• The Journal of Bigdata
• /
• v.4 no.2
• /
• pp.195-205
• /
• 2019

Linked Open Data (LOD) is rated as the best of any kind of data disclosure, and allows you to search related data by linking them in a standard format across the Internet. There is an increasing number of cases in which relevant data are constructed in the LOD form in the global environment, but in the domestic healthcare sector, the disclosure of data in the form of LOD is still at the beginning stage. In this paper, we introduce a case of LOD platform construction that provides services by linking domestic and international related data by LOD method, based on the data of Korean medical research paper data and health care big data linkage platform. Linking all data from each DB into an LOD requires a lot of time and effort, and is basically an infrastructure task that government or public institutions should be in charge of rather than the private sector. In this study, ten domestic and foreign LOD sites were linked with only a portion of each DB, enabling users to link data from various domestic and foreign organizations in a convenient manner.

• Journal of Convergence Society for SMB
• /
• v.6 no.3
• /
• pp.13-19
• /
• 2016

In various ICBM (IoT, Bigdata, Cloud, Mobile) IT convergence environments, IT technologies have been evolved, new information security threats have been occurred. As information security incidents in major public agencies, financial institutions and companies occurred, it was emphasized that the importance of human security was disclosed. Thus, implementing of information security management system could protect hacks and security breaches and respond quickly to accidents so it minimized the sized of loss. In this paper, comparison of human security controls shown in ISO27001, COBIT, NIST 800-53, K-ISMS, Cyber Security Framework such as the main information security management systems was analyzed, and proposed of the security implications about effective controls of human resources security issues.

• The Journal of Bigdata
• /
• v.1 no.1
• /
• pp.9-17
• /
• 2016

This study identifies major features in water supply and introduces important factors in water services based on the information from data mining analysis of water quantity and water pressure measured from sensors. It also suggests more accurate methods using multiple regression analysis and neural network in predicting short term prediction of water demand in water service. A small block of a county is selected for the data collection and tests. There isa water demand on business such as public offices and hospitalstoo in this area. Real stream data from sensors in this area is collected. Among 2,728 data sets collected, 2,632 sets are used for modelling and 96 sets are used for testing. The shows that neural network is better than multiple regression analysis in their prediction performance.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.181-188
• /
• 2020

As the size of the airport terminal grows in line with the rapid growth of aviation passengers, the advanced baggage handling system that combines various data technologies has become an essential element in order to handle the baggage carried by passengers swiftly and accurately. Therefore, this study introduces the method of analyzing the baggage handling capacity of domestic airports through the latest data analysis methodology from the process point of view to advance the operation of the airport BHS and the main points based on event log data. By presenting an accurate load prediction method, it can lead to advanced BHS operation strategies in the future, such as the preemptive arrangement of resources and optimization of flight-carrousel scheduling. The data used in the analysis utilized the APIs that can be obtained by searching for "Korea Airports Corporation" in the public data portal. As a result of applying the method to the domestic airport BHS simulation model, it was possible to confirm a high level of predictive performance.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.55-63
• /
• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• The Journal of Bigdata
• /
• v.6 no.1
• /
• pp.161-178
• /
• 2021

The importance of 'Big Data' is increasing to the point that it is likened to '21st century crude oil'. For smart city IoT data, attention should be paid to quality control as the quality of data is associated with the quality of public services. However, data quality indicators presented through ISO/IEC organizations and domestic/foreign organizations are limited to the 'User' perspective. To complement these limitations, the study derives supplier-centric indicators and their priorities. After deriving 3 categories and 13 indicators of supplier-oriented smart city IoT data quality evaluation indicators, we derived the priority of indicator categories and data quality indicators through AHP analysis and investigated the feasibility of each indicator. The study can contribute to improving sensor data quality by presenting the basic requirements that data should have to individuals or companies performing the task. Furthermore, data quality control can be performed based on indicator priorities to provide improvements in quality control task efficiency.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.35-49
• /
• 2018

It seems unrealistic to say that fried chicken, often known as the American soul food, has one of the biggest markets in South Korea. Yet, South Korea owns more numbers of fried chicken restaurants than those of McDonald's franchise globally[4]. Needless to say not all these fast-food commerce survive in such small country. In this study, we propose a predictive model that could potentially help one's decision whilst deciding to open a store. We've extracted all fried chicken restaurants registered at the Korean Ministry of the Interior and Safety, then collected a number of features that seem relevant to a store's closure. After comparing the results of different algorithms, we conclude that in order to best predict a store's survival is FDA(Flexible Discriminant Analysis). While Neural Network showed the highest prediction rate, FDA showed better balanced performance considering sensitivity and specificity.

• The Journal of Bigdata
• /
• v.7 no.1
• /
• pp.37-47
• /
• 2022

Autonomous delivery operation data is the key to driving a paradigm shift for last-mile delivery in the Corona era. To bridge the technological gap between domestic autonomous delivery robots and overseas technology-leading countries, large-scale data collection and verification that can be used for artificial intelligence training is required as the top priority. Therefore, overseas technology-leading countries are contributing to verification and technological development by opening AI training data in public data that anyone can use. In this paper, 326 objects were collected to trainn autonomous delivery robots, and artificial intelligence models such as Mask r-CNN and Yolo v3 were trained and verified. In addition, the two models were compared based on comparison and the elements required for future autonomous delivery robot research were considered.

• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.43-73
• /
• 2022

In order to improve productivity, reduce costs, and improve decision-making efficiency, which are one of the main contents of the digital transformation promotion goal, many companies are promoting the introduction of various IT for digital transformation. Information technology (IT) is a key means of determining competitiveness, and the IT adoption worldwide is increasing every year. The financial industry is also actively introducing huge amounts of IT every year to generate profits, improve work efficiency, and secure a strategic competitive advantage. Compared to some studies on the IT adoption in the public and corporate sectors, empirical studies that reflect the characteristics of the financial industry are insufficient. In this study, the purpose of this study was to derive factors affecting the IT adoption in the financial industry for the promotion of digital transformation, and to analyze weights and priorities. By revealing through data analysis that there is a difference in the relative priorities of factors in the financial IT adoption for each group, it can be used as a reference model for which factors should be considered prior to IT adoption from the perspective of each group. It will be meaningful in that it exists.

• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.91-102
• /
• 2022

As artificial intelligence develops, AI chatbot systems are actively taking place. For example, in public institutions, the use of chatbots is expanding to work assistance and professional knowledge services in civil complaints and administration, and private companies are using chatbots for interactive customer response services. In this study, we propose a scenario-based AI voice chatbot system to reduce museum operating costs and provide interactive guidance services to visitors. The implemented voice chatbot system consists of a watcher object that detects the user's voice by monitoring a specific directory in real-time, and an event handler object that outputs AI's response voice by performing inference by model sequentially when a voice file is created. And Including a function to prevent duplication using thread and a deque, GPU operations are not duplicated during inference in a single GPU environment.