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Presentation on “ML-based Cross-Platform Query Optimization” at ICDE 2020

Dr. Zoi Kaoudi, a Sr. Researcher at TU Berlin’s DIMA Group [1] and DFKI’s IAM Group [2] gave a talk on “ML-based Cross-Platform Query Optimization” [3] at the 36th IEEE International Conference on Data Engineering (ICDE 2020) [4]. In her talk, Zoi introduced Robopt, a novel vector-based optimizer for a cross-platform system called Rheem. Robopt uses machine learning (ML) models to predict the cost of plans in addition to scaling up the enumeration process of cross-platform query optimization via vectorization. To ease building ML models, Robopt is accompanied by a scalable training data generator. The evaluation of Robopt shows that: (i) the vector-based approach is more efficient and scalable than simply using a ML model -and- (ii) Robopt matches and, in some cases, improves Rheem’s cost-based optimizer in choosing good plans, without requiring any tuning effort. To listen to her talk, you can find a recording on YouTube [5].


[1] TU Berlin Database Systems and Information Management Group, https://www.dima.tu-berlin.de/.

[2] DFKI Intelligent Analytics for Massive Data Group, https://bit.ly/2LKoY4Y.

[3] “ML-based Cross-Platform Query Optimization,” Zoi Kaoudi, Jorge-Arnulfo Quiané-Ruiz, Bertty Contreras-Rojas, Rodrigo Pardo-Meza, Anis Troudi, and Sanjay Chawla, https://bit.ly/2TjuG1R.

[4] The 36th IEEE International Conference on Data Engineering, https://www.utdallas.edu/icde/.

[5] Live recordings of presentations from the ICDE Research Session No. 27 on ML and Databases 2, https://www.youtube.com/watch?v=KjIXgElcM80&feature=youtu.be.

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