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Research Area I: Cloud-Native Databases
Databases are moving to the cloud driven by desirable properties such as elasticity, high-availability, and cost competitiveness. Modern cloud-native databases adopt a unique storage-disaggregation architecture, where the computation and storage are decoupled. This architecture brings new challenges (e.g., network bandwidth bottleneck) and opportunities in DBMS design.
Research Area II: New Hardware for Databases
GPU database: GPU is a promising solution for data analytics, driven by the rapid growth of GPU computation power, GPU memory capacity and bandwidth, and PCIe bandwidth. We investigate techniques that can fully unleash the power of GPU in online analytical processing (OLAP) databases.
Research Area III: Core DB Techniques
Scalable transaction processing on multicore CPUs: Computer architectures are moving towards manycore machines with dozens or even hundreds of cores on a single chip. We develop new techniques for modern database management systems (DBMSs) to make transaction processing scalable for this level of massive parallelism.
Databases are moving to the cloud driven by desirable properties such as elasticity, high-availability, and cost competitiveness. Modern cloud-native databases adopt a unique storage-disaggregation architecture, where the computation and storage are decoupled. This architecture brings new challenges (e.g., network bandwidth bottleneck) and opportunities in DBMS design.
Research Area II: New Hardware for Databases
GPU database: GPU is a promising solution for data analytics, driven by the rapid growth of GPU computation power, GPU memory capacity and bandwidth, and PCIe bandwidth. We investigate techniques that can fully unleash the power of GPU in online analytical processing (OLAP) databases.
Research Area III: Core DB Techniques
Scalable transaction processing on multicore CPUs: Computer architectures are moving towards manycore machines with dozens or even hundreds of cores on a single chip. We develop new techniques for modern database management systems (DBMSs) to make transaction processing scalable for this level of massive parallelism.
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Proceedings of the ACM on Management of Datano. 1 (2024): 1-26
Bobbi Yogatama, Brandon Miller, Yunsong Wang, Graham Markall, Jacob Hemstad, Gregory Kimball,Xiangyao Yu
19TH INTERNATIONAL WORKSHOP ON DATA MANAGEMENT ON NEW HARDWARE, DAMON 2023pp.19-26, (2023)
PROCEEDINGS OF THE VLDB ENDOWMENTno. 11 (2023): 3085-3097
Proc. ACM Manag. Datano. 1 (2023): 44:1-44:24
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Proc. VLDB Endow.no. 6 (2023): 1235-1248
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Proc. VLDB Endow.no. 11 (2023): 3085-3097
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Very Large Data Bases Conferencepp.3085-3097, (2023)
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OSDIpp.419-439, (2023)
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