2014/01/22 by Zijian Ming, Ming, Zijian, Chunjie Luo +11 · 1 citation
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #cs.DB
paper · pdf · doi:10.48550/arxiv.1401.5465
openalex publication_date 2014/01/22 · arxiv created 2014/02/27 · arxiv updated 2014/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data).