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Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics
[article]
2018
arXiv
pre-print
We consider methods for learning vector representations of SQL queries to support generalized workload analytics tasks, including workload summarization for index selection and predicting queries that will trigger memory errors. We consider vector representations of both raw SQL text and optimized query plans, and evaluate these methods on synthetic and real SQL workloads. We find that general algorithms based on vector representations can outperform existing approaches that rely on specialized
arXiv:1801.05613v2
fatcat:ozdcnfrgpbci5fgjz42rqbhvt4