• Title/Summary/Keyword: Performance Data

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The Adoption of Big Data to Achieve Firm Performance of Global Logistic Companies in Thailand

  • KITCHAROEN, Krisana
    • Journal of Distribution Science
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    • v.21 no.1
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    • pp.53-63
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    • 2023
  • Purpose: Big Data analytics (BDA) has been recognized to improve firm performance because it can efficiently manage and process large-scale, wide variety, and complex data structures. This study examines the determinants of Big Data analytics adoption toward marketing and financial performance of global logistic companies in Thailand. The research framework is adopted from the technology-organization-environment (TOE) model, including technological factors (relative advantages), organizational factors (technological infrastructure and absorptive capability), environmental factors (industry competition and government support), Big Data analytics adoption, marketing performance, and financial performance. Research design, data, and methodology: A quantitative method is applied by distributing the survey to 450 employees at the manager's level and above. The sampling methods include judgmental, stratified random, and convenience sampling. The data were analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The results showed that all factors significantly influence Big Data analytics adoption, except technological infrastructure. In addition, Big Data analytics adoption significantly influences marketing and financial performance. Conversely, marketing performance has no significant influence on financial performance. Conclusions: The findings of this study can contribute to the strategic improvement of firm performance through Big Data analytics adoption in the logistics, distribution, and supply chain industries.

A Technique for Improving the Performance of Cache Memories

  • Cho, Doosan
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.3
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    • pp.104-108
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    • 2021
  • In order to improve performance in IoT, edge computing system, a memory is usually configured in a hierarchical structure. Based on the distance from CPU, the access speed slows down in the order of registers, cache memory, main memory, and storage. Similar to the change in performance, energy consumption also increases as the distance from the CPU increases. Therefore, it is important to develop a technique that places frequently used data to the upper memory as much as possible to improve performance and energy consumption. However, the technique should solve the problem of cache performance degradation caused by lack of spatial locality that occurs when the data access stride is large. This study proposes a technique to selectively place data with large data access stride to a software-controlled cache. By using the proposed technique, data spatial locality can be improved by reducing the data access interval, and consequently, the cache performance can be improved.

Analysis of the Influence Factors of Data Loading Performance Using Apache Sqoop (아파치 스쿱을 사용한 하둡의 데이터 적재 성능 영향 요인 분석)

  • Chen, Liu;Ko, Junghyun;Yeo, Jeongmo
    • KIPS Transactions on Software and Data Engineering
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    • v.4 no.2
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    • pp.77-82
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    • 2015
  • Big Data technology has been attracted much attention in aspect of fast data processing. Research of practicing Big Data technology is also ongoing to process large-scale structured data much faster in Relatioinal Database(RDB). Although there are lots of studies about measuring analyzing performance, studies about structured data loading performance, prior step of analyzing, is very rare. Thus, in this study, structured data in RDB is tested the performance that loads distributed processing platform Hadoop using Apache sqoop. Also in order to analyze the influence factors of data loading, it is tested repeatedly with different options of data loading and compared with data loading performance among RDB based servers. Although data loading performance of Apache Sqoop in test environment was low, but in large-scale Hadoop cluster environment we can expect much better performance because of getting more hardware resources. It is expected to be based on study improving data loading performance and whole steps of performance analyzing structured data in Hadoop Platform.

Development of Performance Analysis Methodology for Nuclear Power Plant Turbine Cycle Using Validation Model of Performance Measurements (원전 터빈사이클 성능 데이터의 검증 모델에 의한 성능분석 기법의 개발)

  • Kim, Seong-Geun;Choe, Gwang-Hui
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.24 no.12
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    • pp.1625-1634
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    • 2000
  • Verification of measurements is required for precise evaluation of turbine cycle performance in nuclear power plant. We assumed that initial acceptance data and design data of the plant could provide correlation information between performance data. The data can be used as sample sets for the correct estimation model of measurement value. The modeling was done practically by using regression model based on plant design data, plant acceptance data and verified plant performance data of domestic nuclear power plant. We can construct more robust performance analysis system for an operation nuclear power plant with this validation scheme.

A STUDY ON THE CONSTRUCTION OF BIM DATA INTEROPERABILITY FOR ENERGY PERFORMANCE ASSESSMENT BASED ON BIM

  • Jungsik Choi;Hyunjae Yoo;Inhan Kim
    • International conference on construction engineering and project management
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    • 2013.01a
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    • pp.267-273
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    • 2013
  • Early design phase energy modeling is used to provide the design team with first order of magnitude feedback about the impact of various building configurations. For better energy-conscious and sustainable building design and operation, the construction of BIM data interoperability for energy performance assessment in the early design phase is important. The purpose of this study is to suggest construction of BIM data interoperability for energy performance assessment based on BIM. To archive this purpose, the authors have investigated advantage of BIM-based energy performance assessment through comparison with traditional energy performance assessment and suggested requirement for construction of open BIM environment such as BIM data creation, BIM data software practical use, BIM data application and verification. In addition, the authors have suggested BIM data interoperability and BIM energy property mapping method focused on materials.

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Analysis of Supervisory Report for Performance Measurement in the Private Building Construction Sites (민간 건축현장 성과측정을 위한 감리보고서 활용성 분석)

  • Sung, Yookyung;Hur, Youn Kyoung;Lee, Seung Woo;Yoo, Wi Sung
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.11a
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    • pp.217-218
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    • 2022
  • Supervision work deals with important data necessary for the performance management on building construction sites in accordance with the Building Act. Therefore, this study attempts to use the data from supervisory reports to measure the performance of private building projects. Performance measurement is important for systematic management. However, there are only a few cases in which performance measurement is performed because it requires strenuous efforts to collect data for measurement. First, this study derived 6 performance areas and 15 indicators through a few rounds of expert group discussions. Then, we confirmed the performance indicators with high feasibility of data collection through a survey of supervision experts. It is expected that the data of supervisory reports can measure systematically performance and assist in speedy diagnosis of private building construction sites.

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Pavement Performance Model Development Using Bayesian Algorithm (베이지안 기법을 활용한 공용성 모델개발 연구)

  • Mun, Sungho
    • International Journal of Highway Engineering
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    • v.18 no.1
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    • pp.91-97
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    • 2016
  • PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

Performance Optimization of Big Data Center Processing System - Big Data Analysis Algorithm Based on Location Awareness

  • Zhao, Wen-Xuan;Min, Byung-Won
    • International Journal of Contents
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    • v.17 no.3
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    • pp.74-83
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    • 2021
  • A location-aware algorithm is proposed in this study to optimize the system performance of distributed systems for processing big data with low data reliability and application performance. Compared with previous algorithms, the location-aware data block placement algorithm uses data block placement and node data recovery strategies to improve data application performance and reliability. Simulation and actual cluster tests showed that the location-aware placement algorithm proposed in this study could greatly improve data reliability and shorten the application processing time of I/O interfaces in real-time.

Filtering Correction Method and Performance Comparison for Time Series Data

  • Baek, Jongwoo;Choi, Jiyoung;Jung, Hoekyung
    • Journal of information and communication convergence engineering
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    • v.20 no.2
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    • pp.125-130
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    • 2022
  • In modern society, as many data are used for research or commercial purposes, the value of data is gradually increasing. In related fields, research is being actively conducted to collect valuable data, but it is difficult to collect proper data because the value of collection is determined according to the performance of existing sensors. To solve this problem, a method to effectively reduce noise has been proposed, but there is a point in which performance is degraded due to damage caused by noise. In this paper, a device capable of collecting time series data was designed to correct such data noise, and a correction technique was performed by giving an error value based on the representatively collected ultrafine dust data, and then comparing before and after Compare performance. For the correction method, Kalman, LPF, Savitzky-Golay, and Moving Average filter were used. Savitzky-Golay filter and Moving Average Filter showed excellent correction rate as an experiment. Through this, the performance of the sensor can be supplemented and it is expected that data can be effectively collected.

A Simulation Framework for Wireless Compressed Data Broadcast

  • Seokjin Im
    • International Journal of Advanced Culture Technology
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    • v.11 no.2
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    • pp.315-322
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    • 2023
  • Intelligent IoT environments that accommodate a very large number of clients require technologies that provide secure information service regardless of the number of clients. Wireless data broadcast is an information service technique that ensures scalability to deliver data to all clients simultaneously regardless of the number of clients. In wireless data broadcasting, clients access the wireless channel linearly to explore the data, so the access time of clients is greatly affected by the broadcast cycle. Data compression-based data broadcasting can reduce the broadcast cycle and thus reduce client access time. Therefore, a simulation framework that can evaluate the performance of data broadcasting by applying different data compression algorithms is essential and important. In this paper, we propose a simulation framework to evaluate the performance of data broadcasting that can adopt data compression. We design the framework that enables to apply different data compression algorithms according to the data characteristics. In addition to evaluating the performance according to the data, the proposed framework can also evaluate the performance according to the data scheduling technique and the kind of queries the client wants to process. We implement the proposed framework and evaluate the performance of data broadcasting using the framework applying data compression algorithms to demonstrate the performances of data compression broadcasting.