Yeonjin Ju, Yoosin Kim, Seung Ryul Jeong, Journal of Internet Computing and Services, Vol. 21, No. 1, pp. 127-135, Feb. 2020
10.7472/jksii.2020.21.1.127, Full Text:
Keywords: Equipment Ledger, Unstructured Data, Telecom Domain, Anomaly Detection

Abstract

As the importance of the use and analysis of big data is emerging, there is a growing interest in natural language processing techniques for unstructured data such as news articles and comments. Particularly, as the collection of big data becomes possible, data mining techniques capable of pre-processing and analyzing data are emerging. In this case study with a telecom company, we propose a methodology how to formalize unstructured data using text mining. The domain is determined as equipment failure and the data is about 2.2 million equipment check ledger data. Data on equipment failures by 800,000 per year is accumulated in the equipment check ledger. The equipment check ledger coexist with both formal and unstructured data. Although formal data can be easily used for analysis, unstructured data is difficult to be used immediately for analysis. However, in unstructured data, there is a high possibility that important information. Because it can be contained that is not written in a formal. Therefore, in this study, we study to develop digital transformation method for unstructured data in equipment check ledger.

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