• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.1
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• pp.17-31
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• 2009

The importance of software has been growing rapidly owing to the development of various Internet and e-business applications. The traditional approaches to software evaluation are based on the development process perspective, and their major concerns are no strongly related to use or customer-oriented evaluation of software. According to resource-based theory, company's resource is consisted of human, technology, market value, and finance. Customer satisfaction improved by product satisfaction and service satisfaction. Based on the previous studies the factors of human resources, technology, customer satisfaction are selected to evaluate software company's competence This research suggests the factor effecting on sales performance. And then statistical methods are used for verifying relationship between the factor and sales performance.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.131-143
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• 2022

Focusing on FinTech keywords, this study is analyzing newspaper articles and Twitter data by using text mining methodology in order to understand trends in the industry of domestic digital financial service. In the growth of FinTech lifecycle, the frequency analysis has been performed by four important points: Mobile Payment Service, Internet Primary Bank, Data 3 Act, MyData Businesses. Utilizing frequency analysis, which combines the keywords 'China', 'USA', and 'Future' with the 'FinTech', has been predicting the FinTech industry regarding of the current and future position. Next, sentiment analysis was conducted on Twitter to quantify consumers' expectations and concerns about FinTech services. Therefore, this study is able to share meaningful perspective in that it presented strategic directions that the government and companies can use to understanding future FinTech market by combining frequency analysis and sentiment analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.3
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• pp.157-163
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• 2013

Data mining is a useful technology that can support new discoveries based on the pattern analysis and a variety of linkages between data, and currently is utilized in various fields such as finance, marketing, medical. In this paper, we propose an effective utilization method of data based on analysis of spatial data mining. We make use of basic data of foreigners living in Seoul. However, the data has some features distinguished from other areas of data, classification as sensitive information and legal problem such as personal information protection. So, we use the basic statistical data that does not contain personal information. The main features and contributions of the proposed method are as follows. First, we can use Big Data as information through a variety of ways and can classify and cluster Big Data through refinement. Second. we can use these kinds of information for decision-making of future and new patterns. In the performance evaluation, we will use visual approach through graph of themes. The results of performance evaluation show that the analysis using data mining technology can support new discoveries of patterns and results.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.17-31
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• 2020

With the rapid development of block chain technology, attempts have been made to put the block chain technology into practical use in various fields such as finance and logistics, and also in the public sector where data integrity is very important. Defense Operations In addition, strengthening security and ensuring complete integrity of the command communication network is crucial for operational operation under the network-centered operational environment (NCOE). For this purpose, it is necessary to construct a command communication network applying the block chain network. However, the block chain technology up to now can not solve the security issues such as the 51% attack. In particular, the Practical Byzantine fault tolerance (PBFT) algorithm which is now widely used in blockchain, does not have a penalty factor for nodes that behave maliciously, and there is a problem of failure to make a consensus even if malicious nodes are more than 33% of all nodes. In this paper, we propose a Adaptive Consensus Bound PBFT (ACB-PBFT) algorithm that incorporates a penalty mechanism for anomalous behavior by combining the Trust model to improve the security of the PBFT, which is the main agreement algorithm of the blockchain.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.201-209
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• 2012

The point-based complex event processing handled an instantaneous event by using one time stamp in each event. However, the activity period of the event plays the important role in the field which is the same as the finance, multimedia, medicine, and meteorology. The point-based event is insufficient for expressing the complex temporal relationship in this field. In the application field of the real-time world, the event has the period. The events more than two kinds can be temporally overlapped. In addition, one event can include the other event. The relation about the events of kind of these can not be successive like the point-based event. This thesis designs and implements the method detecting the patterns of the complex event by using the interval-based events. The interval-based events can express the overlapping relation between events. Furthermore, it can include the others. By using the end point of beginning and end point of the termination, the operator of interval-based events shows the interval-based events. It expresses the sequence of the interval-based events and can detect the complex event patterns. This thesis proposes the algorithm using the active instance stack in order to raise efficiency of detection of the complex event patterns. When comprising the event sequence, this thesis applies the window push down technique in order to reduce the number of intermediate results. It raises the utility factor of the running time and memory.

• Smart Media Journal
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• v.8 no.1
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• pp.74-81
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• 2019

Recently, machine learning technology has had a significant impact on society, particularly in the medical, manufacturing, marketing, finance, broadcasting, and agricultural aspects of human lives. In this paper, we study how to apply machine learning techniques to foods, which have the greatest influence on the human survival. In the field of Smart Farm, which integrates the Internet of Things (IoT) technology into agriculture, we focus on optimizing the crop growth environment by monitoring the growth environment in real time. KT Smart Farm Solution 2.0 has adopted machine learning to optimize temperature and humidity in the greenhouse. Most existing smart farm businesses mainly focus on controlling the growth environment and improving productivity. On the other hand, in this study, we are studying how to apply machine learning with respect to harvest time so that we will be able to harvest fruits of the highest quality and ship them at an excellent cost. In order to apply machine learning techniques to the field of smart farms, it is important to acquire abundant voluminous data. Therefore, to apply accurate machine learning technology, it is necessary to continuously collect large data. Therefore, the color, value, internal temperature, and moisture of greenhouse-grown fruits are collected and secured in real time using color, weight, and temperature/humidity sensors. The proposed FPSML provides an architecture that can be used repeatedly for a similar fruit crop. It allows for a more accurate harvest time as massive data is accumulated continuously.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.57-65
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• 2021

A hyper-connected intelligence information society is emerging that creates new value by converging IoT, AI, and Bigdata, which are new technologies of the fourth industrial revolution, in all industrial fields. Everything is connected to the network and data is exploding, and artificial intelligence can learn on its own and even intellectual judgment functions are possible. In particular, the Internet of Things provides a new communication environment that can be connected to anything, anytime, anywhere, enabling super-connections where everything is connected. Artificial intelligence technology is implemented so that computers can execute human perceptions, learning, reasoning, and natural language processing. Artificial intelligence is developing advanced technologies such as machine learning, deep learning, natural language processing, voice recognition, and visual recognition, and includes software, machine learning, and cloud technologies specialized in various applications such as safety, medical, defense, finance, and welfare. Through this, it is utilized in various fields throughout the industry to provide human convenience and new values. However, on the contrary, it is time to respond as intelligent and sophisticated cyber threats are increasing and accompanied by potential adverse functions such as securing the technical safety of new technologies. In this paper, we propose a new data modeling method to enable IoT integrated security control by utilizing artificial intelligence technology as a way to solve these adverse functions.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.1-13
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• 2020

Tencent is an internet-based entertainment platform corporation using technology to enrich the lives of Tencent platform users and assist the contents expansion. Since 2000, Tencent have developed a great growth and innovation in entertainment contents domain. Tencent have become the market leading innovator due to the imitative innovation and M&A. The present study designed case study analyses to investigate the mechanism with regard to the growth strategy of Tencent corporation. Tencent began with imitative internet-based game and social messaging services but then added its own wechat messenger platform, now being extended to other products or services. This imitative innovation strategy enabled Tencent corporation to grow rapidly, to achieve outstanding growth. In addition, Tencent's M&A investment drive is underpinned by a vision of top management team and flexible organizational culture, from building out the Tencent's entertainment platform, game, finance, e-commerce, to global market expansion. While our results shed light on the implications to understanding Tencent's growth, there are limitations of the current study that should be considered when designing next research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.1
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• pp.171-176
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• 2023

The purpose of this study is to analyze the introduction and use cases of Metaverse in the financial sector to learn lessons and implications. Let's take a look. The era of the metaverse is coming. The financial sector is pioneering the blue ocean market in a new era and working with the MZ generation. In order to expand contact points, we are very interested in the new business model, Metaverse, and are actively engaged in research and development. appear to be participating. In the case of finance, information is efficiently transmitted through metaverse, and customers It is predicted that the convenience of customers will be greatly improved by making it possible to use convenient services without visiting a branch. Additionally, by utilizing technologies such as AR and VR, we are trying to provide services linked to the metaverse in earnest. In addition, new financial services such as non-face-to-face asset management consulting services and brokerage services for funds through Metaverse Business models are also expected to be created. It is still in its infancy, and it is currently in its infancy, Metaverse is being used for educational purposes.