On efficiently generating realistic social media timeline structures

Abstract

A framework of synthetic data generator to generate social media timeline structures is proposed in this paper, which is useful for benchmarking query processing over social media data, and validating hypothesis over users' behavior. It is flexible to generate synthetic data with different distributions. With the help of its asynchronized parallel processing model and delayed update strategy, it is efficient to feed out timeline structure with high throughput. We show in experiments that our method can generate realistic social media timeline structures efficiently.