Efficient view maintenance at data warehouses

Abstract

We present incremental view maintenance algorithms for a data warehouse derived from multiple distributed autonomous data sources. We begin with a detailed framework for analyzing view maintenance algorithms for multiple data sources with concurrent updates. Earlier approaches for view maintenance in the presence of concurrent updates typically require two types of messages: one to compute the view change due to the initial update and the other to compensate the view change due to interfering concurrent updates. The algorithms developed in this paper instead perform the compensation locally by using the information that is already available at the data warehouse. The first algorithm, termed SWEEP, ensures complete consistency of the view at the data warehouse in the presence of concurrent updates. Previous algorithms for incremental view maintenance either required a quiescent state at the data warehouse or required an exponential number of messages in terms of the data sources. In contrast, this algorithm does not require that the data warehouse be in a quiescent state for incorporating the new views and also the message complexity is linear in the number of data sources. The second algorithm, termed Nested SWEEP, attempts to compute a composite view change for multiple updates that occur concurrently while maintaining strong consistency.