AAAI Preliminary Version: Link

Technical Supplement on ArXiv: Link

The goal of Approximate Query Processing (AQP) is to provide very fast but “accurate enough” results for costly aggregate queries thereby improving user experience in interactive exploration of large datasets. Recently proposed Machine-Learning based AQP techniques can provide very low latency as query execution only involves model inference as compared to traditional query processing on database clusters. However, with increase in the number of filtering predicates (WHERE clauses), the approximation error significantly increases for these methods. Analysts often use queries with a large number of predicates for insights discovery. Thus, maintaining low approximation error is important to prevent analysts from drawing misleading conclusions. In this paper, we propose ELECTRA, a predicate-aware AQP system that can answer analytics-style queries with a large number of predicates with much smaller approximation errors. ELECTRA uses a conditional generative model that learns the conditional distribution of the data and at runtime generates a small (~1000 rows) but representative sample, on which the query is executed to compute the approximate result. Our evaluations with four different baselines on three real-world datasets show that ELECTRA provides lower AQP error for large number of predicates compared to baselines.

Datasets

  1. Flights
    • Dataset obtained from United States Department of Transporation - Bureau of Transportation Statistics’ website - Link.
    • Table used was On_Time_Reporting_Carrier_On_Time_Performance_1987_present for the year of 2019.
  2. Housing
    • Dataset obtained from Kaggle - Link
  3. Beijing PM2.5
    • Dataset obtained from UCI Machine Learning Repository - Link

Relative Error Plots

Figure 5 Figure 5a Flights Dataset Figure 5b Housing Dataset Figure 5c Beijing PM2.5 Dataset

Figure 7 Figure 7a AVG Queries Figure 7b SUM Queries

Contributors