Answering Imprecise Queries over Autonomous Databases

Current approaches for answering queries with imprecise constraints require users to provide distance metrics and importance measures for attributes of interest metrics that are hard to elicit from lay users. Moreover they assume the ability to modify the architecture of the underlying database. Given that most Web databases are autonomous and may have users with limited expertise over the associated domains, current approaches for answering imprecise queries are not applicable to autonomous databases such as those accessible on the Web. This dissertation presents AIMQ a domain independent framework for supporting imprecise queries over autonomous databases with minimal input from users and no modifications to the underlying database. AIMQ provides answers satisfying an imprecise query by identifying and executing a set of precise queries similar to the imprecise query. AIMQ begins by mapping the given imprecise query to a precise query with non-null resultset. Then using a approximate functional dependency (AFD) based query relaxation heuristic AIMQ identifies a set of precise queries similar to the initially mapped precise query. AIMQ measures the similarity between a tuple and the imprecise query as the weighted summation of similarity over attributes. However, measuring similarity requires distance metrics to be provided by the users or an expert; often quite difficult even for experts. Hence, a context-sensitive domain-independent semantic similarity estimation technique has also been developed as part of AIMQ. Results of empirical evaluation conducted using multiple real-life databases demonstrate both the domain independence and the efficiency of AIMQ’s learning