SHF: Medium: Collaborative Research: Computer-Aided Programming for Data Science

University Of Texas At Austin, Austin TX

Investigators

Abstract

The goal of this project, named DataWizard, is to dramatically simplify the effort that is currently required for data analytics through the use of computer-aided programming. Specifically, this project aims to semi-automate data collection, querying, and wrangling tasks by automatically generating programs from informal specifications. As a result, the DataWizard project will allow domain scientists to focus on more interesting data analytics and visualization tasks, leaving the "grunt work" of data science to computer-aided programming tools. The project will also advance the state-of-the-art in automated program synthesis and natural language processing and apply these techniques to the burgeoning field of big data analytics. From a technical perspective, the goals of the DataWizard project are three-fold. First, this project develops novel programming-by-example and information extraction techniques to address challenges that arise in data collection, including consolidation of different data sources, transformations between hierarchical and relational data, and extraction of information from unstructured data sources. Second, this project explores new techniques for querying data using natural language descriptions. In particular, this project considers data extraction from relational and noSQL databases as well as semi-structured data sources, such as XML and JSON. Third, this project develops novel program synthesis methods for automating data wrangling, cleaning, and imputation tasks that commonly arise in data analytics. Overall, these techniques make it significantly easier for data scientists to gain insights from messy data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.