Challenges inbuilding a DBMS Resource Advisor

Abstract

The AVATAR Information Extraction System (IES) at the IBM Almaden Research Center enables highprecision, rule-based, information extraction from text-documents. Drawing from our experience we propose the use of probabilistic database techniques as the formal underpinnings of information extraction systems so as to maintain high precision while increasing recall. This involves building a framework where rule-based annotators can be mapped to queries in a database system. We use examples from AVATAR IES to describe the challenges in achieving this goal. Finally, we show that deriving precision estimates in such a database system presents a significant challenge for probabilistic database systems.