Demonstration of BitGourmet: Data Analysis via Deterministic Approximation
Saehan Jo, Immanuel Trummer · 2020
We demonstrate BitGourmet, a novel data analysis system that supports deterministic approximate query processing (DAQ). The system executes aggregation queries and produces deterministic bounds that are guaranteed to contain the true value. The system allows users to set a precision constraint on query results. Given a user-defined target precision, we operate on a carefully selected data subset to satisfy the precision constraint. More precisely, we divide each column vertically, bit-by-bit. Our specialized query processing engine evaluates queries on subsets of these bit vectors. This involves a scenario-specific query optimizer which relies on quality and cost models to decide the optimal bit selection and execution plan. In our demonstration, we show that DAQ realizes an interesting trade-off between result quality and execution time, making data analysis more interactive. We also offer manual control over the query plan, i.e., the bit selection and the execution plan, so that users can gain more insights into our system and DAQ in general.