Our capability to generate increasingly large and more complex datasets has established the need for scalable methods that can provide insight into important variable trends. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex data sets. This paper presents a new method in which coherent and meaningful visualizations are constructed to convey relational information about the trends that exist \emph{between} variables in a query. Correlation fields are created between pairs of variables and used in conjunction with the cumulative distribution function of each of the query's variables to reveal, both visually and statistically, trends in variable behavior and interactions. We illustrate our concepts by discussing interactions between variables in two flame-front simulations
