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In this paper, we introduce a new problem termed query reverse engineering (QRE). Given a database and a result table —the output of some known or unknown query on —the goal of QRE is to reverse-engineer a query such that the output of query on database (denoted by ) is equal to (i.e., ). The QRE problem has useful applications in database usability, data analysis, and data security. In this work, we propose a data-driven approach, TALOS for Tree-based classifier with At Least One Semantics, that is based on a novel dynamic data classification formulation and extend the approach to efficiently support the three key dimensions of the QRE problem: whether the input query is known/unknown, supporting different query fragments, and supporting multiple database versions.