The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
빅데이터가 다양한 분야에서 주목받게 되면서, 대규모의 과학 데이터를 분석하기 위한 데이터 탐색에 대한 연구가 인기를 얻고 있습니다. 과학적 데이터의 탐색적 분석을 지원하려면 대상 데이터의 효과적인 요약 및 시각화는 물론 최신 데이터 관리 시스템과의 원활한 협력이 필요합니다. 본 논문에서는 과학적 배열 데이터의 탐색 기반 분석에 중점을 두고 공간 V-최적 히스토그램 데이터베이스 연구분야의 히스토그램 개념을 바탕으로 정리한다. 우리는 일반적인 기반으로 히스토그램 구성 접근법을 제안합니다. 계층적 분할 뿐만 아니라 좀 더 구체적인 것, l-그리드 파티셔닝, 과학적 데이터 분석에서 효과적이고 효율적인 데이터 시각화를 위한 것입니다. 또한 제안된 알고리즘을 과학적인 데이터 처리 및 관리에 적합한 최첨단 어레이 DBMS에 구현한다. 우리 방법의 효과성과 효율성을 검증하기 위해 쓰나미 재해 시 대규모 대피 시뮬레이션 데이터, 실제 택시 데이터, 합성 데이터를 활용하여 실험을 수행합니다.
Jing ZHAO
Nagoya University
Yoshiharu ISHIKAWA
Nagoya University
Lei CHEN
Hong Kong University of Science and Technology
Chuan XIAO
Nagoya University
Kento SUGIURA
Nagoya University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
Jing ZHAO, Yoshiharu ISHIKAWA, Lei CHEN, Chuan XIAO, Kento SUGIURA, "Building Hierarchical Spatial Histograms for Exploratory Analysis in Array DBMS" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 788-799, April 2019, doi: 10.1587/transinf.2018DAP0020.
Abstract: As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018DAP0020/_p
부
@ARTICLE{e102-d_4_788,
author={Jing ZHAO, Yoshiharu ISHIKAWA, Lei CHEN, Chuan XIAO, Kento SUGIURA, },
journal={IEICE TRANSACTIONS on Information},
title={Building Hierarchical Spatial Histograms for Exploratory Analysis in Array DBMS},
year={2019},
volume={E102-D},
number={4},
pages={788-799},
abstract={As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.},
keywords={},
doi={10.1587/transinf.2018DAP0020},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Building Hierarchical Spatial Histograms for Exploratory Analysis in Array DBMS
T2 - IEICE TRANSACTIONS on Information
SP - 788
EP - 799
AU - Jing ZHAO
AU - Yoshiharu ISHIKAWA
AU - Lei CHEN
AU - Chuan XIAO
AU - Kento SUGIURA
PY - 2019
DO - 10.1587/transinf.2018DAP0020
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - As big data attracts attention in a variety of fields, research on data exploration for analyzing large-scale scientific data has gained popularity. To support exploratory analysis of scientific data, effective summarization and visualization of the target data as well as seamless cooperation with modern data management systems are in demand. In this paper, we focus on the exploration-based analysis of scientific array data, and define a spatial V-Optimal histogram to summarize it based on the notion of histograms in the database research area. We propose histogram construction approaches based on a general hierarchical partitioning as well as a more specific one, the l-grid partitioning, for effective and efficient data visualization in scientific data analysis. In addition, we implement the proposed algorithms on the state-of-the-art array DBMS, which is appropriate to process and manage scientific data. Experiments are conducted using massive evacuation simulation data in tsunami disasters, real taxi data as well as synthetic data, to verify the effectiveness and efficiency of our methods.
ER -