%0 Journal Article
	%A Qi Xiu and  Hiromu Hota and  Yohsuke Ishii and  Takuya Oda
	%D 2017
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 123, 2017
	%T Data Quality Enhancement with String Length Distribution
	%U https://publications.waset.org/pdf/10006693
	%V 123
	%X Recently, collectable manufacturing data are rapidly
increasing. On the other hand, mega recall is getting serious as
a social problem. Under such circumstances, there are increasing
needs for preventing mega recalls by defect analysis such as
root cause analysis and abnormal detection utilizing manufacturing
data. However, the time to classify strings in manufacturing data
by traditional method is too long to meet requirement of quick
defect analysis. Therefore, we present String Length Distribution
Classification method (SLDC) to correctly classify strings in a short
time. This method learns character features, especially string length
distribution from Product ID, Machine ID in BOM and asset list.
By applying the proposal to strings in actual manufacturing data, we
verified that the classification time of strings can be reduced by 80%.
As a result, it can be estimated that the requirement of quick defect
analysis can be fulfilled.
	%P 371 - 378