Standard

Information graph-based creation of parallel queries for databases. / Shichkina, Yulia; Gushchanskiy, Dmitry; Degtyarev, Alexander.

в: International Journal of Business Intelligence and Data Mining, Том 13, № 4, 2018, стр. 475-491.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Shichkina, Y, Gushchanskiy, D & Degtyarev, A 2018, 'Information graph-based creation of parallel queries for databases', International Journal of Business Intelligence and Data Mining, Том. 13, № 4, стр. 475-491. https://doi.org/10.1504/IJBIDM.2018.094982

APA

Shichkina, Y., Gushchanskiy, D., & Degtyarev, A. (2018). Information graph-based creation of parallel queries for databases. International Journal of Business Intelligence and Data Mining, 13(4), 475-491. https://doi.org/10.1504/IJBIDM.2018.094982

Vancouver

Shichkina Y, Gushchanskiy D, Degtyarev A. Information graph-based creation of parallel queries for databases. International Journal of Business Intelligence and Data Mining. 2018;13(4):475-491. https://doi.org/10.1504/IJBIDM.2018.094982

Author

Shichkina, Yulia ; Gushchanskiy, Dmitry ; Degtyarev, Alexander. / Information graph-based creation of parallel queries for databases. в: International Journal of Business Intelligence and Data Mining. 2018 ; Том 13, № 4. стр. 475-491.

BibTeX

@article{4dcb4cff77be48b6aac1703a6141bd76,
title = "Information graph-based creation of parallel queries for databases",
abstract = "The article describes the query parallelization method that takes into account the dependencies between operations in the data query. The method is based on the representation of the query as a directed graph with vertices as operations and edges as data connections. The graph is processed as an adjacency list, saving more memory than during processing a sparse adjacency matrix. The graph is modified only by operations, which do not change the elements of the adjacency list. Therefore it is possible to achieve intra-query parallelism by consideration of a request structure and implementation of mathematical methods of parallel calculations for its equivalent transformation. This article also presents an example of complex query parallelisation and describes applicability of the graph theory and methods of parallel computing both for query parallelisation and optimisation.",
keywords = "parallel computing, optimization methods, relational database, query, information graph, query parallelisation, Parallel computing, Query, Information graph, Relational database, Query parallelisation, Optimisation methods",
author = "Yulia Shichkina and Dmitry Gushchanskiy and Alexander Degtyarev",
note = "Publisher Copyright: Copyright {\textcopyright} 2018 Inderscience Enterprises Ltd.",
year = "2018",
doi = "10.1504/IJBIDM.2018.094982",
language = "English",
volume = "13",
pages = "475--491",
journal = "International Journal of Business Intelligence and Data Mining",
issn = "1743-8187",
publisher = "Inderscience",
number = "4",

}

RIS

TY - JOUR

T1 - Information graph-based creation of parallel queries for databases

AU - Shichkina, Yulia

AU - Gushchanskiy, Dmitry

AU - Degtyarev, Alexander

N1 - Publisher Copyright: Copyright © 2018 Inderscience Enterprises Ltd.

PY - 2018

Y1 - 2018

N2 - The article describes the query parallelization method that takes into account the dependencies between operations in the data query. The method is based on the representation of the query as a directed graph with vertices as operations and edges as data connections. The graph is processed as an adjacency list, saving more memory than during processing a sparse adjacency matrix. The graph is modified only by operations, which do not change the elements of the adjacency list. Therefore it is possible to achieve intra-query parallelism by consideration of a request structure and implementation of mathematical methods of parallel calculations for its equivalent transformation. This article also presents an example of complex query parallelisation and describes applicability of the graph theory and methods of parallel computing both for query parallelisation and optimisation.

AB - The article describes the query parallelization method that takes into account the dependencies between operations in the data query. The method is based on the representation of the query as a directed graph with vertices as operations and edges as data connections. The graph is processed as an adjacency list, saving more memory than during processing a sparse adjacency matrix. The graph is modified only by operations, which do not change the elements of the adjacency list. Therefore it is possible to achieve intra-query parallelism by consideration of a request structure and implementation of mathematical methods of parallel calculations for its equivalent transformation. This article also presents an example of complex query parallelisation and describes applicability of the graph theory and methods of parallel computing both for query parallelisation and optimisation.

KW - parallel computing

KW - optimization methods

KW - relational database

KW - query

KW - information graph

KW - query parallelisation

KW - Parallel computing

KW - Query

KW - Information graph

KW - Relational database

KW - Query parallelisation

KW - Optimisation methods

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U2 - 10.1504/IJBIDM.2018.094982

DO - 10.1504/IJBIDM.2018.094982

M3 - Article

VL - 13

SP - 475

EP - 491

JO - International Journal of Business Intelligence and Data Mining

JF - International Journal of Business Intelligence and Data Mining

SN - 1743-8187

IS - 4

ER -

ID: 7743850