• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.1-11
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• 2010

Many applications of DSMS(Data Stream Management System) require not only to process real-time stream data efficiently but also to provide high quality services such as data mining and data warehouse combining with DBMS(Database Management System) to users. In this paper we execute the performance benchmark of the combined system of DSMS and DBMS that is developed for high quality services. We use the stream data of network monitoring application system and combine the traditional representative DSMSs and DBMSs in a single system for the performance testing. We develop the total performance benchmark tool implementing JAVA language for the our testing. For our performance testing, we combine DSMS such as STREAM and Coral8 and DBMS such MySQL and Oracle10g respectively.

• KEPCO Journal on Electric Power and Energy
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• v.3 no.1
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• pp.9-16
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• 2017

The performance test is conducted for the purpose of determining the accurate thermal performance of the power generation facility or deriving the factors of thermal efficiency degradation. Compared to the acquisition method of power plant thermal performance test data by compensating cable or transmission cable, performance test using wireless instrument can acquire digital data in order to shorten the period due to installation and demolition of instrument and enhance safety of workers and relatively accurate data can be acquired thereby improving work efficiency. Wireless instruments have already been introduced to the market a long time ago, and some of them are used in industry such as petrochemical industry. However, there is no example which has been conducted for performance test of power generation facilities. In order to apply power generation facilities, a reliable system capable of acquiring performance data smoothly without affecting the control system is required. The wireless measurement system can eliminate the measurement defects and errors such as the damage due to the movement of the connecting cable, the extension due to the extension of the shield wire, the contact failure at the contact point between the measuring sensor and the connecting wire, This method has the advantage of collecting relatively accurate performance test data.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.8
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• pp.29-36
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• 2022

The objective of this research is to find out the effect of innovative culture and quality of work life on competitive advantage strategy with the mediation of individual performance. This research is the continuance of previous research conducted by Adya Hermawati with an originality aspect emphasizing a concept comprising innovative culture, quality of work life, and individual performance as factors that control competitive advantage strategy. The research subject is Tourism Industry SMEs. Explanatory research is a research method used in this study, by surveying respondents. The data sources in this research are primary and secondary. Primary data is collected from respondents directly through a questionnaire whereas secondary data are obtained from references that are relevant to research problems. In conformity with this explanation, the type of research data is quantitative data. The results of this research show that: innovative culture has an effect on individual performance, quality of work life affects individual performance, innovative culture has an effect on competitive advantage, quality of work life affects competitive advantage, individual performance has an effect on competitive advantage, innovative culture affects competitive advantage with the mediation of individual performance, and quality of work life affects competitive advantage with the mediation of individual performance.

• Smart Media Journal
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• v.10 no.1
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• pp.108-115
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• 2021

This study investigated the relationships between the analytics capability and value of big data and business performance for big data analysts of business organizations. The values that big data can bring were categorized into transactional value, strategic value, transformational value, and informational value, and we attempted to verify whether these values lead to business performance. Two hundred samples from employees with experience in big data analysis were collected and analyzed. The hypotheses were tested with a structural equation model, and the capability of big data analytics was found to have a significant effect on the value and business performance of big data. Among the big data values, transactional value, strategic value, and transformational value had a positive effect on business performance, but the impact of informational value has not been proven. The results of this study are expected to provide useful information to business organizations seeking to achieve business performance using big data.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.35-40
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• 2009

Many researchers strive to research on ways on how to improve the performance of RFID system and many papers were written to solve one of the major drawbacks of potent technology related with data management. As RFID system captures billions of data, problems arising from dirty data and large volume of data causes uproar in the RFID community those researchers are finding ways on how to address this issue. Especially, effective data management is important to manage large volume of data. Data reduction techniques in attempts to address the issues on data are also presented in this paper. This paper introduces readers to a new data reduction algorithm that might be an alternative to reduce data in RFID Systems. A process on how to extract data from the reduced database is also presented. Performance study is conducted to analyze the new data reduction algorithm. Our performance analysis shows the utility and feasibility of our categorization reduction algorithms.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.109-121
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• 2023

This study aims to address the literature gap by examining the direct relationship between big data analytics capability, marketing innovation, and organizational innovations. Additionally, this study would examine big data analytics capability as the antecedent for both innovation types and how these relationships influence firm performance. The research model is developed based on the integration of resource-based view and knowledge-based view theories. The quantitative method is used as the research methodology for this study. Based on a purposive sampling method, a total of 115 questionnaires were obtained from managers in star-rated hotels located in Malaysia. Partial least square structural equation modeling (PLS-SEM) is utilized for the data analysis. The result shows that big data analytics capability positively affects marketing and organizational innovations. The findings show that big data analytics capability and organizational innovation positively influence firm performance. Nonetheless, the result revealed that marketing innovation is not positively related to firm performance. The findings also indicate to hotel managers the importance of big data analytic capability and the resources required to build and develop this capability. The contributions from this study enrich the literature on big data and innovation, which is particularly limited in the hospitality and tourism context.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.293-298
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• 2017

Recently, much research has been devoted to implementing and provisioning high-performance computing environment using clusters with multiple computers and high-performance networking technologies. In-memory based Key-Value stores, such as Redis or Memcached, are widely used in high performance cluster environments to improve the data processing performance. We can distribute data at different storage nodes, and each computing node can access it at a high speed using these In-memory based Key-Value stores. InfiniBand is a de-facto technology that is widely used to interconnect each node of a cluster. In this paper, we propose a new data pre-fetching scheme for Key-Value store based on high performance clusters to improve the performance. The proposed scheme utilizes the data transfer characteristics of InfiniBand. The results of the simulation show that the proposed scheme can reduce the data transfer time by up to about 28%.

• Journal of Distribution Science
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• v.16 no.9
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• pp.53-58
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• 2018

Purpose - This study aims to examine how the time variations of customer satisfaction influence retail firms' performance. Research design, data, and methodology - The study employs yearly time series customer satisfaction data of Korean retail secured from the National Customer Satisfaction Index(NCSI) for the 2011~2016 period. Our data includes a total of 90 observations of 15 retail firms in 5 different sector(department store, filling station, large discount store, open market, TV home shopping). We obtained the firm performance data from the KIS Value database. The variables for financial performance include sales and net profit. Results - The results show that customer satisfaction has dynamic effects on retail firms' performance. More specifically, the time variation of customer satisfaction has the moderating effect on the linkage between customer satisfaction and financial performance as well as direct effects on the firms' financial performance. Conclusions - Customer satisfaction has the current effect lasting over time on firm performance and changes of customer satisfaction in positive direction also impact on firm performance. Retail firms need to not only focus on improving customer satisfaction in the current term, but make efforts to continuously enhance customer satisfaction in the long term.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Knowledge Management Research
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• v.21 no.4
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• pp.243-263
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• 2020

The purpose of this study is to propose the performance measurement model for open big data platform. In order to develop the performance measurement model, we have integrated big data reference architecture(NIST 2018) with performance prism model(Neely et al. 2001) in the platform perspective of open big data. Our proposed model consists of five key building blocks for measuring performance of open data platform as follows: stakeholder contribution, big data governance capabilities, big data service capabilities, big data IT capabilities, and stakeholder satisfaction. In addition, our proposed model have twenty four evaluation indices and seventy five measurement items. We believe that our model could offer both research and practical implications for relevant research.