QUIC: Handling query imprecision & data incompleteness in autonomous databases

As more and more information from autonomous databases becomes available to lay users, query processing over these databases must adapt to deal with the imprecise nature of user queries as well as incompleteness in the data due to missing attribute values (aka "null values"). In such scenarios, the query processor begins to acquire the role of a recommender system. Specifically, in addition to presenting answers which satisfy the user's query, the query processor is expected to provide highly relevant answers even though they do not exactly satisfy the query predicates. This broadened view of query processing poses several technical challenges. We propose a decision theoretic model for ranking answers in the in the order of their expected relevance to the user. This model combines a relevance function that reflects the relevance a user would associate with answer tuples and a density function which reflects the each tuple's distribution of missing data. Adoption of this model foregrounds three general challenges: (i) how to assess the relevance and density functions automatically (ii) how to support e±cient query processing to re- trieve relevant tuples and (iii) how to make users trust the recom- mended answers. We present a general framework for addressing these challenges, describe a preliminary implementation of the QUIC system and discuss the results of our preliminary empirical evaluation.