한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제21권2호
  • /
  • Pages.211-223
  • /
  • 2018
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  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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교통정보 추론을 위한 비정형데이터 분석과 다중패턴저장 기법

Unstructured Data Analysis and Multi-pattern Storage Technique for Traffic Information Inference

  • Kim, Yonghoon (Dept. of Computer Engineering, Pukyong National University) ;
  • Kim, Booil (Dept. of Electrical, Electronics and Software Engineering, Pukyong National University) ;
  • Chung, Mokdong (Dept. of Computer Engineering, Pukyong National University)
  • 투고 : 2018.01.12
  • 심사 : 2018.01.24
  • 발행 : 2018.02.28
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초록

To understand the meaning of data is a common goal of research on unstructured data. Among these unstructured data, there are difficulties in analyzing the meaning of unstructured data related to corpus and sentences. In the existing researches, the researchers used LSA to select sentences with the most similar meaning to specific words of the sentences. However, it is problematic to examine many sentences continuously. In order to solve unstructured data classification problem, several search sites are available to classify the frequency of words and to serve to users. In this paper, we propose a method of classifying documents by using the frequency of similar words, and the frequency of non-relevant words to be applied as weights, and storing them in terms of a multi-pattern storage. We use Tensorflow's Softmax to the nearby sentences for machine learning, and utilize it for unstructured data analysis and the inference of traffic information.

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참고문헌

  1. C.J. Kim and K.B. Hwang, "Comparative Study of Machine Learning Techniques for Spammer Detection in Social Bookmarking System," Journal of Korean Institute of Information Scientests and Engineers Transations on Computing Practices, Vol. 15, No. 5, pp. 345-349, 2009.
  2. Y.H. Kim and M.D. Chung, "Unstructured Data Service Model Utilizing Context-Aware Big Data Analysis," Journal of Advances in Computer Science and Ubiquitous Computing, Vol. 421, pp. 926-931, 2016.
  3. K. Zupanc and Z. Bosnic, "Automated Essay Evaluation with Semantic Analysis," Knowledge-Based Systems, Vol. 120, pp. 118-132, 2017. https://doi.org/10.1016/j.knosys.2017.01.006
  4. L. Hellsten and L. Loet, "Automated Analysis of Topic-Actor Networks on Twitter: New Approach to the Analysis of Socio-semantic Networks," arXiv preprint arXiv:1711.08387, 2017.
  5. Y.S. Song and Y.M. Kwon, “Experiment of non-specialist entering into artificial intelligence field: Based on Tensor Flow,” Journal of Korea Multimedia Society, Vol. 20, No. 3, pp. 53-61, 2016.
  6. P.D. Turney and P. Pantel, "From Frequency to Meaning: Vector Space Models of Semantics," Journal Artificial Intelligence Research, Vol. 37, pp. 141-188, 2010.
  7. Google Inc., https://www.tensorflow.org, 2017.
  8. D.K. Shin and E.Y Ahn, “A Pen Drawing Method by Tensor-based Strokes Generation,” Journal of Korea Multimedia Society, Vol. 20, No. 4, pp. 713-720, 2017. https://doi.org/10.9717/kmms.2017.20.4.713
  9. S. Galit, P.C. Bruce, M.L. Stephens, and N.R. Patel, Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner , 3rd Edition, John Wiley and Sons, Inc., 2016.
  10. N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A Convolutional Neural Network for Modelling Sentences," Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 655-665, 2014.
  11. J. Zbontar and Y. Yann, "Computing the Stereo Matching Cost with a Convolutional Neural Network," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592-1599, 2015.
  12. J. Jin, X. Ma, and I. Kosonen, "An Intelligent Control System for Traffic Lights with Simulation-based Evaluation," Control Engineering Practice, Vol. 58, pp. 24-33, 2017. https://doi.org/10.1016/j.conengprac.2016.09.009
  13. S.S. Sandhu, N. Jain, A. Gaurav, and N.C.S.N Lyengar, “Agent Based Intelligent Traffic Management System for Smart Cities,” International Journal of Smart Home, Vol. 9, No. 12, pp. 307-316, 2015. https://doi.org/10.14257/ijsh.2015.9.12.31
  14. R. Sundar, S. Hebbar, and V. Golla, “Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection,” IEEE Sensors J ournal, Vol. 15, No. 2, pp. 1109-1113, 2015. https://doi.org/10.1109/JSEN.2014.2360288
  15. Y.H. Kim and M.D. Chung, "Traffic Prediction System Utilizing Application and Control of Environmental Information," Advances in Computer Science and Ubiquitous Computing, Vol. 474, pp. 1043-1050, 2017.