• Management Science and Financial Engineering
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• v.11 no.3
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• pp.19-45
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• 2005

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

• Journal of Computing Science and Engineering
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• v.2 no.4
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• pp.394-411
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• 2008

The data in automation and control systems can be achieved not only from different field devices but also from different OPC (OLE for Process Control) servers. However, current OPC clients can only read and decode the simple data from OPC servers. They will have some problems to acquire structured data and exchange the structured data. In addition to the large network control systems, the OPC clients can read, write, and subscribe to thousands of data points from/to OPC servers. Due to that, the most important factor for building a high performance and scalable industrial control system is the ability to transfer the process data between server and client in the most efficient and fastest way. In order to solve these problems, we propose a means to implement the OPC DA (Data Access) server supporting the OPC complex data, so that the OPC DA clients are able to read and decode any type of data from OPC servers. We also propose a method for caching the process data in large industrial control systems to overcome the limitation of performance of the pure OPC DA system. The performance analysis and discussion indicate that the proposed system has an acceptable performance and is feasible in order for applying to real-time industrial systems today.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.359-360
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• 2023

As data management and analysis technology advances, there is active discussion on how to utilize data generated in construction projects. Among them, the materials produced during the supervision work are highly useful because their generation cycle and format are regulated according to relevant laws. In this study, we analyzed whether the data produced during the supervision work in the construction phase of apartment housing can be utilized for project performance management. First, this study identified key data necessary for performance management through FGI with experts in the field of apartment housing. Next, we collected supervisory data from the case project and identified whether the data was generated, its cycle, and storage format. As a result of the analysis, the supervisory data contained various information that could measure the performance of construction projects and had the advantage of standardized data. In the future, utilizing supervisory data is expected to enable effective performance management of apartment housing construction projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.235-250
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• 2009

An understanding of performance of database driven network applications is critical in combating slow performance of e-commerce web sites, besides efficient web page design, and high quality and well-maintained web server equipment. This paper describes a method of measuring performance from the user viewpoint, which can help enormously in making realistic assessment of true performance of database driven applications. The performance measurements were taken at user locations by using several specially designed JavaScript functions along with ASP scripts. A performance study is presented in this paper, comparing performance of data access using stored procedures with the traditional way of querying a database. It is generally believed that stored procedures have performance benefits as they are pre-compiled. However, our study shows that the data access approach using stored procedures provides significant benefits(by about 30%) over the traditional approach for querying a commercial MySQL database, only when retrieving a substantial amount of data(at least 10,000 rows of data).

• Journal of the Korean Society of Propulsion Engineers
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• v.13 no.6
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• pp.1-7
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• 2009

The gas turbine engine performance is greatly relied on its component performance characteristics. Generally, acquisition of component maps is not easy for engine purchasers because it is an expensive intellectual property of gas turbine engine supplier. In the previous work, the maps were inversely generated from engine performance deck data, but this method is limited to obtain the realistic maps due to calculated performance deck data. Therefore this work proposes newly to generate more realistic compressor map from experimental performance test data. And then a realistic compressor map can be generated form those processed data using the proposed extended scaling method at each rotational speed. Evaluation can be made through comparison between performance analysis results using the performance simulation program including the generated compressor map and on-condition monitoring performance data.

• Journal of Distribution Science
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• v.22 no.8
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• pp.65-76
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• 2024

Purpose: The study aims to recognize the relationship between big data analytics capabilities, big data analytics process, and perceived business performance from supply chain management to total customer satisfaction. Research design, data and methodology: The study followed a quantitative approach with a descriptive design. The data was collected from leading e-commerce companies in India using a structured questionnaire, and the data was coded and decoded using MS Excel, SPSS, and R language. It was further tested using Cronbach's alpha, KMO, and Bartlett's test for reliability and internal consistency. Results: The results showed that the big data analytics process acts as a robust mediator between big data analytics capabilities and perceived business performance. The 'direct, indirect and total effect of the model' and 'PLS-SEM model' showed that the big data analytics process directly impacts business performance. Conclusions: A complete indirect relationship exists between big data analytics capabilities and perceived business performance through the big data analytics process. The research contributesto e-commerce companies' understanding of the importance of big data analytics capabilities and processes.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.153-164
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• 2012

There is a belief that timely and precise data are important to decisions and the better decisions are related to better firm performance. However, empirical research investigating the effect of data quality on firm financial performance is still scarce up to recently. Current study empirically explores such an effect of data quality on firm accounting performance in the Korean manufacturing industry during 2008~2010 with secondary data. The results show that better data quality does not impact on sales and operating profit, but positively and significantly impacts on EVA(Economic Value Added). Raising the level of data quality management maturity by one level can increase EVA by about 34% in manufacturing firms.

• Nuclear Engineering and Technology
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• v.37 no.5
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• pp.491-502
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• 2005

Reliable human performance is a prerequisite in securing the safety of complicated process systems such as nuclear power plants. However, the amount of available knowledge that can explain why operators deviate from an expected performance level is so small because of the infrequency of real accidents. Therefore, in this study, a database that contains a set of useful information extracted from simulated emergencies was developed in order to provide important clues for understanding the change of operators' performance under stressful conditions (i.e., real accidents). The database was developed under Microsoft Windows TM environment using Microsoft Access $97^{TM}$ and Microsoft Visual Basic $6.0^{TM}$. In the database, operators' performance data obtained from the analysis of over 100 audio-visual records for simulated emergencies were stored using twenty kinds of distinctive data fields. A total of ten kinds of operators' performance data are available from the developed database. Although it is still difficult to predict operators' performance under stressful conditions based on the results of simulated emergencies, simulation studies remain the most feasible way to scrutinize performance. Accordingly, it is expected that the performance data of this study will provide a concrete foundation for understanding the change of operators' performance in emergency situations.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.112-121
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• 2016

This paper focuses on competencies of data scientists and behavioral intention that affect big data analysis performance. This experiment examined nine core factors required by data scientists. In order to investigate this, we conducted a survey to gather data from 103 data scientists who participated in big data competition at Kaggle platform and used factor analysis and PLS-SEM for the analysis methods. The results show that some key competency factors have influential effect on the big data analysis performance. This study is to provide a new theoretical basis needed for relevant research by analyzing the structural relationship between the individual competencies and performance, and practically to identify the priorities of the core competencies that data scientists must have.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.749-758
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• 2019

This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.