KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Visualization Tool of Distortion-Free Time-Series Matching

왜곡 제거 시계열 매칭의 시각화 도구

  • 문성우 (강원대학교 컴퓨터과학과) ;
  • 이상훈 (강원대학교 컴퓨터과학과) ;
  • 김범수 (한국과학기술원 산업경영연구소) ;
  • 문양세 (강원대학교 컴퓨터과학과)
  • Received : 2015.07.29
  • Accepted : 2015.09.03
  • Published : 2015.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper we propose a visualization tool for distortion-free time-series matching. Supporting distortion-free is a very important factor in time-series matching to get more accurate matching results. In this paper, we visualize the result of time-series matching, which removes various time-series distortions such as noise, offset translation, amplitude scaling, and linear trend by using moving average, normalization, linear detrending transformations, respectively. The proposed visualization tool works as a client-server model. The client sends a user-selected time-series, of which distortions are removed, to the server and visualizes the matching results. The server efficiently performs the distortion-free time-series matching on the multi-dimensional R*-tree index. By visualizing the matching result as five different charts, we can more easily and more intuitively understand the matching result.

본 논문에서는 왜곡 제거 시계열 매칭의 시각화 도구를 제안한다. 시계열 매칭에서 왜곡 제거 지원은 정확한 유사 시계열 검색을 위한 매우 중요한 요소이다. 본 논문에서는 시계열에서 발생하는 잡음, 위치 이동, 진폭 조정, 선형 추세의 왜곡들을 이동평균 변환, 정규화 변환, 선형 추세 제거 기법을 사용하여 제거한 후, 시계열 매칭을 수행하고 그 결과를 시각화한다. 본 논문에서 제안하는 시각화 도구는 클라이언트-서버 모델을 기반으로 구현된다. 클라이언트는 사용자가 선택한 시계열의 왜곡을 제거하여 서버에 전달하고, 매칭 결과의 시각화를 수행한다. 서버는 R*-트리 기반의 다차원 인덱스로 왜곡 제거 시계열 매칭을 효율적으로 수행한다. 왜곡 제거 시계열 매칭의 결과는 다섯 가지 차트로 달리 표현하여 사용자가 직관적이고 쉽게 이해할 수 있도록 구현한다.

Keywords

References

  1. Visualization [Ineternet], http://en.wikipedia.org/wiki/Visualization/.
  2. E. Keogh and C. A. Ratanamahatana, "Indexing and Mining Large Time Series Databases," in Proc. of 12th Int'l Conf. on Database Systems for Advanced Applications, Tutorial, Bangkok, Thailand, Apr., 2007.
  3. M.-S. Gil, B.-S. Kim, M.-J. Choi, and Y.-S. Moon, "Fast Index Construction in Distortion-Free Time-Series Subsequence Matching," in Proc. of the 2nd Int'l Conf. on Big Data and Smart Computing, Jeju, Korea, pp.130-135, Feb., 2015.
  4. Y.-S. Moon and J. Kim, "Efficient Moving Average Transform-Based Subsequence Matching Algorithms in Time-Series Databases," Information Sciences, Vol.177, No.23, pp.5415-5431, Dec., 2007. https://doi.org/10.1016/j.ins.2007.05.038
  5. W.-K. Loh, S.-W. Kim, and K.-Y. Whang, "Index Interpolation: A Subsequence Matching Algorithm Supporting Moving Average Transform of Arbitrary Order in Time-Series Databases," IEICE Trans. on Information and Systems, Vol.E84-D, No.1, pp.76-86, Jan., 2001.
  6. Y.-S. Moon and J. Kim, "Fast Normalization-Transformed Subsequence Matching in Time-Series Databases," IEICE Trans. on Information and Systems, Vol.E90-D, No.12, pp. 2007-2018, Dec., 2007. https://doi.org/10.1093/ietisy/e90-d.12.2007
  7. W.-K. Loh, S.-W. Kim, and K.-Y. Whang, "A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases," Data Mining and Knowledge Discovery, Vol.9, No.1, pp.5-28, Jul., 2004. https://doi.org/10.1023/B:DAMI.0000026902.89522.a3
  8. M.-S. Gil, Y.-S. Moon, and B.-S. Kim, "Linear Detrending Subsequence Matching in Time-Series Databases," IEICE Trans. on Information and Systems, Vol.E94-D, No.4, pp. 917-920, Apr., 2011. https://doi.org/10.1587/transinf.E94.D.917
  9. R. Agrawal, C. Faloutsos, and A. Swami, "Efficient Similarity Search in Sequence Databases," in Proc. of the 4th Int'l Conf. on Foundations of Data Organization and Algorithms, Chicago, Illinois, pp.69-84, Oct., 1993.
  10. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, "Fast Subsequence Matching in Time-Series Databases," in Proc. of Int'l Conf. on Management of Data, ACM SIGMOD, Minneapolis, Minnesota, pp.419-429, May, 1994.
  11. Y.-S. Moon and B. S. Lee, "Safe MBR-Transformation in Similar Sequence Matching," Information Sciences, Vol.270, pp.28-40, Jun., 2014. https://doi.org/10.1016/j.ins.2014.02.127
  12. Y.-S. Moon, B.-S. Kim, M. S. Kim, and K.-Y. Whang, "Scaling-invariant Boundary Image Matching Using Time-series Matching Techniques," Data & Knowledge Engineering, Vol.69, No.10, pp.1022-1042, Oct., 2010. https://doi.org/10.1016/j.datak.2010.07.001
  13. S. Lee, B.-S. Kim, M.-J. Choi, and Y.-S. Moon, "An approximate Multi-Step k-NN Search in Time-Series Databases," Advances in Computer Science and its Applications, Vol.279, pp.173-178, Dec., 2013.
  14. W.-S. Han, J. Lee, Y.-S. Moon, and H. Jiang, "Ranked Subsequence Matching in Time-Series Databases," in Proc. of the 33rd Int'l Conf. on Very Large Data Bases, Vienna, Austria, pp.423-434, Sep., 2007.
  15. H.-S. Lim, K.-Y. Whang, and Y.-S. Moon, "Similar Sequence Matching Supporting Variable-Length and Variable-Tolerance Continuous Queries on Time-Series Data Stream," Information Sciences, Vol.178, No.6, pp. 1461-1478, Mar., 2008. https://doi.org/10.1016/j.ins.2007.10.026
  16. Y.-S. Moon, K.-Y. Whang, and W.-K. Loh, "Duality-Based Subsequence Matching in Time-Series Databases," in Proc. of 17th Int'l Conf. on Data Engineering, IEEE, Heidelberg, Germany, pp.263-272, Apr. 2001.
  17. S.-J. Lee, J. Lee, H. Cho, and W. S. Han, "A Visualization Tool for Ranked Subsequence Matching in Time-Series Databases," Journal of KIISE, Vol.38, No.2, pp.92-103, Apr., 2011.
  18. T. Rakthanmanon, et al., "Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping," in Proc. of the 18th Int'l Conf. on Kowledge Discovery and Data Mining, Beijing, China, pp.262-270, Aug., 2012.
  19. M. Weber, M. Alexa, and W. Muller, "Visualizing Time-Series on Spirals," in Proc. of Int'l Conf. on IEEE Symp. on Information Visualization, San Diego, California, pp.7-13, Oct., 2001.
  20. B.-S. Kim, Y.-S. Moon, M.-J. Choi, and J. Kim, "Interactive Noise-Controlled Boundary Image Matching Using the Time-Series Moving Average Transform," Multimedia Tools and Applications, Vol.72, Issue.3, pp.2543-2571, Oct., 2014. https://doi.org/10.1007/s11042-013-1552-3
  21. W.-K. Loh, Y.-S. Moon, and J. Srivastava, "Distortion-free Predictive Streaming Time-Series Matching," Information Sciences, Vol.180, No.8, pp.1458-1476, Apr., 2010. https://doi.org/10.1016/j.ins.2009.12.009
  22. X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh, "Experimental Comparison of Representation Methods and Distance Measures for Time-Series Data," Data Mining and Knowledge Discovery, Vol.26, No.2, pp.275-309, Mar., 2013. https://doi.org/10.1007/s10618-012-0250-5
  23. S. Papadimitriou and P. S. Yu, "Optimal Multi-Scale Patterns in Time Series Streams," in Proc. of Int'l Conf. on Management of data, ACM SIGMOD, Chicago, Illinois, pp.647-658, Jun., 2006.
  24. H. Chen and Y. Zhu, "A Robust Video Watermarking Algorithm based on Singular Value Decomposition and Slope-based Embedding Technique," Multimedia Tools and Applications, Vol.71, No.3, pp.991-1012, Aug., 2014. https://doi.org/10.1007/s11042-012-1238-2
  25. MSChart [Ineternet], https://code.msdn.microsoft.com/mschart/.
  26. ECG Data [Ineternet], http://www.physionet.org/.