VLDB 2022: Conference Awards
Chaired by Xuemin Lin and Fatma Ozcan
Best Regular Research Paper
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks Jingshu Peng (Hong Kong University of Science and Technology)*, Zhao Chen (Hong Kong University of Science and Technology), Yingxia Shao (BUPT), Yanyan Shen (Shanghai Jiao Tong University), Lei Chen (Hong Kong University of Science and Technology), Jiannong Cao (The Hong Kong Polytechnic University)
Best Regular Research Paper Runner Ups
Threshold Queries in Theory and in the Wild Angela Bonifati (Univ. of Lyon), Stefania Dumbrava (ENSIIE), George Fletcher (Eindhoven University of Technology), Jan Hidders (University of London, Birbeck), Matthias Hofer (University of Bayreuth), Wim Martens (University of Bayreuth), Filip Murlak (University of Warsaw), Joshua Shinavier (Uber), Sławek Staworko (University of Lille)*, Dominik Tomaszuk (University of Bialystok)
Sortledton: a universal, transactional graph data structure Per Fuchs (TU Munich)*, Jana Giceva (TU Munich), Domagoj Margan (Imperial College London)
Best Experiment, Analysis and Benchmark Paper
Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs Jeremy Chen (University of Waterloo)*, Yuqing Huang (University of Waterloo), Mushi Wang (University of Waterloo), Semih Salihoglu (University of Waterloo), Kenneth Salem (University of Waterloo)
Best Scalable Data Science Paper
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework Xupeng Miao (Peking University)*, Hailin Zhang (Peking University), Yining Shi (Peking University), Xiaonan Nie (Peking University), Zhi Yang (Peking University), Yangyu Tao (Tencent), Bin Cui (Peking University)
Best Industry Paper
Hardware Acceleration of Compression and Encryption in SAP HANA Monica Chiosa (ETH Zurich)*, Fabio Maschi (ETH Zurich), Ingo Müller (Google), Gustavo Alonso (ETH Zurich), Norman May (SAP SE)
VLDB2022 Best Demonstration
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem Yuki Asada (Microsoft), Victor Fu (Microsoft), Apurva Gandhi (Microsoft), Advitya Gemawat (Microsoft), Lihao Zhang (Microsoft), Dong He (the University of Washington), Vivek Gupta (Microsoft), Ehi Nosakhare (Microsoft), Dalitso Banda (Microsoft), Rathijit Sen (Microsoft), and Matteo Interlandi (Microsoft)*