IEICE TRANSACTIONS on Information
Building Hierarchical Spatial Histograms for Exploratory Analysis in Array DBMS
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.4 pp.788-799
- Publication Date
- 2019/04/01
- Publicized
- 2019/01/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018DAP0020
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
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
Keyword
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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
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@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},}
Copy
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 -
English
