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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2019, Vol. 13 Issue (5) : 1072-1101    https://doi.org/10.1007/s11704-018-8022-z
RESEARCH ARTICLE
A parallel data generator for efficiently generating “realistic” social streams
Chengcheng YU1, Fan XIA2, Weining QIAN2(), Aoying ZHOU2
1. College of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
2. School of Data Science and Engineering, East China Normal University , Shanghai 200062, China
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Abstract

A social stream refers to the data stream that records a series of social entities and the dynamic interactions between two entities. It can be employed to model the changes of entity states in numerous applications. The social streams, the combination of graph and streaming data, pose great challenge to efficient analytical query processing, and are key to better understanding users’ behavior. Considering of privacy and other related issues, a social stream generator is of great significance. A framework of synthetic social stream generator (SSG) is proposed in this paper. The generated social streams using SSG can be tuned to capture several kinds of fundamental social stream properties, including patterns about users’ behavior and graph patterns. Extensive empirical studies with several real-life social stream data sets show that SSG can produce data that better fit to real data. It is also confirmed that SSG can generate social stream data continuously with stable throughput and memory consumption. Furthermore, we propose a parallel implementation of SSG with the help of asynchronized parallel processing model and delayed update strategy. Our experiments verify that the throughput of the parallel implementation can increase linearly by increasing nodes.

Keywords social stream      data generator      SSG      parallel generation     
Corresponding Author(s): Weining QIAN   
Just Accepted Date: 01 August 2018   Online First Date: 26 February 2019    Issue Date: 25 June 2019
 Cite this article:   
Chengcheng YU,Fan XIA,Weining QIAN, et al. A parallel data generator for efficiently generating “realistic” social streams[J]. Front. Comput. Sci., 2019, 13(5): 1072-1101.
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https://academic.hep.com.cn/fcs/EN/10.1007/s11704-018-8022-z
https://academic.hep.com.cn/fcs/EN/Y2019/V13/I5/1072
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