• Smart Media Journal
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• v.6 no.1
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• pp.9-15
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• 2017

The trends in ICT are concentrated in IoT, Bigdata, and Cloud Computing. These mega-trends do not operate independently, and mass storage technology is essential as large computing technology is needed in the background to support them. In order to evaluate the performance of high-capacity storage based on open source Ceph, we carry out the demonstration test of Abyss Storage with domestic and overseas sites using educational network KOREN. In addition, storage media and network bonding are tested to evaluate the performance of the storage itself. Although there is a substantial difference in aspect of the physical speed among storage medias, there is no significant performance difference in the storage media test performed. As a solution to this problem, we could get performance improvement through network acceleration. In addition, we conducted actual performance test of Abyss Storage internal and external network by connecting domestic and overseas sites using KOREN network.

• Smart Media Journal
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• v.7 no.1
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• pp.9-15
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• 2018

As an organization or organization grows in size, many different types of data are being generated in different systems. There is a need for a way to improve efficiency by processing data smarter in different systems. Just like DataLake, we are creating a single domain model that accurately describes the data and can represent the most important data for the entire business. In order to realize the benefits of a DataLake, it is import to know how a DataLake may be expected to work and what components architecturally may help to build a fully functional DataLake. DataLake components have a life cycle according to the data flow. And while th data flows into a DataLake from the point of acquisition, its meta-data is captured and managed along with data traceability, data lineage, and security aspects based on data sensitivity across its life cycle. According to this reason, we have designed the DataLake Framework based on Abyss Storage Cluster.

• Smart Media Journal
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• v.7 no.3
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• pp.57-63
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• 2018

With many types of data generated in the shift of business environment as a result of growth of an organization or enterprise, there is a need to improve the data-processing efficiency in smarter means with a single domain model such as Data Lake. In particular, creating a logical single domain model from physical partitioned multi-site data by the finite resources of nature and shared economy is very important in terms of efficient operation of computing resources. Based on the advantages of the existing Data Lake framework, we define the CDA-Concept (connected data architecture concept) and functions of Data Lake Framework over Abyss Storage for integrating multiple sites in various application domains and managing the data lifecycle. Also, it performs the interface design and validation verification for Interface #2 & #3 of the connected data architecture-concept.

• Smart Media Journal
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• v.10 no.1
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• pp.99-107
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• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• Smart Media Journal
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• v.7 no.4
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• pp.30-36
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• 2018

Single domain model like DataLake framework is in spotlight because it can improve data efficiency and process data smarter in big data environment, where large scaled business system generates huge amount of data. In particular, efficient operation of network, storage, and computing resources in logical single domain model is very important for physically partitioned multi-site data process. Based on the advantages of Data Lake framework, we define and extend the concept of Connected Data Architecture and functions of DataLake framework for integrating multiple sites in various domains and managing the lifecycle of data. Also, we propose the design of CDA-based AI service and utilization scenarios in various application domain.