  In data exploration, executing complex non-aggregate queries over large
databases can be time-consuming. Our paper introduces a novel approach to
address this challenge, focusing on finding an optimized subset of data,
referred to as the approximation set, for query execution. The goal is to
maximize query result quality while minimizing execution time. We formalize
this problem as Approximate Non-Aggregates Query Processing (ANAQP) and
establish its NP-completeness. To tackle this, we propose an approximate
solution using advanced Reinforcement Learning architecture, termed ASQP-RL.
This approach overcomes challenges related to the large action space and the
need for generalization beyond a known query workload. Experimental results on
two benchmarks demonstrate the superior performance of ASQP-RL, outperforming
baselines by 30% in accuracy and achieving efficiency gains of 10-35X. Our
research sheds light on the potential of reinforcement learning techniques for
advancing data management tasks. Experimental results on two benchmarks show
that ASQP-RL significantly outperforms the baselines both in terms of accuracy
(30% better) and efficiency (10-35X). This research provides valuable insights
into the potential of RL techniques for future advancements in data management
tasks.
