LAQP: Learning-based Approximate Query Processing
This work addresses the problem of fast and accurate query processing for big data users, presenting an incremental improvement over prior AQP techniques.
The paper tackles the challenge of querying big data by proposing LAQP, a learning-based approximate query processing method that uses an error model to predict sampling errors, achieving higher accuracy than existing methods with a small off-line sample.
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.