Deeper excavation of relevance of data and a top-down thinking to take apart financial data into blocks for more efficient analysis are essential for the big data, as well as to eliminate data noise and to remove data redundancy in the process1. The financial data classification standard, which always performs excellently in these aspects, is an essential premise for data mining and analysis in the big data2. To find a method to form the classification standard framework that can meet diverse purposes is very important. This research proposes a way to form the framework of financial data classification standard based on uniform classification standard and relative books, improved by comparing with classification standard of existed financial database and verified by practice with financial data sources. This framework can adapt to trends in the era of the big data and improve data storage mode.
Yang, S., Guo, K., Li, J., Zhong, Y., Liu, R., & Feng, Z. (2014). Framework formation of financial data classification standard in the era of the big data. In Procedia Computer Science (Vol. 30, pp. 88–96). Elsevier B.V. https://doi.org/10.1016/j.procs.2014.05.385