Answering Why-not Questions on Top-k Queries

After decades of effort working on database performance, the quality and the usability of database systems have received more attention in recent years. In particular, the feature of explaining missing tuples in a query result, or the so-called "why-not" questions, has recently become an active topic. In this paper, we study the problem of answering why-not questions on top-k queries. Our motivation is that we know many users love to use top-k queries when they are making multi-criteria decisions. However, they often feel frustrated when they are asked to quantify their feeling as a set of numeric weightings, and feel even more frustrated after they see the query results do not include their expected answers. In this paper, we use the query-refinement method to approach the problem. Given as inputs the original top-k query and a set of missing tuples, our algorithm returns to the user a refined top-k query that includes the missing tuples. A case study and experimental results show that our approach returns high quality explanations to users efficiently.