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
Memandangkan data besar menarik perhatian dalam pelbagai bidang, penyelidikan tentang penerokaan data untuk menganalisis data saintifik berskala besar telah mendapat populariti. Untuk menyokong analisis penerokaan data saintifik, ringkasan dan visualisasi data sasaran yang berkesan serta kerjasama yang lancar dengan sistem pengurusan data moden diperlukan. Dalam makalah ini, kami memberi tumpuan kepada analisis berasaskan penerokaan data tatasusunan saintifik, dan mentakrifkan a spatial V-Histogram Optimal untuk meringkaskannya berdasarkan tanggapan histogram dalam kawasan penyelidikan pangkalan data. Kami mencadangkan pendekatan pembinaan histogram berdasarkan umum pembahagian hierarki serta yang lebih spesifik, iaitu pembahagian l-grid, untuk visualisasi data yang berkesan dan cekap dalam analisis data saintifik. Selain itu, kami melaksanakan algoritma yang dicadangkan pada DBMS tatasusunan terkini, yang sesuai untuk memproses dan mengurus data saintifik. Eksperimen dijalankan menggunakan data simulasi pemindahan besar-besaran dalam bencana tsunami, data teksi sebenar serta data sintetik, untuk mengesahkan keberkesanan dan kecekapan kaedah kami.
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
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Salinan
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
Salinan
@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},}
Salinan
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 -