• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.5948-5971
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• 2019

Estimating loss rates with the packet traces captured from some point in the middle of the network has received much attention within the research community. Meanwhile, existing intermediate-point methods like [1] require the capturing system to capture all the TCP traffic that crosses the border of an access network (typically Gigabit network) destined to or coming from the Internet. However, limited to the performance of current hardware and software, capturing network traffic in a Gigabit environment is still a challenging task. The uncaptured packets will affect the total number of captured packets and the estimated number of packet losses, which eventually affects the accuracy of the estimated loss rate. Therefore, to obtain more accurate loss rate, a method of strengthening packet loss measurement from the network intermediate point is proposed in this paper. Through constructing a series of heuristic rules and leveraging the binomial distribution principle, the proposed method realizes the compensation for the estimated loss rate. Also, experiment results show that although there is no increase in the proportion of accurate estimates, the compensation makes the majority of estimates closer to the accurate ones.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.45-52
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• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4063-4086
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• 2016

Cloud computing is a robust technology, which facilitate to resolve many parallel distributed computing issues in the modern Big Data environment. Hadoop is an ecosystem, which process large data-sets in distributed computing environment. The HDFS is a filesystem of Hadoop, which process data blocks to the cluster nodes. The data block placement has become a bottleneck to overall performance in a Hadoop cluster. The current placement policy assumes that, all Datanodes have equal computing capacity to process data blocks. This computing capacity includes availability of same storage media and same processing performances of a node. As a result, Hadoop cluster performance gets effected with unbalanced workloads, inefficient storage-tier, network traffic congestion and HDFS integrity issues. This paper proposes a storage-tier-aware Robust Data Placement (RDP) scheme, which systematically resolves unbalanced workloads, reduces network congestion to an optimal state, utilizes storage-tier in a useful manner and minimizes the HDFS integrity issues. The experimental results show that the proposed approach reduced unbalanced workload issue to 72%. Moreover, the presented approach resolve storage-tier compatibility problem to 81% by predicting storage for block jobs and improved overall data block placement by 78% through pre-calculated computing capacity allocations and execution of map files over respective Namenode and Datanodes.

• 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.

• 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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.4
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• pp.311-318
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• 2016

The VTS concept was located as a principal methods of maritime safety administration in world's major harbors and expected to become the pivotal role for the future of the maritime and harbor society with e-Navigation epoch. If recent limelight concept of big-data has been included in aspect of information gathering and analysis with various studies, it's required advanced studies to improve the information analysis capability and application range of the data that can be mining by the VTS. In this study, contrast to other studies that aimed quantitative analysis as communication number, it can be mining the time information and each of the communication VTS for the target vessel, including qualitative analysis, such as the purpose or the type of communication. This comparison across multiple items of the collected information, and presenting the VTS data mining model (VCDF) that can be analyzed for the purpose of analyzing way, type and number of communication by ship's type, also number of violations through VTS communication. First, In Busan port case, it shows frequently information service and shows frequently communicating with particular types of vessels. Second, Passive VTS carried out notwithstanding many kinds of traffic violations due to communication congestion. This arranged information can be used as data for the analysis, as possible the level of traffic for VTSO situational awareness, which pointed to the 'workloads' in 'IALA Guideline' and could be used as a database for future research of e-Navigation.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.133-139
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• 2020

Big data, artificial intelligence (AI), and machine learning are keywords that represent the Fourth industrial Revolution. In addition, as the development of science and technology, the Korean government, public institutions and industries want professionals who can collect, analyze, utilize and predict data. This means that data analysis and utilization education become more important. Education on data analysis and utilization is increasing with trends in other academy. However, it is true that not many academy run long-term and systematic education. Korea Institute of Science and Technology Information (KISTI) is a data ecosystem hub and one of its performance missions has been providing data utilization and analysis education to meet the needs of industries, institutions and governments since 1966. In this study, KISTI's data education was analyzed using the number of curriculum trainees per year from 2001 to 2019. With this data, the change of interest in education in information and data field was analyzed by reflecting social and historical situations. And we identified the characteristics of KISTI and trainees. It means that the identity, characteristics, infrastructure, and resources of the institution have a greater impact on the trainees' interest of data-use education.In particular, KISTI, as a research institute, conducts research in various fields, including bio, weather, traffic, disaster and so on. And it has various research data in science and technology field. The purpose of this study can provide direction forthe establishment of new curriculum using data that can represent KISTI's strengths and identity. One of the conclusions of this paper would be KISTI's greatest advantages if it could be used in education to analyze and visualize many research data. Finally, through this study, it can expect that KISTI will be able to present a new direction for designing data curricula with quality education that can fulfill its role and responsibilities and highlight its strengths.

• 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.

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.67-78
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• 2017

Traffic congestion cost is increasing annually. Specifically congestion caused by the CDB traffic contains more than a half of the total congestion cost. Recent advancement in the field of Big Data, AI paved the way to industry revolution 4.0. And, these new technologies creates tremendous changes in the traffic information dissemination. Eventually, accurate and timely traffic information will give a positive impact on decreasing traffic congestion cost. This study, therefore, focused on developing both recurrent and non-recurrent congestion prediction models on urban roads by adopting Recurrent Neural Network(RNN), a tribe in machine learning. Two hidden layers with scaled conjugate gradient backpropagation algorithm were selected, and tested. Result of the analysis driven the authors to 25 meaningful links out of 33 total links that have appropriate mean square errors. Authors concluded that RNN model is a feasible model to predict congestion.