The ACM SIGMOD Best Demo Runner Up Award

A team from the UvA, lead by dr. Schelter, won the Best Demo Runner Up Award for their demonstration on "Proactively Screening Machine Learning Pipelines with ArgusEyes". Machine learning pipelines in real-world applications often suffer from a variety of data-related issues, such as data leakage or label errors, which require reasoning about complex dependencies between their inputs and outputs. These issues are usually only detected in hindsight after deployment, after they caused harm in production. The ArgusEyes system enables data scientists to proactively screen their ML pipelines as part of continuous integration and catch potential problems early before deployment to production.

A preview of the video is available here.

The ACM SIGMOD Systems Award

This award recognizes “an individual or set of individuals who developed a software or hardware system whose technical contributions have had significant impact on the theory or practice of large-scale data management systems.” These systems usually have large-scale real-world applications and have influenced the design of future data processing systems. This year Apache Flink won the award.

Apache Flink is an open-source big data stream analytics platform, based on post-relational user-defined functions, combining database and distributed systems concepts, with the goal of enabling modern data analysis and machine learning for big data. In 2014, the code base was open sourced and donated to the Apache Software Foundation. Today, the project is driven by an international open community with over a 1000 contributors, powers business-critical applications in many companies and enterprises around the globe, and is integrated into cloud services such as Amazon Kinesis Data Analytics. Moreover, Flink is an active platform for research and innovation in many universities and companies worldwide.

Dr. Schelter is part of the large number of Apache Flink contributors who won the award. He worked on Flink as part his PhD at TU Berlin, where he implemented the runtime for distributed iterative computations, designed a novel fault-tolerance mechanism for self-healing fixpoint algorithms, and was instrumental in transitioning the project to the Apache Software Foundation in 2014.

SIGMOD Systems Award