Research on Homomorphic Compression Database Technology
Abstract:
Today's rapidly growing data volumes pose pressing challenges to modern data management and analysis in terms of space and time. This research focuses on homomorphic compression database solution, hoping to solve this series of problems from the space and time dimensions. This report introduces the application of homomorphic compression database technology in different scenarios: (1) Text: data represented in text form. (2) Database: structured data stored in databases. (3) Graph: graph data composed of nodes and edges. (4) Stream: data streams generated in a continuous and real-time manner. (5) Machine learning: data sets used for machine learning.
Bio:
Feng Zhang is a Professor at Renmin University of China. He received his PhD from Tsinghua University in 2017, and has been a visiting scholar at NCSU in 2016 and NUS in 2018. His research interests include databases and high-performance computing. His papers are published in prestigious international conferences and journals including SIGMOD, VLDB, SC, USENIX ATC, ASPLOS, and NeurIPS. He got ACM SIGHPC China Rising Star, TPDS Best Paper, and ASPLOS Distinguished Artifact Award. He serves as TPDS AE and WISE 2023 PC Co-Chair. He has provided consulting services to numerous IT companies in China, including Alibaba, Tencent, and Ant Company.