Computer networks are becoming a crucial part of a business' lifeline,therefore, managing these networks, and ensuring they remain operational, is an increasingly important task. This thesis discusses issues involvedwith performing network management, specifically with means of reducing and storing the large quantity of data that networks management tools and systems generate.

The value of the network management data collected diminishes as the data ages. The value that remains is in the trend that the data outlinesof the macroscopic behavior of the network. This thesis proposes an algorithm which highlight these trends and, in the process, causes a significant data reduction. The proposed algorithm's reductionmethodology is compared with standard data reduction techniques. It isfurther shown that the processed data is suited for storage in a temporaldatabase. The benefits of storing time series data, such as the networkmanagement data discussed (i.e., Link Utilization, CPU Load, and DeviceReachability), in a temporal database is demonstrated by storing the data in both a rational Oracle 7 and a temporal TIGER database, and examining results of queries against both databases.