Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform.

数据库查询是关系数据库的最重要功能之一。用户有兴趣查看各种数据表示形式，并且这可能会根据数据库用途和所存储数据的性质而有所不同。空军技术学院大约有100条数据记录，这些记录将转换为标准的Scorpion数据模型格式。关系数据库旨在容纳此数据及其关联的传感器和非传感器元数据。确定性多项式时间查询用于测试该模式相对于其他两个模式的性能，该模式具有100个和1000个日志的重复数据和随机元数据的数据库。在这些方法中，性能最佳的方法被选为AFIT的数据库解决方案，现在需要开发更复杂，更有用的查询来进行过滤器研究。为此，请考虑组合多目标背包/布景数据库查询。解决布景覆盖问题或背包问题的算法可以单独使用，以实现有用的结果，但是，它们在一起可以为潜在的用户提供更多的功能。本文探索了多目标KP / SCP的NP-Hard问题域，提出了遗传算法和Hill Climber算法，使用Java实现这些算法，使用来自两个测试数据库的SQL查询填充其数据结构，最后比较了这些算法的执行情况。