S4

Abstract

An enterprise information worker is often aware of a few example tuples that should be present in the output of the query. Query discovery systems have been developed to discover project-join queries that contain the given example tuples in their output. However, they require the output to exactly contain all the example tuples and do not perform any ranking. To address this limitation, we study the problem of efficiently discovering top-k project join queries which approximately contain the given example tuples in their output. We extend our algorithms to incrementally produce results as soon as the user finishes typing/modifying a cell. Our experiments on real-life and synthetic datasets show that our proposed solution is significantly more efficient compared with applying state-of-the-art algorithms.

Feedback driven improvement of data preparation pipelines

“…For example, in relation to format transformation, Wrangler [19] can suggest potential transformation programs, and FlashFill [16] can synthesize transformation programs from examples. There are also proposals in which mappings are discovered based on data instances (e.g., [2,15,26]). In ETL, there are also a variety of approaches that seek to reduce the amount of manual labour required.…”