Building optimal information systems automatically

Abstract

Software frameworks which support integration and scaling of text analysis algorithms make it possible to build complex, high performance information systems for information extraction, information retrieval, and question answering; IBM's Watson is a prominent example. As the complexity and scaling of information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into an information system in order to achieve a desired or optimal level of performance on a given task. This paper presents a formal representation of the space of possible system configurations, given a set of information processing components and their parameters (configuration space) and discusses algorithmic approaches to determine the optimal configuration within a given configuration space (configuration space exploration or CSE). We introduce the CSE framework, an extension to the UIMA framework which provides a general distributed solution for building and exploring configuration spaces for information systems. The CSE framework was used to implement biomedical information systems in case studies involving over a trillion different configuration combinations of components and parameter values operating on question answering tasks from the TREC Genomics. The framework automatically and efficiently evaluated different system configurations, and identified configurations that achieved better results than prior published results.