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Transaction optimization in rule databases

Authors: E. BERTINO; CATANIA, BARBARA; GUERRINI, GIOVANNA; D. MONTESI;

Transaction optimization in rule databases

Abstract

The authors propose an approach to transection optimization in rule databases. It is based on a new technique to express updates in rule languages based on a non-immediate update execution. This technique is used to statically characterize some properties of the rules and transactions. Those properties are used at run-time to detect and remove redundant/complementary updates which are useless, hence increasing the efficiency of transaction execution. >

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
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!
5
Average
Average
Average
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