Di erential Algorithms for Data Warehousing

Detecting and extracting modi cations from information sources is an integral part of data warehousing. For unsophisticated sources, in practice it is often necessary to infer modi cations by periodically comparing snapshots of data from the source. Although this snapshot di erential problem is closely related to traditional joins and outerjoins, there are signi cant di erences, 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 di erent." The algorithms are studied via analysis and an implementation of two of them; the results illustrate the potential gains achievable with the new algorithms.