2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Liu Yan

The development of international agriculture trade during the COVID-19 pandemic has encountered significant challenges. The processing of international agricultural trade data using machine learning techniques needs to be improved to perform effective analysis of agricultural trade. An essential issue for international agricultural trade is the accurate yield estimation for the numerous crops involved in international trade. Data mining techniques are the necessary approach for accomplishing practical and effective solutions for this problem. This paper combined the bidirectional encoder representations from transformers (BERT) model to conduct data mining and developed a trade data analysis system with efficient data analysis capabilities. Our results indicate that our model does reasonably well and obtains adequate information in deciding international agricultural trade. It can also be instrumental for policy and decision-making regarding international agricultural trade.


2021 ◽  
Vol 5 (4) ◽  
pp. 23-26
Author(s):  
Ning Yang

Enterprise Business Intelligence (BI) system refers to data mining through the existing database of the enterprise, and data analysis according to customer requirements through comprehensive processing. The data analysis efficiency is high and the operation is convenient. This paper mainly analyzes the application of enterprise BI data analysis system in enterprises.


2019 ◽  
Vol 110 ◽  
pp. 02007
Author(s):  
Pavel Kagan

The paper studies the processing of large information data arrays (Big Data) in construction. The issues of the applicability of the big data concept (Big Data) at various stages of the life cycle of buildings and structures are considered. Methods for data conversion for their further processing are proposed. The methods used in the analysis of "big data" allow working with unstructured data sets (Data Mining). An approach is considered, in which the analysis of arbitrary data can be reduced to text analysis, similar to the analysis of ordinary text messages. At the moment, it is important and interesting to isolate non-obvious links present in the analysed data. The advantage of using big data is that it is not necessary to advance hypotheses for testing. Hypotheses appear during data analysis. Dependence analysis is a basic approach when working with big data. The concept of an automatic big data analysis system is proposed. For data mining, text analysis algorithms should be used, and discriminant functions should be used for the main problem to be solved (data classification).


2018 ◽  
Vol 89 (10) ◽  
pp. 10K114 ◽  
Author(s):  
M. C. Thompson ◽  
T. M. Schindler ◽  
R. Mendoza ◽  
H. Gota ◽  
S. Putvinski ◽  
...  

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