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Qd-tree: Learning Data Layouts for Big Data Analytics:

, In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data,

Yang, Zongheng ; Chandramouli, Badrish ; Wang, Chi... - p. 193-208 , 2020

Link: https://dl.acm.org/doi/10.1145/3318464.3389770

RT 
T1 <cTitle>Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data</cTitle>: T1 <Title>Qd-tree: Learning Data Layouts for Big Data Analytics</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3389770&Exemplar=1&LAN=DE
				
A1 Yang, Zongheng
A1 Chandramouli, Badrish
A1 Wang, Chi
A1 Gehrke, Johannes
A1 Li, Yinan
A1 Minhas, Umar Farooq
A1 Larson, Per-Åke
A1 Kossmann, Donald
A1 Acharya, Rajeev
PB ACM
YR 2020
K1 OLAP
K1 big data
K1 data analytics
K1 data layout
K1 data partitioning
K1 deep learning
K1 deep reinforcement learning
K1 indexing
K1 query processing
K1 storage
K1 Information systems
K1 Data management systems
K1 Data structures
K1 Data access methods
K1 Data layout
K1 Computing methodologies
K1 Machine learning
K1 Learning paradigms
K1 Reinforcement learning
K1 Information storage systems
K1 Record storage systems
K1 Relational storage
K1 Database management system engines
K1 Database query processing
SP 193
OP 208
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3318464.3389770
DO https://dl.acm.org/doi/10.1145/3318464.3389770
SF ELIB - SuUB Bremen

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