• Journal of Information Management
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• v.38 no.1
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• pp.75-98
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• 2007

Each domain of web services increased in geometrical progression by remarkable growth of the Internet serves various services or functions, and earns an income. And all services are divided by individual goal in each domain. What counts is that we must offer a personal data, our human resource information to use web service in the majority of cases. Otherwise we have to act under constraint in using the many web services. In this paper, we analyze the database structure or schema for managing human resource information from several web sites or service demands, and propose an agent design and implementation method for preventing duplication input of personal human resource information and sharing the human resource data.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.