Improving the performance and functionality of database system optimizers requires experimentation on real customer data. Often these data are of sensitive nature and the only way to keep them is by applying a non-reversible transformation to obfuscate them. However, in order that the database optimizer generates exactly the same query plans as for the sensitive data, the transformation has to preserve the order and some important properties of the data distribution. Unfortunately, existing data obfuscation techniques do not preserve all of these properties and therefore are not applicable in this context. In this paper we present a Desensitizer tool that we have developed for optimizer performance experiments of HP's Neoview high availability data warehousing product. The tool is based on novel numeric and string desensitization algorithms which are agnostic to the database system. We explain the core concepts behind the algorithms, how they preserve the required data properties and important implementation considerations that were made. We present the architecture of the Desensitizer tool and results of the extensive validation that we conducted.

This note is devoted to a few remarks on the performance evaluation of systems with service interruptions such as priority queues for lower priority customers, systems subject to breakdowns, etc. Recent work on priority queues has shown that a popular approximation method, the “reduced occupancy approximation”, can be exceedingly inaccurate for a range of parameter values. We identify a cause of inaccuracy and, hence, propose a simple correction that provides a substantial improvement in the results. Using the example of a simple model with service interruptions, we show also that conditional probabilities can be of value in deriving recurrent solutions to some problems.