10 June 2020 Department of Computer Science , Faculty

10 June 2020 – NUS Computing Distinguished Professor Ooi Beng Chin has won the 2020 ACM SIGMOD Edgar F. Codd Innovations Award, for his contributions to the development of database systems and databases. Prof Ooi received the award at the 2020 ACM SIGMOD/PODS International Conference on Management of Data, held online from June 14 to 19.

Among the award’s previous winners include subsequent Turing Award winners Jim Gray and Mike Stonebraker.

Upon receiving the award, Prof Ooi said: “I am honoured and humbled to win this award. As the study of databases is a well-established research area, it’s getting much harder to discover new breakthroughs.”

“Fortunately, we have emerging applications, demands, and new data types and operations that challenge existing systems, which provide opportunities for innovation,” he added.

The database systems Prof Ooi has developed over the years include Apache Software Foundation top level project Apache SINGA, a distributed platform for deep learning, and COOL, an online cohort analytical processing system that supports various types of data analytics, including online analytical processing (OLAP) and cohort analysis.

In particular, he says that COOL can help in the fight against the ongoing COVID-19 pandemic, by helping researchers uncover insights quickly from large amounts of data.

Examples include analysing the effectiveness of various types of medicine on recovery, and analysing the risk factors of different age groups.

Medical researchers can upload datasets into COOL for analysis, by selecting pre-defined analytics from an extensible algorithm pool.

“The system will handle the underlying computation as well as online monitoring, at a speed of one to two orders of magnitude faster than implementations on existing systems,” said Prof Ooi.

“Along with the evolving research on COVID-19, the COOL system can facilitate quantifying and monitoring the long-term effectiveness of various interventions, trials and policies in a timely manner,” he added.

 

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For more information about the COOL system and how it can be used for COVID-19 cohort analysis, visit their main website.

The open-source code for the system is also available for download here.