具有理论保证的k-遗憾最小化问题的有效处理
Efficient Processing of k-regret Minimization Queries with Theoretical Guarantees
摘要：
帮助最终用户从大型数据集中识别出期望的结果是多准则决策的一个重要问题。为了解决这个问题，top-k和skyline查询已经被广泛采用，但是它们都具有固有的缺点，即，用户要么必须提供特定的效用函数，要么面临许多结果。结合top-k查询和skyline查询的优点，提出了k-遗憾最小化查询。由于问题的NP-困难性，k-遗憾最小化查询是耗时的，贪婪框架被广泛采用。然而，贪婪的方法返回结果的质量仍然缺乏正式的理论分析。在本文中，我们首先填补这一空白进行非平凡的理论分析的近似比返回的结果。为了加快查询处理速度，一个基于采样的方法，StocPreGreed，开发，以减少评估成本。此外，还对所需样本量进行了理论分析，以限制返回结果的质量。最后，在真实数据集和合成数据集上进行了综合实验，以证明所提出的方法的效率和有效性。
英文摘要：
Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, StocPreGreed,, is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.
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