• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.75-78
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• 2017

This paper compares operational and network analysis of centralized and distributed repository for big data solutions in the IoT enabled Smart Grid environment. The comparative analysis clearly depicts that centralize repository consumes less memory consumption while distributed locality-based repository reduce network complexity issues than centralize repository in state-of-the-art Big Data Solution.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.155-164
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• 2016

In this paper, we investigate and analyze big data platform published at home and abroad. The results had a problem with personal information security on each platform. In particular, there was a vulnerability in the encryption of personal information stored in big data representative of HBase NoSQL DB that is commonly used for big data platform. However, data encryption and decryption cause the system load. In this paper, we propose a method of encryption with HBase, encryption and decryption systems, and methods for applying the personal information management system (PMIS) for each step of the way and big data platform to reduce the load on the network to communicate. And we propose a new big data platform that reflects this. Therefore, the proposed Big Data platform will greatly contribute to the activation of Big Data used to obtain personal information security and system performance efficiency.

• Journal of Platform Technology
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• v.8 no.2
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• pp.3-9
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• 2020

Big Data is a new concept in the global and local area. This field has gained tremendous momentum in the recent years and has attracted attention of several researchers. Big Data is a data analysis methodology enabled by recent advances in information and communications technology. However, big data analysis requires a huge amount of computing resources making adoption costs of big data technology. Therefore, it is not affordable for many small and medium enterprises. We survey the concepts and characteristics of Big Data along with a number of tools like HADOOP, HPCC for managing Big Data. It also presents an overview of big data like Characteristics of Big data, big data technology, big data management tools etc. We have also highlighted on some challenges and opportunities related to the fields of big data.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.84-90
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• 2019

Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It's also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business' changing requirements, if they prove to be highly valuable to business environment.

• Proceedings of the Korea Contents Association Conference
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• 2017.05a
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• pp.33-34
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• 2017

In 2015, Ministry of Health and Welfare of Korea announced a research and development plan of using Korean healthcare data to support decision making, reduce cost and enhance a better treatment. This project relies on the adoption of BigData technology such as Apache Hadoop, Apache Spark to store and process HealthCare Data from various institution. Here we present an approach a design and implementation of OLAP server in Korean HealthCare BigData platform. This approach is used to establish a basis for promoting personalized healthcare research for decision making, forecasting disease and developing customized diagnosis and treatment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1886-1908
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• 2020

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them. Additionally, the presence of the imbalance data in big data is a massive concern to the research industry. In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework. In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm. The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm. The two-level query matching is performed for determining the requested data. The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data. The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21]. Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets. The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.61-68
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• 2021

The development of the fourth industry has enabled users to quickly share a lot of data online. We can analyze big data on information about tourist attractions and users' experiences and opinions using artificial intelligence. It can also analyze the association between characteristics of users and types of tourism. This paper analyzes individual characteristics, recommends customized tourist sites and proposes a system to provide the sacred texts of recommended tourist sites as AR services. The system uses machine learning to analyze the relationship between personality type and tourism type preference. Based on this, it recommends tourist attractions according to the gender and personality types of users. When the user finishes selecting a tourist destination from the recommendation list, it visualizes the information of the selected tourist destination with AR.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.607-612
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• 2015

As the amount of vehicle's diagnostics data increases, the actors in automotive ecosystem will encounter difficulties to perform a real time analysis in order to simulate or to design new services according to the data gathered from the connected cars. In this paper, we have conducted a study of a Big Data solution that expresses the essential deep analytics to process and analyze vast quantities of vehicles on board diagnostics data generated by cars. Hadoop and its ecosystems have been deployed to process a large data and delivered useful outcomes that may be used by actors in automotive ecosystem to deliver new services to car owners. As the Intelligent transport system is involved to guarantee safety, reduce rate of crash and injured in the accident due to speed, addressing big data solution based on vehicle diagnostics data is upcoming to monitor real time outcome from it and making collection of data from several connected cars, facilitating reliable processing and easier storage of data collected.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.20-25
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• 2016

With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name node's main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.