A parallel data generator for efficiently generating “realistic” social streams  

作　　者：Chengcheng YU Fan XIA Weining QIAN Aoying ZHOU 

机构地区：[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

出　　处：《Frontiers of Computer Science》2019年第5期1072-1101,共30页中国计算机科学前沿（英文版）

摘　　要：A social stream refers to the data stream that records a series of social entities and the dynamic interac-tions 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 genera-tor is of great significance. A framework of synthetic social stream generator (SSG) is proposed in this paper. The gener-ated social streams using SSG can be tuned to capture sev-eral kinds of fundamental social stream properties, includ-ing patterns about users' behavior and graph patterns. Ex-tensive 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 implemen-tation of SSG with the help of asynchronized parallel pro-cessing model and delayed update strategy. Our experiments verify that the throughput of the parallel implementation can increase linearly by increasing nodes.

关 键 词：SOCIAL STREAM data GENERATOR SSG parallel generation 

分 类 号：TP[自动化与计算机技术]