An Interpretation and Translation System Using Location Based Big Data Analysis

SANGJIN, LEE; Seokhoon Kim

doi:10.34163/jkits.2017.12.2.001

Nowadays, an accuracy of interpretation and translation system, which are based on a natural language, are quotidianly increasing, because a big data processing method has been dramatically progressed. However, there are still some lacks for an user who is needed a colloquial style interpretation and translation. To this end, we propose an interpretation and translation system for the user, which are using a location information based on big data analysis. When the proposed system collect data, the system is preferentially gathering the data which are generated in the near regions, and which are related with the regions. At this time, the criteria of data gathering process are each data location information. And then, the system will configure various data set by their data type. The configured data set, which it will be used pre-learning parameters in a deep neural network. Based on these parameters for the pre-learning process, the proposed system carry out a deep learning process. By doing this, the system provide a improved interpretation and translation result for colloquial style. To verify an excellence of the system, we compare with an existing system. In the performance evaluation, the proposed system show enhanced results about 10% than the existing system.

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* 2022년 이후 발행 논문의 참고문헌은 현재 구축 중입니다.

[web] K. Panetta / 2016 / Top 10 strategic technology trends 2017, design and implementation of network virtual keyboard using smartphone / Gartner / http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/

[journal] 김난주 / 2016 / Security Technology Framework in Bigdata Environment / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 11(3) : 251~260

[journal] E. P. Xing / 2015 / Petuum: A new platform for distributed machine learning on big data / IEEE Transactions on Big Data 1(2) : 49~67

[journal] 최재영 / 2016 / A Reliable Service Provided Model for Session Hijacking Attacks in Big Data Service Environments / 한국지식정보기술학회 논문지 / 한국지식정보기술학회 11(6) : 597~606

[report] Korea Tourism Organization / 2016 / 2015 Global online trend

[other] J. Dean / 2016 / Building Google: From search engine to AI poster child / Structure Data 2016

[journal] J. K. Choi / 2016 / Current status and implications of big data analysis at domestic and abroad / KISTEP InI 2016(14) : 33~43

[journal] Y-K. Ki / 2016 / Research trends in artificial intelligence and deep learning / Weekly Technology Trends 1736 : 10~19

[journal] O. G. Min / 2016 / Research and development trends of artificial intelligence via evolving a bigdata / Weekly Technology Trends 1735 : 10~21

[journal] D. K. Kang / 2016 / Machine learning technology trends based on deep learning / Weekly Technology Trends 1742 : 12~24

[journal] N-J. Kwak / 2016 / Deep learning technology trends / Weekly Technology Trends 1745 : 13~25

[other] G. Hinton / 2015 / NIPS deep learning / NIPS Tutorial

[journal] M. A. Alsheikh / 2014 / Machine learning in wireless sensor networks: algorithms, strategies, and applications / IEEE Communications Surveys and Tutorials 16(4) : 1996~2018

[journal] A. Akusok / 2015 / High-performance extreme learning machines: A complete toolbox for big data applications / IEEE Access 3 : 1011~1025

[journal] S. Nan / 2017 / Density-dependent quantized least squares support vector machine for large data sets / IEEE Transactions on Neural Networks and Learning Systems 28(1) : 94~106

[journal] S-H. Kim / 2015 / Real-time interpreting and translating system based on analyzing big data for tourists / Indian Journal of Science and Technology 8(25) : 1~6

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