• International Journal of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.83-90
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• 2016

The emergence of cloud has been able to provide a variety of IT service to the user. As organizations and companies are increased that provide these cloud service, many problems arises on integration. However, with the advent of latest technologies such as big data, document-oriented database, and MapReduce, this problem can be easily solved. This paper is intended to design the Cloud Aggregator to provide them as a service to collect information of the cloud system providing each service. To do this, we use the DBaaS(DataBase as a Service) and MapReduce techniques. This makes it possible to maintain the functionality of existing system and correct the problem that may occur depending on the combination.

• Asian review of World Histories
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• v.4 no.1
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• pp.83-123
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• 2016

We describe a vision of historical analysis at the world scale, through the digital assembly of historical sources into a cloud-based database, where machine-learning techniques can be used to summarize the database into a time-integrated actor-to-actor complex network. Using this time-integrated network as a template, we then apply the method of automatic narratives to discover key actors ('who'), key events ('what'), key periods ('when'), key locations ('where'), key motives ('why'), and key actions ('how') that can be presented as hypotheses to world historians. We show two test cases on how this method works. To accelerate the pace of knowledge discovery and verification, we describe how historians would interact with these automatic narratives through an online, map-based knowledge aggregator that learns how scholars filter information, and eventually takes over this function to free historians from the more important tasks of verification, and stitching together coherent storylines. Ultimately, multiple coherent storylines that are not necessary compatible with each other can be discovered through human-computer interactions by the map-based knowledge aggregator.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.299-308
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• 2022

Recent, energy transition policies are driving to increase in the number of small photovoltaic(PV) generators. It is difficult for system operators to accurately anticipate the amount of power generated from such small scale PV generation, and this may disrupt dispatch schedules and result in an increase in cost. The need for a Virtual Power Plant(VPP) is emerging as a way of resolving these problems, as it would integrate small-scale PV plants and eliminate uncertainty about the amount of power generated, control voltage, and provide power reserves. In this paper, the cost evaluation methods are described for determination of VPP cloud charges both Net Present Value(NPV) method and Profitability Index(PI) method, the calculated outcomes of the two types of cost evaluation methods are presented in detail. It seems we secure profitability as we get 1.22 of profitability index from calculation results, it may be attractive for the aggregator as NPV is enough for satisfying profitability.