Data mining algorithms have been the focus of much research recently. In practice, the input data to a data mining process resides in a large data warehouse whose data is kept up-to-date through periodic or occasional addition and deletion of blocks of data. Most data mining algorithms have either assumed that the input data is static, or have been designed for arbitrary insertions and deletions of data records.In this paper, we consider a dynamic environment that evolves through systematic addition or deletion of blocks of data. We introduce a new dimension called the data span dimension, which allows user-defined selections of a temporal subset of the database. Taking this new degree of freedom into account, we describe efficient model maintenance algorithms for frequent itemsets and clusters. We then describe a generic algorithm that takes any traditional incremental model maintenance algorithm and transforms it into an algorithm that allows restrictions on the data span dimension. In a detailed experimental study, we examine the validity and performance of our ideas.