As the information available to lay users through autonomous data sources continues to increase, mediators become important to ensure that the wealth of information available is tapped effectively. A key challenge that these information mediators need to handle is the varying levels of incompleteness in the underlying databases in terms of missing attribute values. Existing approaches such as QPIAD aim to mine and use Approximate Functional Dependencies (AFDs) to predict and retrieve relevant incomplete tuples. These approaches make independence assumptions about missing values---which critically hobbles their performance when there are tuples containing missing values for multiple correlated attributes. In this paper, we present a principled probabilistic alternative that views an incomplete tuple as defining a distribution over the complete tuples that it stands for. We learn this distribution in terms of Bayes networks. Our approach involves mining "learning" Bayes networks from a sample of the database, and using it to do both imputation (predict a missing value) and query rewriting (retrieve relevant results with incompleteness on the query-constrained attributes, when the data sources are autonomous). We present empirical studies to demonstrate that (i) at higher levels of incompleteness, when multiple attribute values are missing, Bayes networks do provide a significantly higher classification accuracy and (ii) the relevant possible answers retrieved by the queries reformulated using Bayes networks provide higher precision and recall than AFDs while keeping query processing costs manageable.
@cite_0: We introduce a novel algorithm, TOQR, for relaxing failed queries over databases, that is, over-constrained DNF queries that return an empty result. TOQR uses a small dataset to discover the implicit relationships among the domain attributes, and then it exploits this domain knowledge to relax the failed query. TOQR starts with a relaxed query that does not include any constraint, and it tries to add to it as many as possible of the original constraints or their relaxations. The order in which the constraints are added is derived from the domain's causal structure, which is learned by applying the TAN algorithm to the small training dataset. Our experiments show that TOQR clearly outperforms other approaches: even when trained on a handful of examples, it successfully relaxes more that 97 of the failed queries; furthermore, TOQR'S relaxed queries are highly similar to the original failed query.
@cite_18: Current approaches for answering queries with imprecise constraints require user-specific distance metrics and importance measures for attributes of interest - metrics that are hard to elicit from lay users. We present AIMQ, a domain and user independent approach for answering imprecise queries over autonomous Web databases. We developed methods for query relaxation that use approximate functional dependencies. We also present an approach to automatically estimate the similarity between values of categorical attributes. Experimental results demonstrating the robustness, efficiency and effectiveness of AIMQ are presented. Results of a preliminary user study demonstrating the high precision of the AIMQ system is also provided.
Work on querying inconsistent databases usually focuses on fixing problems with the query itself @cite_0 @cite_18 . If the query has an empty resultset, or if the query does not include all relevant keywords, it can be automatically augmented to fix those shortcomings. The objective of this work is to deal with shortcomings of the data --- our query rewriting algorithms help retrieve useful tuples even in the presence of multiple missing values in them.