Approximate Query Processing Based on Approximate Materialized View

In the context of big data, the interactive analysis database system needs to answer aggregate queries within a reasonable response time. The proposed AQP++ framework can integrate data preprocessing and AQP. It connects existing AQP engine with data preprocessing method to complete the connection between them in the process of interaction analysis. After the research on the application of materialized views in AQP++ framework, it is found that the materialized views used in the two parts of the framework both come from the accurate results of precomputation, so there's still a time bottleneck under large scale data. Based on such limitations, we proposed to use approximate materialized views for subsequent results reuse. We take the method of identifying approximate interval as an example, compared the improvement of AQP++ by using approximate materialized view, and trying different sampling methods to find better time and accurate performance results. By constructed larger samples, we compared the differences of time, space and accuracy between approximate and general materialized views in AQP++, and analyzed the reasons for the poor performance in some cases of our methods. Based on the experimental results, it proved that the use of approximate materialized view can improve the AQP++ framework, it effectively save time and storage space in the preprocessing stage, and obtain the accuracy similar to or better than the general AQP results as well.