Ranked lists are an essential methodology to succinctly summarize outstanding items, computed over database tables or crowdsourced in dedicated websites. In this work, we address the problem of reverse engineering top-k queries over a database, that is, given a relation R and a sample topk result list, our approach, named PALEO 1 , aims at determining an SQL query that returns the provided input result when executed over R. The core problem consists of nding predicates of the where clause that return the given items, determining the correct ranking criteria, and to evaluate the most promising candidate queries rst. To capture cases where only a sample of R is available or when R is dierent to the relation that indeed generated the input, we put forward a probabilistic model that allows assessing the chance of a query to output tuples that are resembling or are somewhat close to the input data. We further propose an iterative candidate query execution to further eliminate unpromising queries before being executed. We report on the results of a comprehensive performance evaluation using data and queries of the TPC-H and SSB [14] benchmarks.

A novel approach to explore databases using ranked lists is demonstrated. Working with ranked lists, capturing the relative performance of entities, is a very intuitive and widely applicable concept. Users can post lists of entities for which explanatory SQL queries and full result lists are returned. By refining the input, the results, or the queries, user can interactively explore the database content. The demonstrated system is centered around our PALEO framework for reverse engineering OLAP-style database queries and novel work on mining interesting categorical attributes.