Warehouse views need to be updated when source data changes.

No abstract

Data warehouses collect large quantities of data from distributed sources into a single repository. A t ypical load to create or maintain a warehouse processes GBs of data, takes hours or even days to execute, and involves many complex and user-de ned transformations of the data e.g., nd duplicates, resolve data inconsistencies, and add unique keys. If the load fails, a possible approach is to redo" the entire load. A better approach is to resume the incomplete load from where it was interrupted. Unfortunately, traditional algorithms for resuming the load either impose unacceptable overhead during normal operation, or rely on the speci cs of transformations. We develop a resumption algorithm called DR that imposes no overhead and relies only on the high-level properties of the transformations. We show that DR can lead to a ten-fold reduction in resumption time by performing experiments using commercial software.

Data warehouses collect large quantities of data from distributed sources into a single repository. A t ypical load to create or maintain a warehouse processes GBs of data, takes hours or even days to execute, and involves many complex and user-de ned transformations of the data e.g., nd duplicates, resolve data inconsistencies, and add unique keys. If the load fails, a possible approach is to redo" the entire load. A better approach is to resume the incomplete load from where it was interrupted. Unfortunately, traditional algorithms for resuming the load either impose unacceptable overhead during normal operation, or rely on the speci cs of transformations. We develop a resumption algorithm called DR that imposes no overhead and relies only on the high-level properties of the transformations. We show that DR can lead to a ten-fold reduction in resumption time by performing experiments using commercial software.

No abstract