Techniques for structured data discovery

Abstract

The discovery of structured data, or data that is tagged by key-value pairs, is a problem that can be subdivided into two issues: how best to structure information architecture and user interaction for discovery; and how to intelligently display data in a way that that optimizes the discovery of "useful" (i.e. relevant and helpful for a user's current use case) data. In this thesis, I investigate multiple methods of addressing both issues, and the results of evaluating these methods qualitatively and quantitatively. Specifically, I implement and evaluate: a novel interface design which combines different aspects of existing interfaces, two methods of diversifying data subsets given a search query, three methods of incorporating relevance in data subsets given a search query and information about the user's historic queries, a novel method of visualizing structured data, and two methods of inducing hierarchy on structured data in the presence of an partial data schema. These implementations and evaluations are shown to be effective in structuring information architecture and user interaction for structured data discovery, but are only partially effective in intelligently displaying data to optimize discovery of useful structured data.