• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2979-2995
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• 2017

In recent years, there has been an increasing number of studies focused on identifying a set of spreaders to maximize the influence of spreading in complex networks. Although the k-core decomposition can effectively identify the single most influential spreader, selecting a group of nodes that has the largest k-core value as the seeds cannot increase the performance of the influence maximization because the propagation sphere of this group of nodes is overlapped. To overcome this limitation, we propose a neighborhood coreness cover and discount heuristic algorithm named "NCCDH" to identify a set of influential and decentralized seeds. Using this method, a node in the high-order shell with the largest neighborhood coreness and an uncovered status will be selected as the seed in each turn. In addition, the neighbors within the same shell layer of this seed will be covered, and the neighborhood coreness of the neighbors outside the shell layer will be discounted in the subsequent round. The experimental results show that with increases in the spreading probability, the NCCDH outperforms other algorithms in terms of the affected scale and spreading speed under the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models. Furthermore, this approach has a superior running time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1877-1891
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• 2022

As a part of cloud computing technology, algorithms for cloud task scheduling place an important influence on the area of cloud computing in data centers. In our earlier work, we proposed DeepEnergyJS, which was designed based on the original version of the policy gradient and reinforcement learning algorithm. We verified its effectiveness through simulation experiments. In this study, we used the Proximal Policy Optimization (PPO) algorithm to update DeepEnergyJS to DeepEnergyJSV2.0. First, we verify the convergence of the PPO algorithm on the dataset of Alibaba Cluster Data V2018. Then we contrast it with reinforcement learning algorithm in terms of convergence rate, converged value, and stability. The results indicate that PPO performed better in training and test data sets compared with reinforcement learning algorithm, as well as other general heuristic algorithms, such as First Fit, Random, and Tetris. DeepEnergyJSV2.0 achieves better energy efficiency than DeepEnergyJS by about 7.814%.

• The Journal of Economics, Marketing and Management
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• v.6 no.1
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• pp.1-8
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• 2018

Purpose - Local social commerce market has grown up remarkably. And, Coupang has shown new delivery strategy of rocket delivery. Making new paradigm at local market, Coupang has expanded market scale. This study investigated state of local social commerce market, weight and promotion strategy of Coupang market to find out competitiveness edge of Coupang. Research design, data, and methodology - The study investigated state and concept of social commerce to find out state, problems and competitiveness of social commerce. New distribution service was short of precedent studies. Statistical analysis and experimental analysis were not used, and interview was done to investigate three of social commerce businesses. Results - CRM construction is insufficient to have poor system, Local delivery system could not be made enough at overnight delivery and customers were dissatisfied with ties with another company. Promotion shall be done by delivery system for increase of profitability, funding for more investment, chatbot to build new customer control system, and new delivery system to produce profit. Conclusions - Coupang and others have grown up rapidly to worsen profit and to jeopardize survival. Excessive initial investment has threatened the businesses, for instance, low sales of Amazon, excessive expenses, bench marking of logistics system, and others.

• Korea Trade Review
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• v.46 no.1
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• pp.23-38
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• 2021

This paper investigated the preference change of payment terms in international trade along with counteroffer or first offer conditions of the other parties. Studies on trade payment terms have mainly focused on payment term determination factors such as firm size, product price level, country credit rate, etc. We tried to find other factors affecting payment terms choice, during the negotiation process. We applied behavioral economics theories such as 'Status Quo Bias' and 'Anchoring effect' to build our research model. To prove the existence of the above effects, we proceeded with field experiments to the exporting companies in Alibaba.com. Both 'Status Quo Bias' and 'Anchoring effect' were found in the field experiment. Most of the exporting companies preferred traditional payment methods to new payment methods. And an initial request for a low advance payment ratio led to a lower advance payment ratio. Also, the experience of using new payment methods could diminish status quo bias. This paper applied behavioral economics theories and field experiment methodology to the payment term studies in international trades. These attempts could contribute to expanding the diversity of methodology and scope of international trade studies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.63-67
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• 2024

Recently, artificial intelligence (AI) workloads encompassing various industries such as smart logistics, FinTech, and entertainment are being executed on the cloud. In this paper, we address the scheduling issues of various AI workloads on a multi-tenant cloud system composed of heterogeneous GPU clusters. Traditional scheduling decreases GPU utilization in such environments, degrading system performance significantly. To resolve these issues, we present a new scheduling approach utilizing genetic algorithm-based optimization techniques, implemented within a process-based event simulation framework. Trace driven simulations with diverse AI workload traces collected from Alibaba's MLaaS cluster demonstrate that the proposed scheduling improves GPU utilization compared to conventional scheduling significantly.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.9
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• pp.973-978
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• 2015

The drone delivery service as a new important business is emerging worldwide. American DHL parcel service is already running the delivery business using drones, and the Google, Amazon, and china's Alibaba, Baidu are also preparing for the same business. However, there are some problems in security and safety since the heavy parcels flies over the people walking down streets, so many things must be considered. The problems can be solved by communicating among drones to avoid collision. Therefore, the efficient network performance is essential, and to resolve the problem, in this paper, an efficient routing protocol is suggested by the Opnet simulator. The drone intercommunication network of MANET is designed and simulated for the efficient routing protocol.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.317-322
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• 2021

This study focuses on user experience of Ant Insurance Platform(insurance product of Alibaba) and provides insights and improvement advises based on the survey conducted. Given that insurance claim is the most controversial part during the whole process, it is essential to conduct corresponding research from users' prospective. Some Chinese users between their 20's and 40's, who are major users of mobile insurance platforms, were selected as a sample group. Questionaire survey, which was made based on measurement items from Information systems success model, as well as in-depth interview was conducted within the group. According to the result, health, among all kinds, is the most difficult insurance to claim, which was mainly cause by security and information concerns. In order to enhance security, more options should be given to users. As for information, complains should be delt carefully and transparently. This study is expected to be used as referential material for mobile insurance platforms and user experience of insurance claims.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.287-297
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• 2019

Responding to the rapid delivery demands and variety of small-volume products through human resource-dependent logistics operations in online markets is difficult. Logistics robots in fulfillment centers are expected to increase due to difficulties in hiring workers and rising labor costs. Amazon operates more than 100,000 robots in its Fulfillment centers. Alibaba, DHL, and UPS are also introducing robot technology to improve the logistics efficiency. Logistics robots are being used actively in the fierce online market competition of the global distribution industry. On the other hand, Korea is relatively sluggish. To revitalize the introduction of logistics robots in Korea, it is necessary to examine the factors that influence acceptance. The description of acceptability was based on an expanded technology acceptance model that has been verified through various studies. Studies have shown that both the ease and usefulness of purchasing have significant effects, and that ease has the greatest effect. The more experience involved, the more innovative, the more positive. The usefulness was shown to be positive as the perceived risks are low, price efficiency is good, and social impact is greater. This study provides theoretical and practical implications for logistics robot development and marketing.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.9-21
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• 2020

The Gross Merchandise Volume for the China's Singles day event in 2019 is about $38.4 billion. More than 500 million customers placed about 1.3 billion orders a day, and the related delivery volume is 2.8 billion. The main technologies associated with the 4^th Industrial Revolution are bringing about a big change in the logistics industry. The purpose of this study is to present implications by reviewing the main technologies which are applied to China's Singles day event, the introduction of smart logistics in China, and analyzing the progress of Singles day, smart system of Alibaba, its significance. China still has poor infrastructure in non-capital areas. And many Chinese companies are actively introducing and developing smart logistics to cover the vast continental area of China. Singles Day is a representative case in point where the smart logistics and main technologies related to 4^th Industrial Revolution are applied. The data obtained through smart logistics would be reused for inventory management, production planning, and order processing, contributing to the optimization of the company's operations. In the era of the 4^th Industrial Revolution, domestic companies and governments need to make efforts to expand the introduction of smart logistics to secure competitiveness with global advanced companies.