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  1. Efficient maintenance and recovery of data warehouses

    Labio, Wilburt
    Stanford, Calif. : Stanford University, Dept. of Computer Science, [1999]

    Abstract: "Data warehouses collect data from multiple remote sources and integrate the information as materialized views in a local database. The materialized views are used to answer queries that analyze the collected data for patterns, anomalies, and trends. This type of query processing is often called on-line analytical processing (OLAP). So that OLAP queries can be posed and answered easily, the data from the remote sources is 'cleansed' and translated to a common schema. The warehouse views must be updated when changes are made to the remote information sources. Otherwise, the answers to OLAP queries are based on stale data. Answering OLAP queries based on stale data is clearly a problem especially if (answers to) OLAP queries are used to support critical decisions made by the organization that owns the data warehouse. Because the primary purpose of the data warehouse is to answer OLAP queries, only a limited amount of time and/or resources can be devoted to the warehouse update. Hence, we have developed new techniques to ensure that the warehouse update can be done efficiently. Also, the warehouse update is not devoid of failures. Since only a limited amount of time and/or resources are devoted to the warehouse update, it is most likely infeasible to restart the warehouse update from scratch. Thus, we have developed new techniques for resuming failed warehouse updates. Finally, warehouse updates typically transfer gigabytes of data into the warehouse. Although the price of disk storage is decreasing, there will be a point in the 'lifetime' of a data warehouse when keeping and administering all of the collected [sic] is unreasonable. Thus, we have investigated techniques for reducing the storage cost of a data warehouse by selectively 'expiring' information that is not needed."

  2. Efficient maintenance and recovery of data warehouses

    Labio, Wilburt Juan
    1999.

    Online Search ProQuest Dissertations & Theses. Not all titles available.

  3. Comparing Very Large Database Snapshots

    May 1995.

    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.

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