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Cascade-aware partitioning of large graph databases

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Demirci, Gunduz Vehbi, Ferhatosmanoglu, Hakan and Aykanat, Cevdet (2018) Cascade-aware partitioning of large graph databases. The VLDB Journal, 28 (3). pp. 329-350. doi:10.1007/s00778-018-0531-8 ISSN 1066-8888.

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Official URL: http://dx.doi.org/10.1007/s00778-018-0531-8

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Abstract

Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Social networks -- Databases -- Mathematical models
Journal or Publication Title: The VLDB Journal
Publisher: Springer Berlin Heidelberg
ISSN: 1066-8888
Official Date: June 2018
Dates:
DateEvent
June 2018Published
13 December 2018Available
29 November 2018Accepted
Volume: 28
Number: 3
Page Range: pp. 329-350
DOI: 10.1007/s00778-018-0531-8
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 2 January 2019
Date of first compliant Open Access: 3 January 2019

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