The agreement will support advanced research on the optimization of databases, with a particular focus on improving the performance of parameterized queries.
Parameterized queries, widely used in database systems, is a kind of data-intensive computing tasks that typically require substantial computing resources and energy. Consequently, database researchers are continuously exploring optimization strategies to reduce CPU and memory consumption, shorten query times, enhance the overall performance of database systems, and foster more eco-friendly database operations. Although machine learning technologies have been applied to parameterized queries as part of this research direction, the current methods face limitations when dealing with complex queries.
To address this technical challenge, NTU Singapore and OceanBase will initiate collaborative research on "Learning to Rank for Parametric Query Optimization" based on OceanBase 4.0 Paetica. Researchers from both institutions will investigate the application of innovative technologies to resolve performance bottlenecks associated with parameterized queries, including the development of new algorithms that dynamically adapt to ever-changing database environments, leveraging advanced machine learning technologies to generate near-optimal cache plan sets for parameterized queries, and more accurately pinpointing and forecasting the effects of subtle parameter changes on query plan efficacy to identify the optimal option.
"The incorporation of machine learning technologies into database systems offers a significant opportunity to overcome the performance constraints of parameterized queries, enhancing efficiency and reducing energy consumption," stated Prof. Cong Gao, principal investigator of the project and a tenured professor at NTU Singapore's School of Computer Science and Engineering. "Our collaboration with OceanBase is a step towards advancing database and green computing technologies into a new era of innovation."
"We are excited to be joining forces with NTU Singapore to explore cutting-edge innovations in machine learning for databases. This venture goes beyond enhancing database technologies and is crucial for the environmental sustainability of technology systems that underpin our digital economy, "said Yang Chuanhui, Chief Technology Officer of OceanBase. "We are confident that this university-industry collaborated research will hasten advancements in this area and reveal significant opportunities as database systems being more widely deployed."
So far, OceanBase has developed both cloud-based and on-premise versions of its database product, catering to over 1000 customers from a wide range of industries, including financial services, energy, telecommunications, and the internet. It has provided services across 30 availability zones in Asia, Europe, and America on major global cloud platforms such as Amazon Web Services and Alibaba Cloud. OceanBase was also recognized with an Honorable Mention in the 2023 Gartner® Magic Quadrant™ for Cloud Database Management Systems.
About OceanBase
Launched in 2010, OceanBase is a distributed relational database. OceanBase's strengths over alternative solutions include strong data consistency, high availability, high performance, cost effectiveness, elastic scalability, and high compatibility with mainstream relational databases. It enables transactions and analytical queries with just one set of data engines, empowering real-time business intelligence.
To learn more, please visit: https://en.oceanbase.com/
Source: OceanBase
Reporter: PR Wire
Editor: PR Wire
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