数据库安全功能测试自动化框架设计与实现

传统的top-k查询为顾客返回符合其偏好的产品集合,reverse top-k查询则返回将给定产品作为top-k结果的偏好集合。reverse top-k查询由于能帮助生产者评估产品对顾客的影响,因此在商业分析中具有重要价值。现有的reverse top-k查询假设数据是精确的,许多现实应用中,数据的不确定性广泛存在。将reverse top-k查询扩展到不确定数据上,并给出了基于物化视图的高效查询算法GMV。实验结果表明,GMV算法能够减少需要计算的偏好数量,具有较高的计算效率。

DBMS Security Independence Test Framework Design and Implementation

Traditional top-k query returns the products to customers according to their preferences,whereas reverse top-k query returns the preferences for which a given product is in the top-k result.Reverse top-k query is valuable in business analysis because it can help the manufacturers evaluate the impact of a product on customers.Existing reverse top-k query assumes the underlying data is certain,however,uncertainty arises in many real applications.In this paper,we extended reverse top-k query to uncertain data,and provided an efficient query algorithm named GMV based on materialized views.Our experimental evaluation demonstrates that,GMV can reduce the preferences which need to be computed,and achieves relatively high computational efficiency.