Computer technology in the 21st century has allowed us to gather and collect data at rates that would have seemed impossible less than a decade ago. As such, typical data base management systems (DBMS) are having great difficulty storing and analyzing data in the traditional way. Systems that receive large amounts of data in transient data streams generally need to analyze the data immediately without storing it on a disk. These systems, referred to as data stream management systems (DSMS), have been pushed to the forefront by technology that demands analysis of data in real time. Density estimation is an essential tool used to make sense of data collected by large scale systems. This talk will focus on an iterative method for constructing and continually updating a probability density function. The approach is shown to work well with simulated data as well as real data collected from URL Internet headers here at George Mason University.