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We prove that finding the optimal graph partition to minimize query processing cost is a NP-hard problem.
The main purpose of this paper is to show the advantage of using a model proposed by us, which minimizes roundtrip response time versus traditional models that minimize query transmission and processing costs for the design of a distributed database with vertical fragmentation.
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Efficient roll-up and drill-down operations take place in order to maximize the performance by minimizing query flooding.
However, since workloads are mainly produced by group operations, minimizing query costs landing on hyperedges and balancing the workloads should be the objectives in horizontal scaling.
Such behavior significantly minimizing the query delay.
Minimizing the query cost among multi-hosts is important to data processing for big data applications.
In this article, we develop algorithms to select a set of views to materialize in a data warehouse in order to minimize the total query response time under the constraint of total view maintenance time.
Moreover, for query batch processing, one can devise a relocation schedule for a given relation which will further minimize the interference and query processing costs.
In our approach, the static analysis queries the auditor about program properties, and uses abductive inference to minimize the number of queries needed.
A client individually identifies a small group of peers who have the same interests as itself to prefetch their entire file indices of the related interests, minimizing unnecessary outgoing queries and significantly reducing query response time.
In this paper, we consider the problem of asking the optimal set of queries to minimize the resulting output uncertainty.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com