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Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska
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Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska
Article
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Informatyka Automatyka Pomiary w Gospodarce i Ochronie Środowiska
Article . 2020
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OPTIMIZATION IN VERY LARGE DATABASES BY PARTITIONING TABLES

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 30 Sep 2020Publisher:Politechnika LubelskaJournal:Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, volume 10, pages 95-98 (issn: 2083-0157, eissn: 2391-6761, Copyright policy )
Authors: Piotr Bednarczuk;

doi: 10.35784/iapgos.2056

OPTIMIZATION IN VERY LARGE DATABASES BY PARTITIONING TABLES

Abstract

Very large databases like data warehouse slow down over time. This is usually due to a large daily increase in the data in the individual tables, counted in millions of records per day. How do we make sure our queries do not slow down over time? Table partitioning comes in handy, and, when used correctly, can ensure the smooth operation of very large databases with billions of records, even after several years.

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Keywords

Environmental sciences, partitioning, AdventureWorksDW, Environmental engineering, GE1-350, billions of records, TA170-171, data warehouse optimization

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    		 1
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    		 Average
    influence
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
gold