LAQP: Learning-based Approximate Query Processing

• URL: http://arxiv.org/abs/2003.02446v1
• Date: Thu, 5 Mar 2020 06:08:25 GMT
• Title: LAQP: Learning-based Approximate Query Processing
• Authors: Meifan Zhang and Hongzhi Wang
• Abstract summary: Approximate query processing (AQP) is a way to meet the requirement of fast response. We propose a learning-based AQP method called the LAQP. It builds an error model learned from the historical queries to predict the sampling-based estimation error of each new query.
• Score: 5.249017312277057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Querying on big data is a challenging task due to the rapid growth of data amount. Approximate query processing (AQP) is a way to meet the requirement of fast response. In this paper, we propose a learning-based AQP method called the LAQP. The LAQP builds an error model learned from the historical queries to predict the sampling-based estimation error of each new query. It makes a combination of the sampling-based AQP, the pre-computed aggregations and the learned error model to provide high-accurate query estimations with a small off-line sample. The experimental results indicate that our LAQP outperforms the sampling-based AQP, the pre-aggregation-based AQP and the most recent learning-based AQP method.

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