Abstract Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong resemblance to order batching in the warehousing context. While the latter is a well-studied problem, the literature on optimization techniques for query batching problem is quite scarce/nonexistent. In this study, we develop a Mixed Binary Quadratic Program (MBQP) to optimize query-batching in a database system. This model uses the coefficients of a linear regression on the query retrieval time, trained by a large set of randomly generated query sets. We also propose two heuristics, the so-called restricted-cardinality search methods I and II, for solving the proposed MBQP. To demonstrate the effectiveness of our proposed techniques, we conduct a comprehensive computational study over randomly generated instances of three well-known database benchmarks. The computational results show that when the proposed MBQP is solved using the designed heuristics, an improvement of up to 61.8% in retrieving time is achieved.

中文翻译：

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数据库系统中的查询批处理优化

摘要 基于查询之间共享数据和计算的技术一直是数据库系统中一个活跃的研究课题。虽然该领域的工作开发了被证明有效的算法和系统，但缺乏用于查询处理和优化的严格建模和理论研究。数据库系统中的查询批处理与仓储上下文中的订单批处理非常相似。虽然后者是一个经过充分研究的问题，但关于查询批处理问题的优化技术的文献非常稀缺/不存在。在这项研究中，我们开发了一个混合二进制二次程序 (MBQP) 来优化数据库系统中的查询批处理。该模型使用查询检索时间的线性回归系数，由大量随机生成的查询集训练。我们还提出了两种启发式方法，所谓的受限基数搜索方法 I 和 II，用于解决提议的 MBQP。为了证明我们提出的技术的有效性，我们对三个众所周知的数据库基准的随机生成的实例进行了全面的计算研究。计算结果表明，当使用设计的启发式算法求解所提出的 MBQP 时，检索时间提高了 61.8%。