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GB/T 41778-2022: Information technology - Industrial big data - Terminology
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| GB/T 41778-2022 | English | 269 |
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Information technology - Industrial big data - Terminology
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GB/T 41778-2022
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Basic data | Standard ID | GB/T 41778-2022 (GB/T41778-2022) | | Description (Translated English) | Information technology - Industrial big data - Terminology | | Sector / Industry | National Standard (Recommended) | | Classification of Chinese Standard | L70 | | Classification of International Standard | 35.020; 01.040.35 | | Word Count Estimation | 14,117 | | Date of Issue | 2022-10-14 | | Date of Implementation | 2023-05-01 | | Issuing agency(ies) | State Administration for Market Regulation, China National Standardization Administration | GB/T 41778-2022: Information technology - Industrial big data - Terminology---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
Information technology -- Industrial big data -- Terminology
ICS 35.020;01.040.35
CCSL70
National Standards of People's Republic of China
Information Technology Industry Big Data Terminology
Published on 2022-10-12
2023-05-01 Implementation
State Administration for Market Regulation
Released by the National Standardization Administration
directory
Preface I
1 Scope 1
2 Normative references 1
3 Terms and Definitions 1
Reference 7
index 8
foreword
This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for Standardization Work Part 1.Structure and Drafting Rules of Standardization Documents"
drafted.
Please note that some content of this document may be patented. The issuing agency of this document assumes no responsibility for identifying patents.
This document is proposed and managed by the National Information Technology Standardization Technical Committee (SAC/TC28).
This document was drafted by. China Electronics Standardization Institute, Tsinghua University, Merrill Lynch Data Technology Co., Ltd., Beijing Digital University
Fang Technology Co., Ltd., Beijing Oriental Jinxin Technology Co., Ltd., Shanghai Intelligent Manufacturing Functional Platform Co., Ltd., Beijing Yixinhua
Chen Software Co., Ltd., Peking University, Yuanshu Technology (Wuxi) Co., Ltd., Wuxi Xuelang Digital Technology Co., Ltd., Inspur Cloud Information Technology
Technology Co., Ltd.
The main drafters of this document. Mei Hong, Sun Wenlong, Du Xiaoyong, Wang Jianmin, Zhang Qun, Wang Chen, Li Xiaoyan, Fan Kefeng, Dong Jian, Li Bing, Yin Zhuo,
Wang Weizhong, Wei Fenglin, Xu Zhe, Qi Jianjun, Zhu Daoyun, Li Ying, Cao Youlin, Liu Guojie, Gao Kun, Wang Weizhe, Li Xiaoxin, Wang Fei, Liu Jun, Chen Li,
Zi Jinjin, Pang Zhen, Wang Feng, Fang Yadong.
Information Technology Industry Big Data Terminology
1 Scope
This document defines common terms and definitions in the field of big data in the information technology industry.
This document is suitable for scientific research, teaching and application in the field of industrial big data.
2 Normative references
There are no normative references in this document.
3 Terms and Definitions
3.1
batch processing
Decompose a large job into multiple tasks to be processed separately by multiple nodes, and then summarize the results of multiple tasks after the decomposition
It is a computing framework for obtaining the final analysis results, which has the capabilities of high availability, high expansion, and high concurrency.
[Source. GB/T 37722-2019, 3.2]
3.2
Bill of Materials bilofmaterial
A hierarchical schedule of all materials that make up a given product.
[Source. GB/T 32236-2015, 2.16]
3.3
cloud storage cloudstorage
According to the specified service level with scalability, virtual storage and data services can be used on-demand and billed through the network.
means of service delivery. This delivery method requires no configuration or is configured in a self-service fashion.
[Source. GB/T 31916.1-2015, 3.1.1]
3.4
data
A formalized representation of information that can be reinterpreted, suitable for communication, interpretation or processing.
NOTE. Data can be processed by manual or automated means.
[Source. GB/T 5271.1-2000, 01.01.02]
3.5
data downsampling
The process of reducing the sampling frequency of industrial IoT time series data.
3.6
data quality dataquantity
The degree to which a characteristic of data meets explicit and implicit requirements when used under specified conditions.
[Source. GB/T 36073-2018, 3.10]
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