• Title/Summary/Keyword: Cloud Service Partner

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A Development of Cloud Service Partner Competency Analysis Framework (클라우드 서비스 파트너 역량 분석 프레임워크 개발)

  • Park, Wonju;Seo, Kwang-Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.21 no.3
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    • pp.69-73
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    • 2022
  • The application of cloud computing to many industrial domains is rapidly increasing, and domestic and foreign cloud service providers are actively conducting business. In the domestic cloud market, it is necessary to establish an ecosystem with partner operators that work closely with private cloud service providers. In this paper, to create such an environment, we propose a framework that can evaluate the capabilities of partners required for cloud service providers to establish specific business strategies. The framework proposed in this study establishes criteria for evaluating partners' competencies and applies a decision-making model such as fuzzy AHP for evaluation. Eventually this will help not only to expand the domestic cloud market but also to strengthen the competitiveness of domestic cloud partners through the growth of the domestic cloud market.

A Study on Establishment of Cloud Service Provider Partner Management Policy (클라우드 서비스 사업자 파트너 관리 정책 수립에 관한 연구)

  • Park, Wonju;Seo, Kwang-Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.20 no.2
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    • pp.115-120
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    • 2021
  • In Korea, where the world's first cloud computing development law was created, cloud service technology has been developing so far, and the industries to which artificial intelligence and big data technologies can be applied based on this are increasing. It is important for domestic and overseas cloud operators to secure many partners in order to provide optimal services to users. It is also important to systematically develop the partner's technology and discover new partners. In particular, the public, medical, and financial sectors are industrial fields that are difficult for domestic as well as global cloud service providers to expand without the help of partners. This study analyzes partner policies for industries caused by domestic regulations through domestic and foreign cases, and aims to establish partner management policies optimized for the domestic environment.

Dynamic Collaborative Cloud Service Platform: Opportunities and Challenges

  • Yoon, Chang-Woo;Hassan, Mohammad Mehedi;Lee, Hyun-Woo;Ryu, Won;Huh, Eui-Nam
    • ETRI Journal
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    • v.32 no.4
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    • pp.634-637
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    • 2010
  • This letter presents a model for a dynamic collaboration (DC) platform among cloud providers (CPs) that prevents adverse business impacts, cloud vendor lock-in and violation of service level agreements with consumers, and also offers collaborative cloud services to consumers. We consider two major challenges. The first challenge is to find an appropriate market model in order to enable the DC platform. The second is to select suitable collaborative partners to provide services. We propose a novel combinatorial auction-based cloud market model that enables a DC platform among CPs. We also propose a new promising multi-objective optimization model to quantitatively evaluate the partners. Simulation experiments were conducted to verify both of the proposed models.

AI Platform Solution Service and Trends (글로벌 AI 플랫폼 솔루션 서비스와 발전 방향)

  • Lee, Kang-Yoon;Kim, Hye-rim;Kim, Jin-soo
    • The Journal of Bigdata
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    • v.2 no.2
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    • pp.9-16
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    • 2017
  • Global Platform Solution Company (aka Amazon, Google, MS, IBM) who has cloud platform, are driving AI and Big Data service on their cloud platform. It will dramatically change Enterprise business value chain and infrastructures in Supply Chain Management, Enterprise Resource Planning in Customer relationship Management. Enterprise are focusing the channel with customers and Business Partners and also changing their infrastructures to platform by integrating data. It will be Digital Transformation for decision support. AI and Deep learning technology are rapidly combined to their data driven platform, which supports mobile, social and big data. The collaboration of platform service with business partner and the customer will generate new ecosystem market and it will be the new way of enterprise revolution as a part of the 4th industrial revolution.

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Artificial Intelligence Technology Trends and IBM Watson References in the Medical Field (인공지능 왓슨 기술과 보건의료의 적용)

  • Lee, Kang Yoon;Kim, Junhewk
    • Korean Medical Education Review
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    • v.18 no.2
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    • pp.51-57
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    • 2016
  • This literature review explores artificial intelligence (AI) technology trends and IBM Watson health and medical references. This study explains how healthcare will be changed by the evolution of AI technology, and also summarizes key technologies in AI, specifically the technology of IBM Watson. We look at this issue from the perspective of 'information overload,' in that medical literature doubles every three years, with approximately 700,000 new scientific articles being published every year, in addition to the explosion of patient data. Estimates are also forecasting a shortage of oncologists, with the demand expected to grow by 42%. Due to this projected shortage, physicians won't likely be able to explore the best treatment options for patients in clinical trials. This issue can be addressed by the AI Watson motivation to solve healthcare industry issues. In addition, the Watson Oncology solution is reviewed from the end user interface point of view. This study also investigates global company platform business to explain how AI and machine learning technology are expanding in the market with use cases. It emphasizes ecosystem partner business models that can support startup and venture businesses including healthcare models. Finally, we identify a need for healthcare company partnerships to be reviewed from the aspect of solution transformation. AI and Watson will change a lot in the healthcare business. This study addresses what we need to prepare for AI, Cognitive Era those are understanding of AI innovation, Cloud Platform business, the importance of data sets, and needs for further enhancement in our knowledge base.