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Author:

Dong, Liang (Dong, Liang.) | Chu, AnKang (Chu, AnKang.) | Liu, Fengkui (Liu, Fengkui.)

Indexed by:

CPCI-S EI Scopus

Abstract:

With the continuous development of modern information technology, the use of scientific and technological literature data is increasing, among which semi-structured data accounts for a large proportion, and it has the characteristics of large quantity and extensive growth. Therefore, distributed database has been widely applied. At the same time, the optimization algorithm for distributed database becomes the research hot spot. However, when using optimization algorithm to solve problems, researchers have not fully solved the limitations of optimization algorithm. In order to improve the retrieval speed of distributed database, this paper combined with the global optimization ability of ant colony algorithm and the local optimization ability of simulated annealing algorithm, the DDQO algorithm is proposed. At the same time, this paper conducted a control experimental on the DDQO algorithm. Experimental results show that the DDQO algorithm is superior to ant colony algorithm and simulated annealing algorithm, it increase the query speed of distributed database by 24%.

Keyword:

ant colony algorithm rapid retrieval simulated annealing algorithm Distributed database

Author Community:

  • [ 1 ] [Dong, Liang]Beijing Univ Technol, Future Network Innovat Ctr, Beijing, Peoples R China
  • [ 2 ] [Chu, AnKang]Beijing Univ Technol, Future Network Innovat Ctr, Beijing, Peoples R China
  • [ 3 ] [Liu, Fengkui]China Elect Power Res Inst, Beijing, Peoples R China

Reprint Author's Address:

  • [Dong, Liang]Beijing Univ Technol, Future Network Innovat Ctr, Beijing, Peoples R China

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Related Keywords:

Source :

ICBDC 2019: PROCEEDINGS OF 2019 4TH INTERNATIONAL CONFERENCE ON BIG DATA AND COMPUTING

Year: 2019

Page: 1-5

Language: English

Cited Count:

WoS CC Cited Count: 7

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 9

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