This paper presents an analytical model to study how working sets scale with database size and other applications parameters in decision-support systems (DSS). The model uses application parameters, that are measured on down-scaled database executions, to predict cache miss ratios for executions of large databases. By applying the model to two database engines and typical DSS queries we find that, even for large databases, the most performance-critical working set is small and is caused by the instructions and private data that are required to access a single tuple. Consequently, its size is not a#ected by the database size. Surprisingly, database data may also exhibit temporal locality but the size of its working set critically depends on the structure of the query, the method of scanning, and the size and the content of the database. 1. INTRODUCTION Decision-support systems (DSS) are one of the key drivers of the high-performance server market  taking the form of uniprocessor ..