• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.191-193
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• 2022

The advent of the 4th Industrial Revolution due to the trigger of digital technologies such as artificial intelligence and big data has caused many changes in society, culture, and industry. However, traditional markets in each region are not responding quickly to new distribution environments and consumer changes. In particular, in the case of traditional markets in Jeju, regional characteristics such as marketing strategies for tourists visiting Jeju have not been utilized. Therefore, this study proposes the establishment of a smart traditional market based on big data and artificial intelligence that utilizes the regional characteristics of Jeju. The research contents include customer profiling through visitor big data analysis, providing tourist movement results through traffic analysis, providing real-time popular product charts, and developing video-based fire and crime prevention functions.

• ETRI Journal
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• v.42 no.2
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• pp.247-257
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• 2020

Spatial experiences in route finding, such as the ability of finding low-traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route-finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience-based route-finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers' experiences is proposed. This ontology is employed to create an ontology-based route-finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route-finding method based on the ontology of drivers' experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.327-334
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• 2018

When a PC is infected with a malicious code, it communicates with the control and command (C&C) server and, by the attacker's instructions, spreads to the internal network and acquires information. The company focuses on preventing attacks from the outside in advance, but malicious codes aiming at APT attacks are infiltrated into the inside somehow. In order to prevent the spread of the damage, it is necessary to perform internal monitoring to detect a PC that is infected with malicious code and attempts to communicate with the C&C server. In this paper, a destination IP monitoring method is proposed in this paper using Bloom filter to quickly and effectively check whether the destination IP of many packets is in the blacklist.

• Information Systems Review
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• v.21 no.2
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• pp.1-27
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• 2019

It is possible to provide Smart Tourism Service through the development of information technology. It is necessary for the tourism industry to understand and utilize Big Data that has tourists' consumption patterns and service usage patterns in order to continuously create a new business model by converging with other industries. This study suggests to activate Jeju Smart Tourism by analyzing Big Data based on credit card usage records and location of tourists in Jeju. The results of the study show that First, the percentage of Chinese tourists visiting Jeju has decreased because of the effect of THAAD. Second, Consumption pattern of Chinese tourists is mostly occurring in the northern areas where airports and duty-free shops are located, while one in other regions is very low. The regional economy of Jeju City and Seogwipo City shows a overall stagnation, without changes in policy, existing consumption trends and growth rates will continue in line with regional characteristics. Third, we need a policy that young people flow into by building Jeju Multi-complex Mall where they can eat, drink, and go shopping at once because the number of young tourists and the price they spend are increasing. Furthermore, it is necessary to provide services for life-support related to weather, shopping, traffic, and facilities etc. through analyzing Wi-Fi usage location. Based on the results, we suggests the marketing strategies and public policies for understanding Jeju tourists' patterns and stimulating Jeju tourism industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.575-576
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• 2017

Recently, research have smart gateways can provide additional services through the IoT and Big Data. However, the existing System is number of devices connected increases to the Server, the stability of the network is degraded and data security is poor. In this paper, we design a smart gateway VPN tunneling control system based on IoT to solve these problems. we propose an effective VPN tunneling technology for low-end targets such as routers, and a method for efficiently controlling traffic in real-time in an environment where the quality of the Internet line changes dramatically. It is possible to control the sensor in the home safely through the VPN at the remote place.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.575-584
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• 2013

Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.

• KIISE Transactions on Computing Practices
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• v.23 no.12
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• pp.677-683
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• 2017

Apache Storm is a representative real-time distributed processing system, which is able to process data streams quickly over distributed servers. Storm currently provides several stream grouping methods to distribute data traffic to multiple servers. Among them, the shuffle grouping may cause a processing delay problem and the local-or-shuffle grouping used to solve the problem may cause the problem of concentrating the traffic on a specific node. In this paper, we propose the locality-aware grouping to solve the problems that may arise in the existing Storm grouping methods. Experimental results show that the proposed locality-aware grouping is considerably superior to the existing shuffle grouping and the local-or-shuffle grouping. These results show that the new grouping is an excellent approach considering both the locality and load balancing which are limitations of the existing Storm.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.67-78
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• 2016

Recently many countries around the world are actively promoting smart city projects to address various urban problems such as traffic congestion, housing shortage, and energy scarcity. Due to development of the Internet of Things (IoT), the development of a smart city with sustainability, convenience, and environment-friendliness was enabled through the effective control and reuse of urban resources. The purpose of this study is to analyze the technical trends of IoT and present a development plan for smart city which is one of the applications of the IoT. To this end, the news articles of the Electronic Times between 2013 and 2015were analyzed using the text mining technique and smart city development cases of other countries were investigated. The analysis results revealed the close relationships of big data, cloud, platforms, and sensors with smart city. For the successful development of a smart city, first, all the interested parties in the city must work together to create new values throughout the entire process of value chain. Second, they must utilize big data and disclose public data more actively than they are doing now. This study has made academic contribution in that it has presented a big data analysis method and stimulated follow-up studies. For the practical contribution, the results of this study provided useful data for the policy making of local governments and administrative agencies for smart city development. This study may have limitations in the incorporation of the total trends because only the news articles of the Electronic Times were selected to analyze the technical trends of the IoT.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.1033-1041
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• 2017

Path travel time based on departure time (PTTDP) is key information in advanced traveler information systems (ATIS). Despite the necessity, forecasting PTTDP is still one of challenges which should be successfully conquered in the forecasting area of intelligent transportation systems (ITS). To address this problem effectively, a methodology to dynamically predict PTTDP between motorway interchanges is proposed in this paper. The method was developed based on the relationships between traffic demands at motorway tollgates and PTTDPs between TGs in the motorway network. Two different data were used as the input of the model: traffic demand data and path travel time data are collected by toll collection system (TCS) and dedicated short range communication (DSRC), respectively. The proposed model was developed based on k-nearest neighbor, one of data mining techniques, in order for the real applications of motorway information systems. In a feasible test with real-world data, the proposed method performed effectively by means of prediction reliability and computational running time to the level of real application of current ATIS.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.65-70
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• 2019

In recently, ship trajectory data consisting of ship position, speed, course, and so on can be obtained from the Automatic Identification System device with which all ships should be equipped. These data are gathered more than 2GB every day at a crowed sea port and used for analysis of ship traffic statistic and patterns. In this study, we propose a method to process ship trajectory data efficiently with distributed computing resources using MapReduce algorithm. In data preprocessing phase, ship dynamic and static data are integrated into target dataset and filtered out ship trajectory that is not of interest. In mapping phase, we convert ship's position to Geohash code, and assign Geohash and ship MMSI to key and value. In reducing phase, key-value pairs are sorted according to the same key value and counted the ship traffic number in a grid cell. To evaluate the proposed method, we implemented it and compared it with IALA waterway risk assessment program(IWRAP) in their performance. The data processing performance improve 1 to 4 times that of the existing ship trajectory analysis program.