Date of Award:

8-2019

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Advisor/Chair:

Curtis Dyreson

Co-Advisor/Chair:

Stephen Clyde

Third Advisor:

Haitao Wang

Abstract

This research investigates how to improve legacy queries. Legacy queries are queries that programmers have coded and are used in applications. A database application typically has tens to hundreds of such queries. One way to improve legacy queries is to add new, interesting queries that are similar to or based on the set of queries. We propose Query AutoAwesome, a tool to generate new queries from legacy queries. The Query AutoAwesome philosophy is taken from Google’s AutoAwesomizer tool for photos, which automatically improves a photo uploaded to Google by animating the photo or adding special effects. In a similar vein, Query AutoAwesome automatically enhances a query by ingesting a database and the query. Query AutoAwesome produces a set of enhanced queries that a user can then choose to use or discard. A key problem that we solve is that the space of potential enhancements is large, so we introduce objective functions to narrow the search space to a tractable space. We describe our plans for implementing Query AutoAwesome and discuss our ideas for future work.

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