• International journal of advanced smart convergence
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• v.10 no.3
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• pp.41-50
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• 2021

The purpose of this study is to analyze various energy platforms using data in the US and to suggest directions and implications. Some of the leading energy platforms are selected and analyzed based on the opening platform model. We focus on the case analysis of the US utility companies. In case of the horizontal open platform, Green Button sponsor's 'Connect My Data (CMD)' driven by the government invites the utility companies to jointly develop the sponsor's data solution. In case of the vertical open platform, the certification program 'Share My Data (SMD)' allows backward compatibility, because the technical improvement is minimal. The utility companies benchmark Amazon's three-sided market mediation and prefer platform and category exclusivity. For the former, they have data analytics companies like Enervee, Opower and for the latter, they have electronics manufactures and energy service providers (ESPs) like Distributed Energy Resources (DERs). Based on this US case study, we suggest the energy platforms to open their platform for renewable energy supply, energy conservation, high-efficiency products, and residential DER dissemination. To successfully implement the government's energy transition policy, the US platforms should be benchmarked as a business model. Especially, it is needed for them to coordinate a platform ecosystem. To ensure trust in the products and services offered on the marketplace platform, platform's certification program is helpful.

• The Journal of Information Systems
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• v.31 no.4
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• pp.101-121
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• 2022

Purpose This study aims to introduce a Smart-Platform for solving numerous problems in urban cities and improving citizens' convenience. This Smart-Platform is achieved by studying the City Data Hub technology to build and spread a Smart City Data Hub . Design/methodology/approach In this study, we analyzed the functions in detail and interlocking flows specific to the published City Data Hub module and designed a Smart-Platform with additional UI-based functions to improve users' convenience based on our previous experience. Findings The City Data Hub will provide a modular data hub platform functionality for application by different stakeholders. However, further research on detailed functionality and experience in interworking legacy systems will be required to apply the platform realistically. Therefore, we judged that this Smart-Platform, which supplements the City Data Hub with additional functions, would be useful for utilizing the City Data Hub in the future. Through the demonstration of the platform designed, it is also necessary to explore the direction of continuous development for the future.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.475-486
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• 2015

Mining the microarray data accumulated in the public data repositories can save experimental cost and time and provide valuable biomedical information. Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study. However, integrating several data sets from different studies is needed to deal with many problems. In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method based on rank. This method is compared with the diagonal linear discriminant analysis, k nearest neighbor method and support vector machine using the cross platform real example data sets of two cancers.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.194-202
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• 2019

In the information society represented by the Fourth Industrial Revolution, various types of data and information that are difficult to see are produced, processed, and processed and circulated to enhance the value of existing goods. The IoT(Internet of Things) paradigm will change the appearance of individual life, industry, disaster, safety and public service fields. In order to implement the IoT paradigm, several elements of technology are required. It is necessary that these various elements are efficiently connected to constitute one system as a whole. It is also necessary to collect, provide, transmit, store and analyze IoT data for implementation of IoT platform. We designed and implemented a big data processing IoT platform for IoT service implementation. Proposed platform system is consist of IoT sensing/control device, IoT message protocol, unstructured data server and big data analysis components. For platform testing, fixed IoT devices were implemented as solar power generation modules and mobile IoT devices as modules for table tennis stroke data measurement. The transmission part uses the HTTP and the CoAP, which are based on the Internet. The data server is composed of Hadoop and the big data is analyzed using R. Through the emprical test using fixed and mobile IoT devices we confirmed that proposed IoT platform system normally process and operate big data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2292-2313
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• 2023

With improving computing performance, various digital platforms are being developed to enable easily utilization of high-performance computing environments. EDISON 1.0 is an online simulation platform widely used in computational science and engineering education. As the research paradigm changes, the demand for developing the EDISON 1.0 platform centered on simulation into the EDISON 2.0 platform centered on data and artificial intelligence is growing. Herein, a data framework, a core module for data-centric research on EDISON 2.0 digital platform, is proposed. The proposed data framework provides the following three functions. First, it provides a data repository suitable for the data lifecycle to increase research reproducibility. Second, it provides a new data model that can integrate, manage, search, and utilize heterogeneous data to support a data-driven interdisciplinary convergence research environment. Finally, it provides an exploratory data analysis (EDA) service and data enrichment using an AI model, both developed to strengthen data reliability and maximize the efficiency and effectiveness of research endeavors. Using the EDISON 2.0 data framework, researchers can conduct interdisciplinary convergence research using heterogeneous data and easily perform data pre-processing through the web-based UI. Further, it presents the opportunity to leverage the derived data obtained through AI technology to gain insights and create new research topics.

• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.216-224
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• 2023

Smart lighting systems have become increasingly popular in several public sectors because of trends toward urbanization and intelligent technologies. In this study, we designed and implemented a web application platform to explore and monitor data acquired from lighting devices at Thammasat University (Rangsit Campus, Thailand). The platform provides a convenient interface for administrative and operative staff to monitor, control, and collect data from sensors installed on campus in real time for creating geographically specific big data. Platform development focuses on both back- and front-end applications to allow a seamless process for recording and displaying data from interconnected devices. Responsible persons can interact with devices and acquire data effortlessly, minimizing workforce and human error. The collected data were analyzed using an exploratory data analysis process. Missing data behavior caused by system outages was also investigated.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.321-326
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• 2021

In a multi-cloud environment, it is necessary to minimize physical movement for efficient interoperability of distributed source data without building a data warehouse or data lake. And there is a need for a data platform that can easily access data anywhere in a multi-cloud environment. In this paper, we propose a new platform based on data fabric centered on a distributed platform suitable for cloud environments that overcomes the limitations of legacy systems. This platform applies the knowledge graph database technique to the physical linkage of source data for interoperability of distributed data. And by integrating all data into one scalable platform in a multi-cloud environment, it uses the holochain technique so that companies can easily access and move data with security and authority guaranteed regardless of where the data is stored. The knowledge graph database mitigates the problem of heterogeneous conflicts of data interoperability in a decentralized environment, and Holochain accelerates the memory and security processing process on traditional blockchains. In this way, data access and sharing of more distributed data interoperability becomes flexible, and metadata matching flexibility is effectively handled.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.77-86
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• 2022

The destructive impact of contagious diseases outbreaks on all life facets necessitates developing effective solutions to control these diseases outbreaks. This research proposes an end-to-end, data-driven platform which consists of multiple modules that are working in harmony to achieve a concrete goal: early detection of contagious diseases outbreaks (i.e., epidemic diseases detection). Achieving that goal enables decision makers and people in power to act promptly, resulting in robust prevention management of contagious diseases. It must be clear that the goal of this proposed platform is not to predict or forecast the spread of contagious diseases, rather, its goal is to promptly detect contagious diseases outbreaks as they happen. The front end of the proposed platform is a web-based dashboard that visualizes diseases outbreaks in real-time on a real map. These outbreaks are detected via another component of the platform which utilizes data mining techniques and algorithms on gathered datasets. Those gathered datasets are managed by yet another component. Specifically, a mobile application will be the main source of data to the platform. Being a vital component of the platform, the datasets are managed by a DBMS that is specifically tailored for this platform. Preliminary results are presented to showcase the performance of a prototype of the proposed platform.

• Journal of Distribution Science
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• v.19 no.4
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• pp.15-24
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• 2021

Purpose: This study deals with the strategic direction of platform business in the 4th Industrial Revolution. In a changing industrial environment, companies that leverage platform businesses can benefit greatly. Platform business is especially important in the distribution industry, so a variety of case analysis studies are needed. In this study, the Shinsegae Group, a platform leader, was selected and case-analyzed to learn more about the growth and development of platform businesses in the distribution industry. Research design, data and Methodology: Various literature research on platform business and secondary data of the distribution industry were reviewed. In this study, additionally, the interview method was used to discuss the development of more platform businesses through the Shinsegae and E-mart related departments. Results: This study investigated the development process and success of distribution platforms. First, a successful platform business builds multi distribution channels. Second, a successful distribution platform business attracts many participants. Third, a successful distribution develops various private label products to build a sustainable platform system. Conclusions and Implications: This study could provide a good example for the growth of companies with distribution platform business. Further implications were discussed.