GeoDeepDive: statistical inference using familiar data-processing languages

Abstract

We describe our proposed demonstration of GeoDeepDive, a system that helps geoscientists discover information and knowledge buried in the text, tables, and figures of geology journal articles. This requires solving a host of classical data management challenges including data acquisition (e.g., from scanned documents), data extraction, and data integration. SIGMOD attendees will see demonstrations of three aspects of our system: (1) an end-to-end system that is of a high enough quality to perform novel geological science, but is written by a small enough team so that each aspect can be manageably explained; (2) a simple feature engineering system that allows a user to write in familiar SQL or Python; and (3) the effect of different sources of feedback on result quality including expert labeling, distant supervision, traditional rules, and crowd-sourced data. Our prototype builds on our work integrating statistical inference and learning tools into traditional database systems. If successful, our demonstration will allow attendees to see that data processing systems that use machine learning contain many familiar data processing problems such as efficient querying, indexing, and supporting tools for database-backed websites, none of which are machine-learning problems, per se.