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, in practice 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 and outerjoins, there are significant differences, which lead to simple new algorithms. In particular, we present algorithms that perform (possibly lossy) 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