Humans are increasingly becoming the primary consumer of structured data. As the volume and heterogeneity of data produced in the world increases, the existing paradigm of using an application layer to query and search for information in data is becoming infeasible. The human end-user is overwhelmed with a barrage of diverse query and data models. Due to the lack of familiarity with the data sources, search queries issued by the user are typically found to be imprecise.To solve this problem, this dissertation introduces the notion of a ;;queried unit;;, or qunit, which is the semantic unit of information returned in response to a user;;s search query. In a qunits-based system, the user comes in with an information need, and is guided to the qunit that is an appropriateresponse for that need. The qunits-based paradigm aids the user by systematically shrinking both the query and result spaces. On one end, the query space is reduced by enriching the user;;s imprecise information need. This is done by extracting information from the user during query input by providing schema and data suggestions. On the other end, the result space is reduced by modeling the structured data into a collection of qunits. This is done using qunit derivation methods that use various sources of information such as query logs.This dissertation describes the design and implementation of a autocompletion-style system that performs both query and result space reduction by interacting with the user in real time, providing suggestions and pruning candidate qunit results. It enables the user to search through databases without any knowledge of the data, schema or the query language.
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