• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.101-109
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• 2014

Medical technology development and increase the income level of a "Long and healthy Life=Wellness," with the growing interest in actively promoting and maintaining health and wellness has become enlarged. In addition, the demand for personalized health care services is growing and extensive medical moves of big data, disease prevention, too. In this paper, the main interest in the market, highlighting wellness in order to support big data-driven healthcare quality through patient-centered medical services purposes. Patients with drug dependence treatment is not to diet but to improve disease prevention and treatment based on analysis of big data. Analysing your Tweets-daily information and wellness disease prevention and treatment, based on the purpose of the dictionary. Efficient big data analysis for node while increasing processing time experiment. Test result case of total access time efficient 26% of one node to three nodes and case of data storage is 63%, case of data aggregate is 18% efficient of one node to three nodes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.286-299
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• 2017

The Korean performing arts has been facing stagnation due to oversupply, lack of effective distribution system, and insufficient business models. In order to overcome these difficulties, it is necessary to improve the efficiency and accuracy of marketing by using more objective market data, and to secure audience development and loyalty. This study considers the viewpoint that 'Big Data' could provide more general and accurate statistics and could ultimately promote tailoring services for performances. We examine the first case of Big Data analysis conducted by a credit card company as well as Big Data's characteristics, analytical techniques, and the theoretical background of performing arts consumer analysis. The purpose of this study is to identify the meaning and limitations of the analysis case on performing arts by Big Data and to overcome these limitations. As a result of the case study, incompleteness of credit card data for performance buyers, limits of verification of existing theory, low utilization, consumer propensity and limit of analysis of purchase driver were derived. In addition, as a solution to overcome these problems, it is possible to identify genre and performances, and to collect qualitative information, such as prospectors information, that can identify trends and purchase factors.combination with surveys, and purchase motives through mashups with social data. This research is ultimately the starting point of how the study of performing arts consumers should be done in the Big Data era and what changes should be sought. Based on our research results, we expect more concrete qualitative analysis cases for the development of audiences, and continue developing solutions for Big Data analysis and processing that accurately represent the performing arts market.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5023-5038
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• 2017

Big data is an emerging technology which deals with wide range of data sets with sizes beyond the ability to work with software tools which is commonly used for processing of data. When we consider a huge network, we have to process a large amount of network information generated, which consists of both normal and abnormal activity logs in large volume of multi-dimensional data. Intrusion Detection System (IDS) is required to monitor the network and to detect the malicious nodes and activities in the network. Massive amount of data makes it difficult to detect threats and attacks. Sequential Pattern mining may be used to identify the patterns of malicious activities which have been an emerging popular trend due to the consideration of quantities, profits and time orders of item. Here we propose a sequential pattern mining algorithm with fuzzy logic feature selection and fuzzy weighted support for huge volumes of network logs to be implemented in Apache Hadoop YARN, which solves the problem of speed and time constraints. Fuzzy logic feature selection selects important features from the feature set. Fuzzy weighted supports provide weights to the inputs and avoid multiple scans. In our simulation we use the attack log from NS-2 MANET environment and compare the proposed algorithm with the state-of-the-art sequential Pattern Mining algorithm, SPADE and Support Vector Machine with Hadoop environment.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.975-987
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• 2013

The Hadoop system was developed by the Apache foundation based on GFS and MapReduce technologies of Google. Many modern systems for managing and processing the big data have been developing based on the Hadoop because the Hadoop was designed for scalability and distributed computing. The R software has been considered as a well-suited analytic tool in the Hadoop based systems because the R is flexible to other languages and has many libraries for complex analyses. We introduced Rhipe which is a R package supporting MapReduce programming easily under the Hadoop system, and implemented a MapReduce program using Rhipe for multiple regression especially. In addition, we compared the computing speeds of our program with the other packages (ff and bigmemory) for processing the large data. The simulation results showed that our program was more fast than ff and bigmemory as the size of data increases.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.253-258
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• 2014

Recently, the development of cloud computing technology is developed as soon as smartphones is increases, and increased that users want to receive big data service. Hadoop framework of the big data service is provided to hadoop file system and hadoop mapreduce supported by data-intensive distributed applications. But, smpartphone service using hadoop system is a very vulnerable state to data authentication. In this paper, we propose a initial authentication protocol of hadoop system assisted by smartphone service. Proposed protocol is combine symmetric key cryptography techniques with ECC algorithm in order to support the secure multiple data processing systems. In particular, the proposed protocol to access the system by the user Hadoop when processing data, the initial authentication key and the symmetric key instead of the elliptic curve by using the public key-based security is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3182-3202
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• 2015

Flexibly expanding the storage capacity required to process a large amount of rapidly increasing unstructured log data is difficult in a conventional computing environment. In addition, implementing a log processing system providing features that categorize and analyze unstructured log data is extremely difficult. To overcome such limitations, we propose and design a MongoDB-based unstructured log processing system (MdbULPS) for collecting, categorizing, and analyzing log data generated from banks. The proposed system includes a Hadoop-based analysis module for reliable parallel-distributed processing of massive log data. Furthermore, because the Hadoop distributed file system (HDFS) stores data by generating replicas of collected log data in block units, the proposed system offers automatic system recovery against system failures and data loss. Finally, by establishing a distributed database using the NoSQL-based MongoDB, the proposed system provides methods of effectively processing unstructured log data. To evaluate the proposed system, we conducted three different performance tests on a local test bed including twelve nodes: comparing our system with a MySQL-based approach, comparing it with an Hbase-based approach, and changing the chunk size option. From the experiments, we found that our system showed better performance in processing unstructured log data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.1 no.2
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• pp.137-143
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• 1997

From now on, the network is being developed into PSTN(public switched telephone network) and PDN(public data network), that is depend on the form of data. The former one pursues sending voice, and the latter one pursues sending data. But it causes big loss of the economy and efficiency. So, ISDN, processing voice and data at same time, gives a big profit to user. To enlarge the ISDN at the narrow area, it is necessary that study to send the mixture form of voice and data in LAN environment. So, this paper proposes the algorithm about the mixture form of voice and data in ethernet and token-ring. that is widely used in these days.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2211-2232
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• 2018

The unstructured and semi-structured big data in social network poses new challenges in query retrieval. This requirement needs to be met by introducing quality retrieval time measures like indexing. Due to the huge volume of data storage, there originate the need for efficient index algorithms to promote query processing. However, conventional algorithms fail to index the huge amount of frequently obtained information in real time and fall short of providing scalable indexing service. In this paper, a new LIndex algorithm, which is a heuristic on Lucene is built on Neo4jHA architecture that holds the social network Big data. LIndex is a flexible and simplified adaptive indexing scheme that ascendancy decomposed shortest paths around term neighbors as basic indexing unit. This newfangled index proves to be effectual in query space pruning of graph database Neo4j, scalable in index construction and deployment. A graph query is processed and optimized beyond the traditional Lucene in a time-based manner to a more efficient path method in LIndex. This advanced algorithm significantly reduces query fetch without compromising the quality of results in time. The experiments are conducted to confirm the efficiency of the proposed query retrieval in Neo4j graph NoSQL database.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.391-396
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• 2024

This study proposes a novel reverse N-Gram approach to overcome the limitations of traditional N-Gram methods and enhance performance in building an entity dictionary specialized for the healthcare sector. The proposed reverse N-Gram technique allows for more precise analysis and processing of the complex linguistic features of healthcare-related big data. To verify the efficiency of the proposed method, big data on healthcare and digital health announced during the Consumer Electronics Show (CES) held each January was collected. Using the Python programming language, 2,185 news titles and summaries mentioned from January 1 to 31 in 2010 and from January 1 to 31 in 2024 were preprocessed with the new reverse N-Gram method. This resulted in the stable construction of a dictionary for natural language processing in the healthcare field.