | 2332-7790 |
Citations | PageRank | References |
5 | 0.41 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Zhao | 1 | 49 | 2.65 |
| Zhikui Chen | 2 | 692 | 66.76 |
| Yueming Hu | 3 | 17 | 7.92 |
| Geyong Min | 4 | 2089 | 224.70 |
| Jiang Zhaohua | 5 | 5 | 0.75 |