INDUSTRY STANDARD

Overview

As a subset of BigDataBench, BigDataBench-DCA is China’s first industry-standard big data benchmark suite, released by Telecom Research Institute of Ministry of Industry and Information Technology together with ICT, CAS, Huawei, China Mobile, Sina, ZTE, Intel (China), Microsoft (China), IBM CDL, Baidu, INSPUR , ZTE, 21viane and UCloud. Currently, the specifications of BigDataBench-DCA have been submitted to and under review of China’s Ministry of Industry and Information Technology.

Benchmarks

There are six data sets and ten workloads in BigDataBench-DCA. Table 1 summarizes the real-world data sets and scalable data generation tools included into BigDataBench-DCA; Table 2 presents the workloads of BigDataBench-DCA.

Table 1: The Summary of Data Sets

data sets Raw data size Scalable data set
1 Wikipedia Entries 4,300,000 English articles（unstructured text） Text Generator of BDGS of BigDataBench
2 Amazon Movie Reviews 7,911,684 reviews (semi-structured text) Text Generator of BDGS of BigDataBench
3 Google Web Graph 875713 nodes, 5105039 edges (unstructured graph) Grap Generator of BDGS of BigDataBench
4 Facebook Social Network 4039 nodes, 88234 edges (unstructured graph) Grap Generator of BDGS of BigDataBench
5 E-commerce Transaction Data Table 1: 4 columns, 38658 rows.Table 2: 6 columns, 242735 rows (structured tables) Table Generator of BDGS of BigDataBench
6 ProfSearch Person Resumes 278956 resumes (semistructured table) Table Generator of BDGS of BigDataBench

Table 2. The summary of the workloads in BigDataBench-DCA

Operations or Algorithm Types Data Source Data Generator Suite
TeraSort IO-Intensive Text From Hadoop
WordCount CPU-Intensive Text From BigDataBench
PageRank Hybrid Graph From BigDataBench
K-means CPU-Intensive Graph From BigDataBench
NaiveBayes CPU-Intensive Text From BigDataBench
Join Hybrid Table From BigDataBench
Aggregation Hybrid Table From BigDataBench
Read/Write/Scan IO-Intensive Table From BigDataBench

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Contributors

  • Kai Wei, CAICT
  • Chunyu Jiang, CAICT
  • Jianfeng Zhan, ICT, CAS
  • Lei Wang, ICT, CAS
  • Jingwei Li
  • Kai Chen, CAICT
  • Shu Wang, Huawei
  • Yang Lu, CMCC
  • Lan Yi, Intel
  • Ning Zou, Intel
  • Guomao Xin, Inspur
  • Jing He, Inspur
  • Yixian Xu, Microsoft China
  • Na Zhang, Microsoft China
  • Liming Zhou, ZTE
  • Dongjie Wei, IBM China
  • Xiaoyi Wang, IBM China
  • Lei Cong, Sina
  • Xiangyu Jiang, Baidu
  • Fei Yang, Baidu
  • He Kang, 21viane
  • Xingjian Zhou, 21viane
  • Dongdong Wang, UCloud

For Citations

If you need a citation for BigDataBench-DCA, please cite the following papers related with your work:

BigDataBench: a Big Data Benchmark Suite from Internet Services. [PDF]

Lei Wang, Jianfeng Zhan, ChunjieLuo, Yuqing Zhu, Qiang Yang, Yongqiang He, WanlingGao, Zhen Jia, Yingjie Shi, Shujie Zhang, Cheng Zhen, Gang Lu, Kent Zhan, Xiaona Li, and BizhuQiu. The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, USA.

License

BigDataBench-DAC is available for researchers interested in big data. Software components of BigDataBench-DAC are all available as open-source software and governed by their own licensing terms. Researchers intending to use BigDataBench-DAC are required to fully understand and abide by the licensing terms of the various components. BigDataBench-DAC is open-source under the Apache License, Version 2.0. Please use all files in compliance with the License. Our BigDataBench-DAC Software components are all available as open-source software and governed by their own licensing terms. If you want to use our BigDataBench-DAC you must understand and comply with their licenses. Software developed externally (not by BigDataBench group)

Software developed internally (by BigDataBench group) BigDataBench-DAC License BigDataBench-DAC Suite Copyright (c) 2013-2015, ICT, Chinese Academy of Sciences All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  • Redistribution of source code must comply with the license and notice disclaimers
  • Redistribution in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimers in the documentation and/or other materials provided by the distribution.

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