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      KCI등재 SCIE SCOPUS

      Practical Implementation of M4 for Web Visulization Service Practical Implementation of M4 for Web Visualization Service

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      https://www.riss.kr/link?id=A105112948

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      다국어 초록 (Multilingual Abstract)

      Vast volumes of time series are becoming more commonin part to recent research advancements in big data analytics andsensor/monitoring networks. However, tranmission of time seriesfor visualization services can cause serious bandwidth wastage andextensive network delays if there is no efficient data management.
      Existing work such as M4 aim to solve this problem by providinghigh data reduction rates through data aggregation and guaranteeingthe reliability of visualization results at the same time. However,current work on M4 does not consider verification in a morepractical environment; for example experimentation on web-basedservers that are openly accessible by users. In this paper, we proposeinter-pixel gradient-based M4 (IGM4) for enhancing the existingM4 scheme, and conduct a study to reduce the amount of dataand delay without distorting the results of visualized graph. Webuild user-friendly web-based system to dealing with the data processingtechnique, and perform test on the empirical environment.
      Finally, we present the results of performance evaluation throughcomparison among the original data, M4, and IGM4 reflecting thevarious kinds of time series and resolutions.
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      Vast volumes of time series are becoming more commonin part to recent research advancements in big data analytics andsensor/monitoring networks. However, tranmission of time seriesfor visualization services can cause serious bandwidth wastage andexten...

      Vast volumes of time series are becoming more commonin part to recent research advancements in big data analytics andsensor/monitoring networks. However, tranmission of time seriesfor visualization services can cause serious bandwidth wastage andextensive network delays if there is no efficient data management.
      Existing work such as M4 aim to solve this problem by providinghigh data reduction rates through data aggregation and guaranteeingthe reliability of visualization results at the same time. However,current work on M4 does not consider verification in a morepractical environment; for example experimentation on web-basedservers that are openly accessible by users. In this paper, we proposeinter-pixel gradient-based M4 (IGM4) for enhancing the existingM4 scheme, and conduct a study to reduce the amount of dataand delay without distorting the results of visualized graph. Webuild user-friendly web-based system to dealing with the data processingtechnique, and perform test on the empirical environment.
      Finally, we present the results of performance evaluation throughcomparison among the original data, M4, and IGM4 reflecting thevarious kinds of time series and resolutions.

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      참고문헌 (Reference)

      1 E. Hauksson, "Waveform Relocated Earthquake Catalog for Southern California(1981 to June 2011)" 102 (102): 2239-2244, 2012

      2 U. Jugel, "VDDA : automatic visualization-driven data aggregation in relational databases" 25 (25): 53-77, 2016

      3 "Ubuntu"

      4 P. Esling, "Time-series data mining" 45 (45): 12-34, 2012

      5 L. Atzori, "The Internet of things : A survey, computer networks" 54 (54): 2787-2805, 2010

      6 "Southern California Earthquake Data Center"

      7 "Mobius Platform"

      8 U. Jugel, "M4 : A visualizationoriented time series data aggregation" 7 (7): 797-808, 2014

      9 "Korea Meteorological Administration"

      10 "JSON Tutorial"

      1 E. Hauksson, "Waveform Relocated Earthquake Catalog for Southern California(1981 to June 2011)" 102 (102): 2239-2244, 2012

      2 U. Jugel, "VDDA : automatic visualization-driven data aggregation in relational databases" 25 (25): 53-77, 2016

      3 "Ubuntu"

      4 P. Esling, "Time-series data mining" 45 (45): 12-34, 2012

      5 L. Atzori, "The Internet of things : A survey, computer networks" 54 (54): 2787-2805, 2010

      6 "Southern California Earthquake Data Center"

      7 "Mobius Platform"

      8 U. Jugel, "M4 : A visualizationoriented time series data aggregation" 7 (7): 797-808, 2014

      9 "Korea Meteorological Administration"

      10 "JSON Tutorial"

      11 M. Ryu, "Integrated semantics service platform for the Internet of things : A case study of a smart office" 15 (15): 2137-2160, 2015

      12 U. Jugel, "Faster visual analytics through pixel-perfect aggregation" 7 (7): 1705-1708, 2014

      13 "Data-driven documents (D3)"

      14 X. Wu, "Data mining with big data" 26 (26): 97-107, 2014

      15 M. Bostock, "D3 : Data-driven documents" 17 (17): 2301-2309, 2011

      16 "Apache Tomcat"

      17 T. Fu, "A review on time series data mining" 24 (24): 164-181, 2011

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2005-01-01 평가 SCI 등재 (등재후보1차) KCI등재
      2004-01-01 평가 등재후보학술지 유지 (등재후보2차) KCI등재후보
      2003-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2001-07-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.74 0.09 0.53
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.42 0.34 0.264 0.02
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