Shrinking the Warehouse Update Window

Warehouse views need to be updated when source data changes. Due to the constantly increasing size of warehouses and the rapid rates of change, there is increasing pressure to reduce the time taken for updating the warehouse views. In this paper we focus on reducing this "update window" by minimizing the time to compute and install a batch of updates. Various strategies have been proposed in the literature for updating a single warehouse view. These algorithms typically cannot be extended to come up with good strategies for updating an entire set of views. We develop an efficient algorithm that selects an optimal update strategy for any single warehouse view. Based on this algorithm, we develop algorithms for selecting strategies to update a set of views. The performance of these algorithms is studied with experiments involving warehouse views based on TPC-D queries. Keywords: data warehousing, materialized view update. 1 Introduction Data warehouses derive data from remote informati..