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Efficiently updating materialized views dblp hayden panettiere dating costar

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There are two incremental refresh methods, known as log-based refresh and partition change tracking (PCT) refresh. Users can perform a complete refresh at any time after the materialized view is created. Copyright © 1986 by the Association for Computing Machinery, Inc.Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets.When new records arrive at a high rate, it is infeasible to continuously update (maintain) MVs and a common solution is to defer maintenance by batching updates together.This view is a radical departure from traditional data processing systems, which assume that the data is one-hundred percent correct.

Our new approach can be summarized as follows: the data, the output of various tools, the input from users — including the program the developer writes — are observations from which the system statistically infers the answer.

We propose an algorithm that uses key and referential constraints which reduces the total number of tuples in auxiliary views and uses idea of information sharing between these auxiliary views to further reduce number of auxiliary views.

This chapter discusses how to refresh materialized views, which is a key element in maintaining good performance and consistent data when working with materialized views in a data warehousing environment.

Some sites might prefer not to refresh all of their materialized views at the same time: as soon as some underlying detail data has been updated, all materialized views using this data become stale.

Therefore, if you defer refreshing your materialized views, you can either rely on your chosen rewrite integrity level to determine whether or not a stale materialized view can be used for query rewrite, or you can temporarily disable query rewrite with an .