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As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the abovementioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly improving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.


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Auxo: A Temporal Graph Management System

Show Author's information Wentao Han( )Kaiwei LiShimin ChenWenguang Chen
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.

Abstract

As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the abovementioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly improving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.

Keywords:

graphs and networks, temporal databases, composite structures
Received: 08 May 2018 Accepted: 27 May 2018 Published: 19 November 2018 Issue date: March 2019
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Publication history

Received: 08 May 2018
Accepted: 27 May 2018
Published: 19 November 2018
Issue date: March 2019

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© The author(s) 2019

Acknowledgements

This work was supported by the National High-Tech Development Plan of China (No. 2015AA015306) and the National Natural Science Foundation of China (No. 61772302).

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