TY - GEN
T1 - Visual analytics with unparalleled variety scaling for big earth data
AU - Yu, Lina
AU - Rilee, Michael L.
AU - Pan, Yu
AU - Zhu, Feiyu
AU - Kuo, Kwo Sen
AU - Yu, Hongfeng
N1 - Funding Information:
This research has been sponsored in part by the National Science Foundation through grants ICER-1541043, ICER- 1540542, and IIS-1423487 and with supplemental funding from the Advanced Information Systems Technology (AIST) program of NASA Earth Science Technology Office.
Funding Information:
ACKNOWLEDGMENT This research has been sponsored in part by the National Science Foundation through grants ICER-1541043, ICER-1540542, and IIS-1423487 and with supplemental funding from the Advanced Information Systems Technology (AIST) program of NASA Earth Science Technology Office.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - We have devised and implemented a key technology, SpatioTemporal Adaptive-Resolution Encoding (STARE), in an array database management system, i.e. SciDB, to achieve unparalleled variety scaling for Big Earth Data, enabling rapid-response visual analytics. STARE not only serves as a unifying data representation homogenizing diverse varieties of Earth Science Datasets, but also supports spatiotemporal data placement alignment of these datasets to optimize a major class of Earth Science data analyses, i.e. those requiring spatiotemporal coincidence. Using STARE, we tailor a data partitioning and distribution strategy for the data access patterns of our scientific analysis, leading to optimal use of distributed resources. With STARE, rapid-response visual analytics are made possible through a high-level query interface, allowing geoscientists to perform data exploration visually, intuitively and interactively. We envision a system based on these innovations to relieve geoscientists of most laborious data management chores so that they may focus better on scientific issues and investigations. A significant boost in scientific productivity may thus be expected. We demonstrate these advantages with a prototypical system including comparisons to alternatives.
AB - We have devised and implemented a key technology, SpatioTemporal Adaptive-Resolution Encoding (STARE), in an array database management system, i.e. SciDB, to achieve unparalleled variety scaling for Big Earth Data, enabling rapid-response visual analytics. STARE not only serves as a unifying data representation homogenizing diverse varieties of Earth Science Datasets, but also supports spatiotemporal data placement alignment of these datasets to optimize a major class of Earth Science data analyses, i.e. those requiring spatiotemporal coincidence. Using STARE, we tailor a data partitioning and distribution strategy for the data access patterns of our scientific analysis, leading to optimal use of distributed resources. With STARE, rapid-response visual analytics are made possible through a high-level query interface, allowing geoscientists to perform data exploration visually, intuitively and interactively. We envision a system based on these innovations to relieve geoscientists of most laborious data management chores so that they may focus better on scientific issues and investigations. A significant boost in scientific productivity may thus be expected. We demonstrate these advantages with a prototypical system including comparisons to alternatives.
KW - GIS
KW - STARE
KW - SciDB
KW - array database
KW - data analysis
KW - indexing
KW - load balancing
KW - variety
UR - http://www.scopus.com/inward/record.url?scp=85047731855&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047731855&partnerID=8YFLogxK
U2 - 10.1109/BigData.2017.8257966
DO - 10.1109/BigData.2017.8257966
M3 - Conference contribution
AN - SCOPUS:85047731855
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 514
EP - 521
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
Y2 - 11 December 2017 through 14 December 2017
ER -