• Journal of Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Communications of the Korean Mathematical Society
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• v.23 no.1
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• pp.11-18
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• 2008

For a nef and big divisor D on a smooth projective surface S, the inequality $h^{0}$(S;$O_{s}(D)$) ${\leq}\;D^2\;+\;2$ is well known. For a nef and big canonical divisor KS, there is a better inequality $h^{0}$(S;$O_{s}(K_s)$) ${\leq}\;\frac{1}{2}{K_{s}}^{2}\;+\;2$ which is called the Noether inequality. We investigate an inequality $h^{0}$(S;$O_{s}(D)$) ${\leq}\;\frac{1}{2}D^{2}\;+\;2$ like Clifford theorem in the case of a curve. We show that this inequality holds except some cases. We show the existence of a counter example for this inequality. We prove also the base-locus freeness of the linear system in the exceptional cases.

• Journal of Information Technology and Architecture
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• v.11 no.4
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• pp.449-462
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• 2014

In big data era, there are a number of considerable parts in processing systems for capturing, storing, and analyzing stored or streaming data. Unlike traditional data handling systems, a big data processing system needs to concern the characteristics (format, velocity, and volume) of being handled data in the system. In this situation, virtualized computing platform is an emerging platform for handling big data effectively, since virtualization technology enables to manage computing resources dynamically and elastically with minimum efforts. In this paper, we analyze resource utilization of virtualized computing resources to discover suitable deployment models in Apache Hadoop and HBase-based big data processing environment. Consequently, Task Tracker service shows high CPU utilization and high Disk I/O overhead during MapReduce phases. Moreover, HRegion service indicates high network resource consumption for transfer the traffic data from DataNode to Task Tracker. DataNode shows high memory resource utilization and Disk I/O overhead for reading stored data.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.1-6
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• 2022

With the emergence of the new era of the 4th industrial revolution, compute-bound workloads with large memory footprint like big data processing increase dramatically. Even in such compute-bound workloads, however, we observe bulky I/Os while loading big data from storage to memory. Although file I/O cache plays a role of accelerating the performance of storage I/O, we found out that the cache hit rate in such environments is not improved even though we increase the file I/O cache capacity because of some special I/O references generated by compute-bound workloads. To cope with this situation, we propose a new file I/O cache management policy that improves the cache hit rate for compute-bound workloads significantly. Trace-driven simulations by replaying file I/O reference logs of compute-bound workloads show that the proposed cache management policy improves the cache hit rate compared to the well-acknowledged CLOCK algorithm by a large margin.

• Journal of Platform Technology
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• v.11 no.4
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• pp.50-61
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• 2023

The advancement of various big data technologies has had a tremendous impact on many industries. Diverse big data research studies have been conducted to process and analyze massive data quickly. Under these circumstances, new emerging technologies such as high-capacity persistent memory (PMEM) and Compute Express Link (CXL) have lately attracted significant attention. However, little investigation into a big data "search" platform has been made. Moreover, most big data software platforms have been still optimized for traditional DRAM-based computing systems. This paper first evaluates the basic performance of Intel Optane PMEM, and then investigates both indexing and searching performance of Elasticsearch, a widely-known enterprise big data search platform, on the PMEM-based computing system to explore its effectiveness and possibility. Extensive and comprehensive experiments shows that the proposed Optane PMEM-based Elasticsearch achieves indexing and searching performance improvement by an average of 1.45 times and 3.2 times respectively compared to DRAM-based system. Consequently, this paper demonstrates the high I/O, high-capacity, and nonvolatile PMEM-based computing systems are very promising for big data search platforms.

• Journal of applied mathematics & informatics
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• v.8 no.3
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• pp.959-973
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• 2001

We shall discuss one of some techniques needed to analyze algorithms. It is called a big-O function technique. The measures of efficiency of an algorithm have two cases. One is the time used by a computer to solve the problem using this algorithm when the input values are of a specified size. The other one is the amount of computer memory required to implement the algorithm when the input values are of a specified size. Mainly, we will restrict our attention to time complexity. To figure out the Time Complexity in nonlinear problems of Numerical Analysis seems to be almost impossible.

• The Journal of the Korea Contents Association
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• v.20 no.2
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• pp.561-571
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• 2020

Location information service using big data is continuously developing. In terms of navigation, the range of services from map API service to ship navigation information has been expanded, and system application information has been extended to SNS and blog search records for each location. Recently, it is being used as a new industry such as location-based search and advertisement, driverless cars, Internet of Things (IoT) and online to offline (O2O) services. In this study, we propose an information system that enables users to receive information about nearby stores more effectively by using big data when a user moves a specific route. In addition, we have designed this system so that local stores can use this system to effectively promote it at low cost. In particular, we analyzed web-based information in real time to improve the accuracy of information provided to users by complementing the data. Through this system, system users will be able to utilize the information more effectively. Also, from a system perspective, it can be used to create new services by integrating with various web services.

• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1499-1505
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• 2012

Recently, the distributed processing system for big data has been actively investigated owing to the development of high speed network and storage technologies. In addition, virtual system that can provide efficient use of system resources through the consolidation of servers has been increasingly recognized. But, when we configure distributed processing system for big data in virtual machine environments, many problems occur. In this paper, we did an experiment on the optimization of I/O bandwidth according to the creation and placement of VMs and tasks with composing Hadoop cluster in virtual environments and evaluated the results of an experiment. These results conducted by this paper will be used in the study on the development of Hadoop Scheduler supporting I/O bandwidth balancing in virtual environments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.256-267
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• 2014

An automatic switch among ensembles of clustering algorithms is proposed as a part of the bibliographic big data retrieval system by utilizing a fuzzy inference engine as a decision support tool to select the fastest performing clustering algorithm between fuzzy C-means (FCM) clustering, Newman-Girvan clustering, and the combination of both. It aims to realize the best clustering performance with the reduction of computational complexity from O($n^3$) to O(n). The automatic switch is developed by using fuzzy logic controller written in Java and accepts 3 inputs from each clustering result, i.e., number of clusters, number of vertices, and time taken to complete the clustering process. The experimental results on PC (Intel Core i5-3210M at 2.50 GHz) demonstrates that the combination of both clustering algorithms is selected as the best performing algorithm in 20 out of 27 cases with the highest percentage of 83.99%, completed in 161 seconds. The self-adapted FCM is selected as the best performing algorithm in 4 cases and the Newman-Girvan is selected in 3 cases.The automatic switch is to be incorporated into the bibliographic big data retrieval system that focuses on visualization of fuzzy relationship using hybrid approach combining FCM and Newman-Girvan algorithm, and is planning to be released to the public through the Internet.

• Knowledge Management Research
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• v.23 no.3
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• pp.285-311
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• 2022

In this study, based on the S-O-R framework, how individual's stimuli (i.e., video characteristics, YouTuber characteristics, real-time broadcasting characteristics of YouTube channel) form organisms (i.e., perceived usefulness, perceived pleasure, social presence), leading to viewers' responses (i.e., continuous watching intention, paid sponsorship intention) on real-time YouTube channels. For this purpose, a research model and hypotheses were constructed, and 369 questionnaire data collected from users of real-time broadcasting channel services on the YouTube platform were analyzed. Result findings confirmed that some video/YouTuber/real-time broadcasting characteristics significantly affect viewers' perceived usefulness/perceived pleasure/social presence, and further influence continuous watching/paid sponsorship intentions. Theoretical and practical implications of the findings are discussed in conclusion.