Reverse Engineering Top-k Join Queries

Abstract

Ranked lists have become a fundamental tool to represent the most important items taken from a large collection of data. Search engines, sports leagues and e-commerce platforms present their results, most successful teams and most popular items in a concise and structured way by making use of ranked lists. This paper introduces the PALEO-J framework which is able to reconstruct top-k database queries, given only the original query output in the form of a ranked list and the database itself. The query to be reverse engineered may contain a wide range of aggregation functions and an arbitrary amount of equality joins, joining several database relations. The challenge of this work is to reconstruct complex queries as fast as possible while operating on large databases and given only the little amount of information provided by the top-k list of entities serving as input. The core contribution is identifying the join predicates in reverse engineering top-k OLAP queries. Furthermore we introduce several optimizations and an advanced classification system to reduce the execution time of the algorithm. Experiments conducted on a large database show the performance of the presented approach and confirm the benefits of our optimizations.