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Delivery: <= 4 days. True-PDF full-copy in English will be manually translated and delivered via email. GB/T 35295-2017: Information technology -- Big data -- Terminology Status: Valid
Basic dataStandard ID: GB/T 35295-2017 (GB/T35295-2017)Description (Translated English): Information technology -- Big data -- Terminology Sector / Industry: National Standard (Recommended) Classification of Chinese Standard: L70 Classification of International Standard: 35.020; 35.240.01 Word Count Estimation: 18,152 Date of Issue: 2017-12-29 Date of Implementation: 2018-07-01 Regulation (derived from): National Standard Announcement 2017 No. 32 Issuing agency(ies): General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China GB/T 35295-2017: Information technology -- 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 - Big data - Terminology ICS 35.020; 35.240.01 L70 National Standards of People's Republic of China Information Technology Big Data Terminology 2017-12-29 released 2018-07-01 implementation General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China Issued by the National Standardization Administration of China Table of contentsForeword Ⅰ 1 Scope 1 2 Terms and definitions 1 2.1 Big data and its application field terminology 1 2.2 Closely related general terms 8 Reference 10 Index 11 Information Technology Big Data Terminology1 ScopeThis standard defines commonly used terms and definitions in the field of information technology big data. This standard applies to scientific research, teaching and application in the field of big data.2 Terms and definitions2.1 Big data and its application field terminology 2.1.1 Bigdata Contains that have the characteristics of huge volume, diverse sources, extremely fast generation, and variability, and are difficult to effectively process with traditional data architecture Large data sets of data. Note. Internationally, the four characteristics of big data are generally expressed directly in volume, variety, velocity, and variability without modification, and are assigned respectively. Given their definitions in the context of big data. a) Volume. The size of the data set that constitutes the big data. b) Variety. Data may come from multiple data warehouses, data fields, or multiple data types. c) Velocity. data flow per unit time. d) variability. Other characteristics of big data, namely the size, speed, and diversity are all in a state of change. 2.1.2 Datalifecycle A set of processes that transform raw data into knowledge that can be used for action. 2.1.3 Bigdatareferencearchitecture A high-level conceptual model used as a tool to facilitate open discussions on the inherent requirements, design structure, and operation of big data. Note. The generally recognized big data reference architecture generally includes system coordinators, data providers, big data application providers, and big data framework providers. 5 logical functional components such as the user and the data consumer. 2.1.4 System coordinator systemorchestrator A logical functional component in the big data reference architecture that defines the required data application activities and integrates them into runnable In the vertical system. Note 1.The system coordinator can be human, software or both. Note 2.System coordinators generally include. business leaders, consultants, data scientists, information architecture designers, software architecture designers, security personnel Department of structural designer, personal information protection architecture designer and network architecture designer. 2.1.5 Dataprovider A logical functional component in the big data reference architecture, which introduces new data or information into the big data system. Note. Data providers generally include. companies, public institutions, scientists, researchers, engineers engaged in data search, network application software, network operators And end users. 2.1.6 Bigdataapplicationprovider A logical functional component in the big data reference architecture that performs data life cycle operations to meet the requirements defined by the system coordinator Requirements and security and privacy protection requirements. Note. Big data application providers generally include. application field experts, platform field experts and consultants. 2.1.7 Big data framework provider bigdataframeworkprovider A logical functional component in the big data reference architecture, which establishes a computing framework in which conversion applications are executed, and Protect data integrity and privacy at all times. Note. Big data framework providers generally include. embedded data set clusters, data centers and cloud providers. 2.1.8 Dataconsumer A logical functional component in the big data reference architecture, which is the end user of the application provided by the big data application provider Or other systems. Note. Data consumers generally include. end users, researchers, applications and systems. 2.1.9 Infrastructureframework A collection composed of functional components such as network, computing, storage and environment. Note 1.The explanation of network, computing, storage and environment is as follows. a) Network. Resources that support the transmission of data from one resource to another resource (e.g., defined physical resources, software resources, virtual resources) Wait). b) Computing. The physical location of the software that executes and resides other big data system components (e.g., physical resources, operating systems, virtual implementations, logical distribution) Manager and storage. c) Storage. Resources that store data in a big data system (e.g., storage, local disks, redundant array of software/hardware of independent disks, storage area networks, Attached network storage). d) Environment. Physical auxiliary resources (such as power supply, cooling, etc.) that must be considered when building a big data system. Note 2.This is a framework that a big data framework provider may provide. 2.1.10 Dataplatformframework Used to guide the realization of a collection of logical data organization and distribution accessed in conjunction with related application programming interfaces (API). Note 1.This type of framework generally also includes data registration and metadata services together with semantic data description (such as formatting ontology or classification). Logical data organization Coverage ranges from simple limited flat files to fully distributed relational data storage or column data storage. Note 2.This is a framework that a big data framework provider may provide. 2.1.11 Processingframework Covers a collection of computing and processing defined data that supports the infrastructure software required for the realization of big data applications. Note. This is a framework that a big data framework provider may provide. 2.1.12 Messaging/communicationsframework Originated from a high-performance computing environment, it provides a collection of APIs for reliable query, transmission and reception of data between nodes in a horizontally scaled cluster. Note. This is a framework that a big data framework provider may provide. 2.1.13 Resource management framework resourcemanagementframework The big data framework provider may provide, use data localization as an input variable to determine whether to install a new processing framework Elements (such as the main node, processing node, job location), so as to achieve a collection of efficient and effective management of the two resources of CPU and storage. Note. This is a framework that a big data framework provider may provide. 2.1.14 Bigdatasystem A system that implements all or part of the functions of the big data reference architecture. 2.1.15 Bigdataservice Data services based on the big data reference architecture. 2.1.16 Vertical scaling The process of improving system parameters such as processing speed, storage, and memory in order to improve performance. 2.1.17 Horizontal scaling The process of using an integrated group of individual resources as a single system. 2.1.18 Big data paradigm bigdataparadigm A kind of horizontally coupled distributed data system and independent resources, used to realize the necessary processing for the effective processing of many data sets. Knowledge of scalability. 2.1.19 Bigdataengineering In order to meet the needs of big data for effective storage, operation and analysis, use advanced technology to manage independent resources to build scalable data The process of the system. 2.1.20 Massively paralel processing A process in which multiple processors work in parallel to perform a specific computing task. 2.1.21 Distributedfilesystem Multiple structured data sets are distributed in the file system of each computing node of one or more server clusters. Note. In this type of system, data may be distributed at the file and/or data set level, and more generally at the data block level, and support multiple clusters at the same time. Nodes interact with different parts of large files and/or data sets. 2.1.22 Distributedcomputing A computing mode that covers the storage layer and the processing layer and is used to implement multi-type programming algorithm models. Note. Distributed calculation results are usually loaded into the analysis environment. MapReduce is the default processing component in data distributed computing. 2.1.23 Scatter-gather The processing form of a large data set, in which the required calculations are divided and distributed on multiple nodes in the cluster, and the overall result is determined by each node The result is merged. Note. Scatter-gather usually requires changes to the processing software algorithm. Example. MapReduce (a calculation model that includes the two calculation processes of Map and Reduce) uses a scattered-aggregate processing form. 2.1.24 Streamingdata Passing through the interface, data generated from continuously running data sources. 2.1.25 Unstructured data Data that does not have a predefined model or is not organized in a predefined way. 2.1.26 Big data life cycle model lifecyclemodelforbigdata A model used to describe the life cycle of "data-information-knowledge-value" of big data and to guide big data-related activities; these activities It is mainly covered by the stages of collection, preparation, analysis and action. Note. The main activities of several stages are as follows. a) Collection stage. collect raw data and store it in the form of raw data; b) Preparation stage. Transform raw data into clean and organized information; c) Analysis stage. use organized information to generate synthetic knowledge; d) Action stage. Use synthesized knowledge to generate value for the organization. 2.1.27 Schema-on-read A data model application; according to this application, before reading data from the database, it must undergo preparations such as conversion, purification, and integration step. 2.1.28 Computationalportability The ability to move the calculation to the location of the data. 2.1.29 Veracity When data is transmitted across borders, a data feature related to data integrity and privacy protection; it also simply refers to the standard of data. Certainty. 2.1.30 Value The importance of data to the organization from an analytical perspective. Note. The field of big data applications is increasingly looking at the value of major data, and determining the value of data also tends to be an important goal of big data analysis. 2.1.31 Volatility The trend of the data structure over time. Note. This term is different from "variability", which is one of the main characteristics of big data. Variability is mainly used to express the volume, speed and variety of big data. Variety of characteristics and other characteristics. 2.1.32 Validity The appropriateness of the data in terms of its intended use. 2.1.33 Big data dynamic application bigdatavelocityapplication Data collection, preparation and analysis (early warning) occur in dynamic changes, and may be summarized or aggregated before data storage. 2.1.34 Big data volume system bigdatavolumesystem A data system that stores data in its original form before the data preparation stage. Note. In this kind of system, the preparation phase is started when the data is read, so it is called the "read mode". 2.1.35 Datawarehouse A database used to store data permanently after data preparation. 2.1.36 Dynamic data datainmotion In an active state, its typical characteristics are the speed and variability of big data. Note. They are transmitted over the network or temporarily reside in computer memory for reading or updating. They are processed and analyzed in real-time or near real-time. 2.1.37 Static data dataatrest In a static state, its typical characteristics are represented by the volume and diversity of big data. Note. They are usually data stored in physical media. 2.1.38 Non-relationalmodels A logical data model that does not follow relational algebra for data storage and processing. Note. The non-relational model is also often called NoSQL, and is usually understood as non-SQL (Structured Query Language) or not only SQL. 2.1.39 Federateddatabasesystem A meta-database management system that transparently maps multiple autonomous database systems to a single federated database. 2.1.40 Datascience Based on the original data, a science that synthesizes knowledge that can be used for action through experience through the entire data life cycle process. 2.1.41 Data Science Paradigm Actionable knowledge extracted directly from data through the process of discovery, hypothesis and hypothesis testing. 2.1.42 Data scientist Data science professionals. They have sufficient knowledge of business requirements management mechanisms, domain knowledge, analytical skills, and Software and systems engineering knowledge that manages the end-to-end data process at each stage of the data life cycle. 2.1.43 Datagovernance The process of disposing, formatting, and standardizing data. Note 1.Data governance is the basic element of data and data system management. Note 2.Data governance involves the full life cycle management of data, regardless of whether the data is in a static, dynamic, unfinished state or a transaction state. 2.1.44 Opendata Data that can be used for other data. 2.1.45 Linkeddata Connect data to other data. 2.1.46 Dataset The data form of data record aggregation. Note. It can have the volume, speed, diversity and variability characteristics of big data. The characteristics of the data set represent the data itself or static data, and the data When it is transmitted on the network or temporarily resides in the computer memory for reading or updating, it characterizes dynamic data. 2.1.47 Retrospective provenance Discussion of the historical metadata of the dataset. Note 1.The Chinese name of this entry is a representation of the verbal definition of the same English noun. Note 2.This is an essential factor in big data analysis. 2.1.48 Analytics The process of synthesizing knowledge based on information. 2.1.49 Analyticprocessescharacteristics It is used to characterize the discovery, development and application of big data analysis process. "Discovery" is the formation of the initial hypothetical conception, "development" is the construction of the analysis process for the specific conception, and "application" is the analysis of the results of the analysis. Package to a specific operating system. 2.1.50 Shared-diskfilesystems A method of storing data that uses a single storage pool and is associated with multiple computing resources. Note. The technical implementation of this type of system supports simultaneous access to many large data sets from multiple nodes. Example 1. Storage Area Networks (Storage Area Networks, SAN for short). Example 2. Network Attached Storage (NAS for short). 2.1.51 Datacharacteristichierarchy A data hierarchy that characterizes data characteristics from different coarse and fine granularities. Note. The characteristic levels of big data generally include the following levels. ---Data elements; ---Record (collection of data elements); ---Data set (collection of records); ---Multiple data sets (collection of data sets). 2.1.52 Scalablestreamprocessing The processing form of dynamic data between data storage. Note. Mainly used for data filtering, conversion or routing. For big data streams, stream processing is often scalable to support distributed processing and pipeline Line processing. 2.1.53 Scalable datastores A storage technology to support the unlimited growth of data storage. Note. The use of such technologies is often accompanied by fault tolerance in order to deal with certain failures of big data system components. 2.1.54 Ontology In the context of big data, it is a semantic model that constrains various subsequent logical models at different levels. Note. The ontology, in essence, can be very general or extremely specialized. 2.1.55 Taxonomies In the context of data analysis, it represents metadata about the relationship between data elements. Note. It is a hierarchical relationship between entities, in this case, a data element is broken down into smaller components. 2.1.56 Graphical model A type of big data record storage that can present relationships between data elements. Note. In this model, the data elements are nodes, and the relationship is expressed as links between nodes. 2.1.57 Complexity In the context of big data, complexity refers to an interactive relationship between various data elements or across data records. degree. 2.1.58 Resource negotiation A resource access mode that supports multi-tenancy and environments that require high availability and low latency. Note. According to this mode, the resource manager is the hub of several node managers; each customer (or user) sequentially requests the application manager in the node manager, The requester following the previous requester is assigned to the application manager of the same or a different node manager. According to the central processing unit (CPU) and memory availability determine the order of the requested tasks and provide appropriate processing resources at the node. 2.1.59 Cluster management A mechanism that provides communication between cluster resources where data resides in a non-relational model. 2.1.60 Softwaredefinedstorage A storage management technique that uses software to determine the dynamic hierarchical allocation of storage. Note. This type of technology can maintain the necessary data retrieval performance with low storage overhead, and is often used in applications such as memory, high-speed caches, solid state drives, and network drives. Use the field. 2.1.61 Software-defined network softwaredefined network; SDN A technology that supports the efficient and effective management of network resources as the key realization of big data. Note. Also known as virtual network (virtual network), it is different from the traditional dedicated physical network link used for data, management, I/O (input/output) and control. SDN includes resource pooling links and actual switching facilities to implement on-demand allocation of specific functions and specific applications (including the original bandwidth of transmission, service Quality and data routing, etc.). 2.1.62 Network function virtualization Virtual application of network functions such as router/route selection, perimeter protection, remote access authentication, and network traffic/load monitoring achieve. Note. Network function virtualization supports high elasticity, fault tolerance, and resource management of information systems. A crucial application of the valley ups and downs problem. 2.1.63 Nativevirtualization A basic form of virtualization in the big data environment. In this form, a hypervisor is run on the local bare metal. Multiple virtual machines composed of operating systems and applications. 2.1.64 Hostedvirtualization A basic form of virtualization in ......Tips & Frequently Asked Questions:Question 1: How long will the true-PDF of GB/T 35295-2017_English be delivered?Answer: Upon your order, we will start to translate GB/T 35295-2017_English as soon as possible, and keep you informed of the progress. The lead time is typically 2 ~ 4 working days. The lengthier the document the longer the lead time.Question 2: Can I share the purchased PDF of GB/T 35295-2017_English with my colleagues?Answer: Yes. The purchased PDF of GB/T 35295-2017_English will be deemed to be sold to your employer/organization who actually pays for it, including your colleagues and your employer's intranet.Question 3: Does the price include tax/VAT?Answer: Yes. 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