A Descriptive Study on Data Profiling: Focusing on Attribute Value Quality Index
Companies are concentrating on securing artificial intelligence (AI) technology in the era of the Fourth Industrial Revolution to increase their productivity through machine learning, which is AI’s core technology, and to allow computers to acquire a high level of quality data through self-learning. Securing big data of good quality is becoming a very significant asset for businesses to boost their competitiveness. It is anticipated that the amount of digital information will expand rapidly around the world, reaching 90 zettabytes (ZB) by 2020. The value quality index on and data attribute is very important to present as it can be beneficial to determine the data quality for a user with regard to whether the data is acceptable for use from the point of view of the user. As a consequence, this helps the user to decide whether or not the data is taken on the basis of the data quality index. In this analysis, we propose a Model calculation of the quality index with structured and unstructured data, as well as the attribute value quality index (AVQI) and structured data value quality index (SDVQI) calculation process. Using the attribute value quality index (AVQI), SDVQI was measured. As unstructured data increases, the estimation of the unstructured data quality index is expected to be useful for assessing the utility of unstructured data. We expect to finish the data profiling model using neural network and statistical analysis (DPNS) in the future.
Author (s) Details
Department of Intellectual Property for Startups, Catholic Kwandong University, 24, Beomil-ro 579, Gangneung-si, Gangwondo 25601, Korea.
Department of IT Policy Management, Soongsil University, Sangdo-dong, Dongjak-gu, Seoul 06978, Korea.
Department of IT Policy Management, Soongsil University, Sangdo-dong, Dongjak-gu, Seoul 06978, Korea. He received his bachelor’s degree of Business Administration in University of Seoul, Seoul (1995) and master’s degree (2002), doctor’s degree of Computer Science in Soongsil University, Seoul (2006). Now, he is working as a professor in the Startup Support Foundation, Soongsil University, Seoul, Korea. His research interests focus on Software Engineering, and Open Source Software.
Department of Business Administration, Soongsil University, Sangdo-dong, Dongjak-gu, Seoul 06978, Korea.
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