• Title/Summary/Keyword: Data Profiling

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Profiling Approach for the Choice between Speculation and Postponement Strategy in Supply Chain Management (공급사슬관리의 예측전략과 지연전략 선택을 위한 프로파일링 접근법)

  • Kang, Sung-Wook;Kim, Gyu-Bae
    • Journal of Distribution Science
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    • v.12 no.4
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    • pp.47-54
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    • 2014
  • Purpose - The postponement strategy, which delays the form, place, and production of products as late as possible, has been widely considered as a competitive supply chain management scheme in an era of mass customization and modular manufacturing. An interesting business phenomenon is that not all manufacturing/logistics firms choose the postponement strategy. Given that postponement is a counter-measure to speculation, which has some advantages under certain environments, the current imprudent inclination toward the postponement strategy may cause firms to lose the potential of the speculation strategy, an alternative strategy in supply chain management. Building on the logistics and manufacturing literature, this study examines characteristics of two contrasting strategies, postponement and speculation, and major factors favoring each strategy. Research design, data, and methodology - We apply the profiling approach to two business cases, HP printer and LG mobile phone. The profiling approach is a method of choosing a particular strategy aligned with environmental factors. While various approaches have been used to check the fit between a business strategy and environmental factors, the literature on manufacturing strategy and logistics has commonly adopted the profiling approach. Major factors used in profiling variables are derived from the literature. Two samples, HP printer and LG mobile phone, are selected, because they represent major characteristics appropriate for each strategy. The profiling is based on data from semi-organized interviews with managers. Results - The profiling approach shows that the postponement strategy is a suitable one for HP printers. Most factors, such as product life cycle, large production volume, low-price, product value, and monetary density, support delaying end products until as late as possible. Despite some exceptions, such as delivery time and economy of scale, our analysis states that the overall profile of HP printer is favorable for the postponement strategy. On the other hand, LG mobile phone may adapt the speculation strategy. Although it has large production volume and low delivery frequency, most characteristics support the speculation strategy for this product. An interesting finding is that, despite common perception that advanced technology products such as mobile telephones favor the postponement strategy, profiling proposes the speculation strategy for this product. Conclusions - Our analysis shows that speculation is not the universal option for supply chain management, and that, when choosing a specific strategy, one should consider many factors simultaneously. A major implication of our work is to emphasize the role of environmental factors such as supply chain variables in choosing an inventory strategy, and the importance of fit rather than solely strategic orientation. A theoretical contribution is to demonstrate the benefit of the simultaneous consideration of business variables in choosing specific strategies. For practitioners, our work leads us to consider the existence and the potential of speculation as a counter-measure to postponement. In addition, the comprehensive framework in this research may be instantly used in examining a practical strategy.

A Study on Scalability of Profiling Method Based on Hardware Performance Counter for Optimal Execution of Supercomputer (슈퍼컴퓨터 최적 실행 지원을 위한 하드웨어 성능 카운터 기반 프로파일링 기법의 확장성 연구)

  • Choi, Jieun;Park, Guenchul;Rho, Seungwoo;Park, Chan-Yeol
    • KIPS Transactions on Computer and Communication Systems
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    • v.9 no.10
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    • pp.221-230
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    • 2020
  • Supercomputer that shares limited resources to multiple users needs a way to optimize the execution of application. For this, it is useful for system administrators to get prior information and hint about the applications to be executed. In most high-performance computing system operations, system administrators strive to increase system productivity by receiving information about execution duration and resource requirements from users when executing tasks. They are also using profiling techniques that generates the necessary information using statistics such as system usage to increase system utilization. In a previous study, we have proposed a scheduling optimization technique by developing a hardware performance counter-based profiling technique that enables characterization of applications without further understanding of the source code. In this paper, we constructed a profiling testbed cluster to support optimal execution of the supercomputer and experimented with the scalability of the profiling method to analyze application characteristics in the built cluster environment. Also, we experimented that the profiling method can be utilized in actual scheduling optimization with scalability even if the application class is reduced or the number of nodes for profiling is minimized. Even though the number of nodes used for profiling was reduced to 1/4, the execution time of the application increased by 1.08% compared to profiling using all nodes, and the scheduling optimization performance improved by up to 37% compared to sequential execution. In addition, profiling by reducing the size of the problem resulted in a quarter of the cost of collecting profiling data and a performance improvement of up to 35%.

Cross-Product Category User Profiling for E-Commerce Personalized Recommendation (전자상거래 개인화 추천을 위한 상품 카테고리 중립적 사용자 프로파일링)

  • Park, Soo-Hwan;Lee, Hong-Joo;Cho, Nam-Jae;Kim, Jong-Woo
    • Asia pacific journal of information systems
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    • v.16 no.3
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    • pp.159-176
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    • 2006
  • Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

A Normal Network Behavior Profiling Method Based on Big Data Analysis Techniques (Hadoop/Hive) (빅데이터 분석 기술(Hadoop/Hive) 기반 네트워크 정상행위 규정 방법)

  • Kim, SungJin;Kim, Kangseok
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.27 no.5
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    • pp.1117-1127
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    • 2017
  • With the advent of Internet of Things (IoT), the number of devices connected to Internet has rapidly increased, but the security for IoT is still vulnerable. It is difficult to integrate existing security technologies due to generating a large amount of traffic by using different protocols to use various IoT devices according to purposes and to operate in a low power environment. Therefore, in this paper, we propose a normal network behavior profiling method based on big data analysis techniques. The proposed method utilizes a Hadoop/Hive for Big Data analytics and an R for statistical computing. Also we verify the effectiveness of the proposed method through a simulation.

Static and Dynamic Analysis of Efficiency of Korean Regional Public Hospitals (지방의료원의 효율성에 대한 정태적 및 동태적 분석)

  • Kim, Jong-Ki;Jeon, Jinh-Wan
    • Korea Journal of Hospital Management
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    • v.15 no.1
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    • pp.27-48
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    • 2010
  • The purpose of this paper is to analyze the efficiency change and its determinants of the regional public hospitals. We utilize 34 regional public hospital's panel data for 6 years from 2003 to 2008. We use DEA(Data Envelopment Analysis)-CCR, BCC model, DEA/Window model, and DEA Profiling. The empirical results show the following findings. First, technical efficiency shows that approximately 3.6% of inefficiency exists on the regional public hospitals and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, DEA/Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of partial efficiency by DEA Profiling show that increase efficiency depends on the number of beds, doctors, and nurses.

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Speculative Parallelism Characterization Profiling in General Purpose Computing Applications

  • Wang, Yaobin;An, Hong;Liu, Zhiqin;Li, Li;Yu, Liang;Zhen, Yilu
    • Journal of Computing Science and Engineering
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    • v.9 no.1
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    • pp.20-28
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    • 2015
  • General purpose computing applications have not yet been thoroughly explored in procedure level speculation, especially in the light-weighted profiling way. This paper proposes a light-weighted profiling mechanism to analyze speculative parallelism characterization in several classic general purpose computing applications from SPEC CPU2000 benchmark. By comparing the key performance factors in loop and procedure-level speculation, it includes new findings on the behaviors of loop and procedure-level parallelism under these applications. The experimental results are as follows. The best gzip application can only achieve a 2.4X speedup in loop level speculation, while the best mcf application can achieve almost 3.5X speedup in procedure level. It proves that our light-weighted profiling method is also effective. It is found that between the loop-level and procedure-level TLS, the latter is better on several cases, which is against the conventional perception. It is especially shown in the applications where their 'hot' procedure body is concluded as 'hot' loops.

Geoacoustic Modeling for Analysis of Attenuation Characteristics using Chirp Acoustic Profiling data (광역주파수 음향반사자료의 감쇠특성 분석을 위한 지질음향모델링 기법 연구)

  • Chang Jae-Kyeong;Yang Sung-Jin
    • Geophysics and Geophysical Exploration
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    • v.2 no.4
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    • pp.202-208
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    • 1999
  • We introduce a new acoustic parameter for the classification of seafloor sediments from chirp sonar acoustic profiling data. The acoustic parameter is defined as a derivative of the unwrapped phase of the Fourier transform of acoustic profiling data. Consequently, it represents the characteristics of attenuation by dissipative dispersion in sediments. And we estimated acoustic properties by geoacoustic modeling using Chirp data obtained from the different sedimentary facies. Our classification results, when compared with the results of analysis of sampled sediments, show that the acoustic parameter discriminates sedimentary facies and bottom hardness. Thus the method in this paper is expected to be an effective means of geoacoustic modeling of the seafloor.

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Analyzing the Research Fronts of Women's Studies in Korea Using Citation Image Makers Profiling (인용 이미지 구축자 프로파일링을 이용한 국내 여성학 분야 연구 전선 분석)

  • Kim, Jo-Ah;Lee, Jae Yun
    • Journal of the Korean Society for information Management
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    • v.33 no.2
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    • pp.201-225
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    • 2016
  • A new technique for revealing the research fronts of a interdisciplinary discipline has been developed. Citation image makers profiling (CIMP) determines the relationships between research papers with the title words of the citing documents. We adapted this new technique to analyze the research fronts and hot topics in women's studies of Korea. By Korean Citation Index (KCI) data in 2015, we selected 148 papers cited more than 9 times as the core documents of women's studies. Analysis of intellectual structure using citation image makers profiling was performed with the 148 core documents and those citing papers. Document co-citation analysis was hindered by citation data sparsity, while CIMP method successfully revealed the structure of research fronts of Korean women's studies including 2 divisions and 6 subdivisions. The CIMP method suggested in this study has good potential to discover the characteristics of research fronts of interdisciplinary research domains.

Reduction of Ambiguity in Phosphorylation-site Localization in Large-scale Phosphopeptide Profiling by Data Filter using Unique Mass Class Information

  • Madar, Inamul Hasan;Back, Seunghoon;Mun, Dong-Gi;Kim, Hokeun;Jung, Jae Hun;Kim, Kwang Pyo;Lee, Sang-Won
    • Bulletin of the Korean Chemical Society
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    • v.35 no.3
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    • pp.845-850
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    • 2014
  • The rapid development of shotgun proteomics is paving the way for extensive proteome profiling, while providing extensive information on various post translational modifications (PTMs) that occur to a proteome of interest. For example, the current phosphoproteomic methods can yield more than 10,000 phosphopeptides identified from a proteome sample. Despite these developments, it remains a challenging issue to pinpoint the true phosphorylation sites, especially when multiple sites are possible for phosphorylation in the peptides. We developed the Phospho-UMC filter, which is a simple method of localizing the site of phosphorylation using unique mass classes (UMCs) information to differentiate phosphopeptides with different phosphorylation sites and increase the confidence in phosphorylation site localization. The method was applied to large scale phosphopeptide profiling data and was demonstrated to be effective in the reducing ambiguity associated with the tandem mass spectrometric data analysis of phosphopeptides.

The Bayesian Framework based on Graphics for the Behavior Profiling (행위 프로파일링을 위한 그래픽 기반의 베이지안 프레임워크)

  • 차병래
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.14 no.5
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    • pp.69-78
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    • 2004
  • The change of attack techniques paradigm was begun by fast extension of the latest Internet and new attack form appearing. But, Most intrusion detection systems detect only known attack type as IDS is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, the experiments to apply various techniques of anomaly detection are appearing. In this paper, we propose an behavior profiling method using Bayesian framework based on graphics from audit data and visualize behavior profile to detect/analyze anomaly behavior. We achieve simulation to translate host/network audit data into BF-XML which is behavior profile of semi-structured data type for anomaly detection and to visualize BF-XML as SVG.