• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.171-178
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• 2019

Korea's shipbuilding industry is in a critical condition due to changes in the domestic and international environment. To overcome this crisis, preemptive development of products and technologies through prediction of new demand for ships is necessary. The goal of this research is to develop an artificial intelligence algorithm based on ship big data in order to predict new demand for ships. We intend to develop a big data analytics platform specialized in predicting ship demand and to utilize the forecast results of new ship demand through data analysis for planning/development of new products. By doing so, the development of sustainable new business models for equipment and equipment manufacturers will create new growth engines for shipyard and shipbuilders. Furthermore, it is expected that shipbuilders will be able to create business cases based on measurable performance, plan market-oriented products and services, and continuously achieve innovation that has high market destructive power.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.377-383
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• 2018

Internet of Things (IoT) is producing various data as the smart environment comes. The IoT data collection is used as important data to judge systems's status. Therefore, it is important to monitor the anomaly state of the sensor in real-time and to detect anomaly data. However, it is necessary to convert the IoT data into a normalized data structure for anomaly detection because of the variety of data structures and protocols. Thus, we can expect a good quality effect such as accurate analysis data quality and service quality. In this paper, we propose an anomaly detection system based on big data from collected sensor data. The proposed system is applied to ensure anomaly detection and keep data quality. In addition, we applied the machine learning model of support vector machine using anomaly detection based on time-series data. As a result, machine learning using preprocessed data was able to accurately detect and predict anomaly.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.111-119
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• 2024

In this paper, we propose a big data platform that can collect information from various sources collected at ocean. Currently operating ocean-related big data platforms are focused on storing and sharing created data, and each data provider is responsible for data collection and preprocessing. There are high costs and inefficiencies in collecting and integrating data in a marine environment using communication networks that are poor compared to those on land, making it difficult to implement related infrastructure. In particular, in fields that require real-time data collection and analysis, such as weather information, radar and sensor data, a number of issues must be considered compared to land-based systems, such as data security, characteristics of organizations and ships, and data collection costs, in addition to communication network issues. First, this paper defines these problems and presents solutions. In order to design a big data platform that reflects this, we first propose a data source, hierarchical MEC, and data flow structure, and then present an overall platform structure that integrates them all.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.2
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• pp.77-82
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• 2015

Big Data technology has been attracted much attention in aspect of fast data processing. Research of practicing Big Data technology is also ongoing to process large-scale structured data much faster in Relatioinal Database(RDB). Although there are lots of studies about measuring analyzing performance, studies about structured data loading performance, prior step of analyzing, is very rare. Thus, in this study, structured data in RDB is tested the performance that loads distributed processing platform Hadoop using Apache sqoop. Also in order to analyze the influence factors of data loading, it is tested repeatedly with different options of data loading and compared with data loading performance among RDB based servers. Although data loading performance of Apache Sqoop in test environment was low, but in large-scale Hadoop cluster environment we can expect much better performance because of getting more hardware resources. It is expected to be based on study improving data loading performance and whole steps of performance analyzing structured data in Hadoop Platform.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.10
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• pp.383-392
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• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.

• The Journal of Bigdata
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• v.2 no.2
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• pp.17-31
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• 2017

With the development of the big data environment, public institutions also have been providing big data infrastructures. Public data is one of the typical examples, and numerous applications using public data have been provided. One of the cases is related to the employment insurance. All employers have to make contracts for the employment insurance for all employees to protect the rights. However, there are abundant cases where employers avoid to buy insurances. To overcome these challenges, a data-driven approach is needed; however, there are lacks of methodologies to integrate, manage, and analyze the public data. In this paper, we propose a methodology to build a predictive model for identifying whether employers have made the contracts of employment insurance based on public data. The methodology includes collection, integration, pre-processing, analysis of data and generating prediction models based on process mining and data mining techniques. Also, we verify the methodology with case studies.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.269-278
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• 2000

Today the pedestrian-related problems a key subject requiring the attention of the traffic engineers for improving the transportation system. Particularly in urban and CBD locations, the pedestrian presents an element of sharp conflict with vehicular traffic. Therefore pedestrian movements must be studied for the purpose of providing guideline for the design and operation of walking transportation systems. This paper is to address the characteristics of walking transportation in a big city. Especially the focuses are emphasized on the ratio occupied by pedestrian traffic among the whole unlinked trips in a city and walking time. The data for analysis are collected in Seoul metropolitan city through sampling 1,006 citizens. Compared with other similar research works this paper utilized diversified tools to acquire more useful results. Finally, policy directions for pedestrian environment improvement were suggested.

• Journal of Information Technology Applications and Management
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• v.17 no.1
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• pp.11-33
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• 2010

The majority of graphs and visuals made publicly available by Thai listed companies tend to be disjointed and minimal. Only a little over fifty percent of the total 478 companies included graphic representations of their business operations and performance in the form of two or three dimensional spreadsheet based graphs in their annual reports, investor relations documents, websites and so on. For novice users, these visual representations are unlikely to give the big picture of what is the company's financial position and performance. Neither will they tell where the company stands in its own operating environment. The existing graphics and visuals, in very rare cases, can provide a sense of the company's future outlook. For boundary users such as audit committees whose duty is to promote good governance through transparency and disclosure, preliminary interview results show that there is some doubt as to whether the inclusion of big-picture visuals can really be of use to minority shareholders. These boundary users expect to see more insightful visuals beyond those produced by traditional spreadsheets which will enable them to learn to cope with the on-going turbulence in today's business environment more quickly. However, the debate is still going on as to where to draw the line between internal or external reporting visuals.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.89-97
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• 2020

This study was conducted to examine how independent audit firms in Vietnam understand and use risk based audit approach (RBAA) in audit practice. To answer the research questions, the researchers used primary and secondary data collected from 2018 to 2019. The results from the interview survey showed that audit firms were aware of the advantages of adopting RBAA. However, RBAA is practiced to a moderate extent by audit firms in Vietnam. Big 4 audit firms use RBAA more popularly than Non-Big 4 audit firms. The causes of the difference are the disadvantages of adopting RBAA and client's characteristics such as relevant guideline, audit fees, auditors' knowledge and experience. Besides, the study investigated factors impacting on the RBAA adoption by distributing a questionnaire to 246 auditors of 126 audit firms in Vietnam. A set of statistical appropriate methods where used through SPSS software version 22.0. The results indicated that there were six factors influencing RBAA adoption including: Auditor's ability, Technological development, Audit fees, auditors' motivation, Audit time and client's risk. Of which, auditor's ability and technological development are factors that have the most significant and positive impacts on the adoption of RBAA. Additional implications were argued in the final section of this study.

• ETRI Journal
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• v.44 no.1
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• pp.135-146
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• 2022

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real-time data analytics architecture for an IoT-based smart healthcare system, which consists of a wireless sensor network and a radio-frequency identification technology in a vertical domain. The proposed platform also includes high-performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff-Parkinson-White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real-time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT-enabling technology. Therefore, the proposed architecture can be used as a time-saving experimental environment for any IoT-based system.