Publications on Other Topics

Data-Centric Computing with the Netezza Architecture

While relational databases have become critically important in business applications and web services, they have played a relatively

minor role in scientific computing, which has generally been concerned with modeling and simulation activities. However, massively parallel database architectures are beginning offer the ability to quickly search through megabytes of data with hundred-fold or even thousand-fold speedup over server-based architectures. These new machines may enable an entirely new class of algorithms for scientific applications, especially when the fundamental computation involves searching through abstract graphs. Three examples are examined and results are reported for implementations on a novel, massively parallel database computer, which enabled very high performance. Promising results from (1) computation of bibliographic couplings, (2) graph searches for sub-circuit motifs within integrated circuit Metrists, and (3) a new approach to word sense disambiguation in natural language processing, a11 suggest that the computational science community might be able to make good use of these new database machines

Davidson, George S., Kevin W. Boyack, Ron Zacharski, Stephen Helmreich, and Jim R. Cowie. 2006. Data-Centric Computing with the Netezza Architecture. Sandia Report SAND2006-1853. (pdf)

A Discourse System for Conversational Characters

This paper describes a discourse system for conversational characters used for interactive stories. This system is part of an environment that allows learners to practice language skills by interacting with the characters, other learners, and native speakers using instant messaging and email. The dialogues are not purely task oriented and, as a result, are difficult to model using traditional AI planners. On the other hand the dialogues must move the story forward and, thus, systems for the meandering dialogues of chatterbots (for example, AliceBot) are not appropriate. Our approach combines two methods. We use the notion of dialogue game or speech act networks to model the local coherence of dialogues. The story moves forward from one dialogue game to another by means of a situated activity planner.

Ron Zacharski. 2003. Proceedings of the Fourth International Conference on Intelligent Text Processing and Computational Linguistics, ed. by Alexander Gelbukh. Heidelberg: Springer-Verlag. 492-495. (pdf)