Efficiently updating materialized views dblp
This chapter includes the following sections: The database maintains data in materialized views by refreshing them after changes to the base tables.Performing a refresh operation requires temporary space to rebuild the indexes and can require additional space for performing the refresh operation itself.He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P.Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the Mac Arthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016.A data warehouse is a large data repository for the purpose of analysis and decision making in organizations.To improve the query performance and to get fast access to the data, data is stored as materialized views (MV) in the data warehouse.His contributions span database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016.In addition, work from his group has been incorporated into major scientific and humanitarian efforts, including the Ice Cube neutrino detector, Paleo Deep Dive and MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies.
If insufficient temporary space is available to rebuild the indexes, then you must explicitly drop each index or mark it About Types of Refresh for Materialized Views The refresh method can be incremental or a complete refresh.
Between batches the MVs become increasingly stale with incorrect, missing, and superfluous rows leading to increasingly inaccurate query results.
We propose Stale View Cleaning (SVC) which addresses this problem from a data cleaning perspective.
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We evaluate our method on a generated dataset from the TPC-D benchmark and a real video distribution application.