Efficient Snapshot Differential Algorithms in Data Warehousing

Abstract

Detecting and extracting modifications from information sources is an integral part of data warehousing. For unsophisticated sources, it is often necessary to infer modifications by periodically comparing snapshots of data from the source. Although this snapshot differential problem is closely related to traditional joins, there are significant differences, whiclead to simple new algorithms. In particular, we present algorithms that perform compression of records. We also present a window algorithm that works very well if the snapshots are not "very different." The algorithms are studied via analysis and an implementation of two of them; the results illustrate the potential gains achievable with the new algorithms