Learned Cardinality Estimation: An In-depth Study
Kyoungmin Kim, Jisung Jung, In Seo, Wook-Shin Han, Kang-Woo Choi, Jaehyok Chong
Abstract
Learned cardinality estimation (CE) has recently gained significant attention for replacing long-studied traditional CE with machine learning, especially for deep learning. However, these estimators were developed independently and have not been fairly or comprehensively compared in common settings. Most studies use a subset of IMDB data which is too simple to measure their limits and determine whether they are ready for real, complex data. Furthermore, they are regarded as black boxes, without a deep understanding of why large errors occur.
Topics & Concepts
Cardinality (data modeling)EstimatorComputer scienceDeep learningMeasure (data warehouse)Artificial intelligenceSimple (philosophy)Machine learningEstimationData miningStatisticsMathematicsEngineeringSystems engineeringEpistemologyPhilosophyMachine Learning and Data ClassificationNeural Networks and ApplicationsMachine Learning and Algorithms