• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.13-19
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• 2018

MapReduce, the Hadoop's essential core technology, is most commonly used to process big data based on the Hadoop distributed file system. However, the existing MapReduce-based big data processing techniques have a feature of dividing and storing files in blocks predefined in the Hadoop distributed file system, thus wasting huge infrastructure resources. Therefore, in this paper, we propose an efficient MapReduce-based big data processing scheme. The proposed method enhances the storage efficiency of a big data infrastructure environment by converting and compressing the data to be processed into a data format in advance suitable for processing by MapReduce. In addition, the proposed method solves the problem of the data processing time delay arising from when implementing with focus on the storage efficiency.

• Korean Journal of Computational Design and Engineering
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• v.17 no.5
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• pp.342-352
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• 2012

As a futuristic construction model, building information model (BIM) based project management system (PMIS) and mobile BIM simulator apps have been showing visible sign. However, researches on the BIM based 3D simulator using mobile device are hard to find result from limitation of mobile device (slow speed at huge 3D file, display size, and etc.) and undefined standard of business processes. Therefore, this research aims at studying application of mobile BIM apps based on cloud computing. Total 8 BIM cloud apps were selected and analyzed in the 5 application feasibility characteristics (speed, view, inquiry, markup, and usability). This research would be essential phase to construct BIM based mobile project management system using cloud computing in the future.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2011.05a
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• pp.145-148
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• 2011

As a futuristic construction model, building information model(BIM) based project management system(PMIS) and mobile PMIS have been showing visible sign. However, researches on the 3D BIM based PMIS using mobile device are hard to find, result from limitation of mobile device application(slow speed at huge BIM file, display size, and etc.) and undefined standard of business processes. Therefore, this research aims at studying feasibility of mobile BIM PMIS based on cloud computing as a business model. In case of applying mobile BIM PMIS, 3D drawings and integrated building informations are possible on mobile devices in real time. it would support increasing the productivity of project participants as designer, engineer, supervisor, and etc. Globally, BIM based PMIS and Mobile BIM system, cloud computing based mobile BIM simulator are in the concept or experimental phase, therefore it is possible to secure global leading technology of IT and construction merger in the mobile BIM.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.2
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• pp.77-85
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• 2017

Due to the expansion of accommodation space caused by increase of population along with lifestyle changes, most of people spend their time indoor except for the travel time. Because of this, environmental change of indoor is very important, and it affects people's health and economy in resources. But, most of people don't acknowledge the importance of indoor environment. Thus, monitoring system for sustaining and managing indoor environment systematically is needed, and big data clusters should be used in order to save and manage numerous sensor data collected from many spaces. In this paper, we design a big data cluster for the indoor environment monitoring in order to store the sensor data and monitor unit of the huge building Implementation design big data cluster-based system for the analysis, and a distributed file system and building a Hadoop, HBase for big data processing. Also, various sensor data is saved for collection, and effective indoor environment management and health enhancement through monitoring is expected.

• The Journal of Society for e-Business Studies
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• v.9 no.1
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• pp.83-104
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• 2004

Recently, information systems that require storage and retrieval of huge amount of data are becoming used widely. Accordingly, research efforts have been made to develop Linux cluster file systems in the SAN environment in which clients themselves can manage metadata and access data directly. Also a semi-flat directory structure based on extendible hashing has been proposed to support fast retrieval of files[1]. In this research, we have designed and implemented the semi-flat extendible hash directory under the Linux system. In order to evaluate the practicality of the directory, we have also implemented the B+-tree based directory and experimented the performance. According to the performance comparisons, the extendible hash directory has the better performance at insert, delete, and search operations. On the other hand, the B+-tree directory is better at sorting files.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1249-1262
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• 2020

Along with the increase of the importance of information used in practice, adversaries tried to take valuable information in diverse ways. The simple and fundamental solution is to encrypt the whole data. Since the cost of encryption is increasing along with the size of data, the cost for securing the data is a burden to a system where the size of the data is not small. For the reason, in some applications where huge data are used for service, service providers do not use any encryption scheme for higher security, which could be a source of trouble. In this work, we introduce a new type of data securing technique named Trapdoor Digital Shredder(TDS) which disintegrates a data to multiple pieces to make it hard to re-construct the original data except the owner of the file who holds some secret keys. The main contribution of the technique is to increase the difficulty in obtaining private information even if an adversary obtains some shredded pieces. To prove the security of our scheme, we first introduce a new security model so called IND-CDA to examine the indistinguishability of shredded pieces. Then, we show that our scheme is secure under IND-CDA model, which implies that an adversary cannot distinguish a subset of shreds of a file from a set of random shreds.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.808-817
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• 2016

Recently, the wearable computing technology with bio-sensors has been rapidly developed and utilized in various areas such as personal health, care-giving for senior citizens who live alone, and sports activities. In particular, the wearable computing equipment to measure vital signs by means of digital yarns and bio sensors is noticeable. The wearable computing devices help users monitor and manage their health in their daily lives through the customized healthcare service. In this paper, we suggest a system for monitoring and analyzing vital signs utilizing smart healthcare clothing with bio-sensors. Vital signs that can be continuously acquired from the clothing is well-known as unstructured data. The amount of data is huge, and they are perceived as the big data. Vital sings are stored by Hadoop Distributed File System(HDFS), and one can build data warehouse for analyzing them in HDFS. We provide health monitoring system based on vital sings that are acquired by biosensors in smart healthcare clothing. We implemented a big data platform which provides health monitoring service to visualize and monitor clinical information and physical activities performed by the users.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.287-294
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• 2015

We are living in epoch of information when Internet touches all aspects of our lives. Therefore, it provides a plenty of services each of which benefits people in different ways. Electronic Mail (E-mail), File Transfer Protocol (FTP), Voice/Video Communication, Search Engines are bright examples of Internet services. Between them Social Network Services (SNS) continuously gain its popularity over the past years. Most popular SNSs like Facebook, Weibo and Twitter generate millions of data every minute. Twitter is one of SNS which allows its users post short instant messages. They, 100 million, posted 340 million tweets per day (2012)[1]. Often big amount of data contains lots of noisy data which can be defined as uninteresting and unclassifiable data. However, researchers can take advantage of such huge information in order to analyze and extract meaningful and interesting features. The way to collect SNS data as well as tweets is handled by crawlers. Twitter crawler has recently emerged as a great tool to crawl Twitter data as well as tweets. In this project, we develop Twitter Crawler system which enables us to extract Twitter data. We implemented our system in Java language along with MySQL. We use Twitter4J which is a java library for communicating with Twitter API. The application, first, connects to Twitter API, then retrieves tweets, and stores them into database. We also develop crawling strategies to efficiently extract tweets in terms of time and amount.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.685-703
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• 2024

Cloud computing provides each consumer with a large-scale computing tool. Different Cyber Attacks can potentially target cloud computing systems, as most cloud computing systems offer services to many people who are not known to be trustworthy. Therefore, to protect that Virtual Machine from threats, a cloud computing system must incorporate some security monitoring framework. There is a tradeoff between the security level of the security system and the performance of the system in this scenario. If strong security is needed, then the service of stronger security using more rules or patterns is provided, since it needs much more computing resources. A new way of security system is introduced in this work in cloud environments to the VM on account of resources allocated to customers are ease. The main spike of Fog computing is part of the cloud server's work in the ongoing study tells the step-by-step cloud server to change the tremendous measurement of information because the endeavor apps are relocated to the cloud to keep the framework cost. The cloud server is devouring and changing a huge measure of information step by step to reduce complications. The Medical Data Health-Care (MDHC) records are stored in Cloud datacenters and Fog layer based on the guard intensity and the key is provoked for ingress the file. The monitoring center sustains the Activity Log, Risk Table, and Health Records. Cloud computing and Fog computing were combined in this paper to review data movement and safe information about MDHC.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.924-942
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• 2005

Recently, large-scale directories have been developed for LINUX cluster file systems to store and retrieve huge amount of data. One of them, GFS directory, has attracted much attention because it is based on extendible hashing, one of dynamic hashing techniques, to support fast access to files. One distinctive feature of the GFS directory is the flat structure where all the leaf nodes are located at the same level of the tree. Hut one disadvantage of the mode structure is that the height of the mode tree has to be increased to make the tree flat after a byte is inserted to a full tree which cannot accommodate it. Thus, one byte addition makes the height of the whole mode tree grow, and each data block of the new tree needs one more link access than the old one. Another dynamic hashing technique which can be used for directories is linear hashing and a couple of researches have shown that it can get better performance at file access times than extendible hashing. [n this research, we have designed and implemented an extendible hashing directory and a linear hashing directory for large-scale LINUX cluster file systems and have compared performance between them. We have used the semi-flat structure which is known to have better access performance than the flat structure. According to the results of the performance evaluation, the linear hashing directory has shown slightly better performance at file inserts and accesses in most cases, whereas the extendible hashing directory is somewhat better at space utilization.