1、 I n t e r n a t i o n a l T e l e c o m m u n i c a t i o n U n i o n ITU-T Series Y TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU Supplement 40 (07/2016) SERIES Y: GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS AND NEXT-GENERATION NETWORKS, INTERNET OF THINGS AND SMART CITIES Big d
2、ata standardization roadmap ITU-T Y-series Recommendations Supplement 40 ITU-T Y-SERIES RECOMMENDATIONS GLOBAL INFORMATION INFRASTRUCTURE, INTERNET PROTOCOL ASPECTS AND NEXT-GENERATION NETWORKS, INTERNET OF THINGS AND SMART CITIES GLOBAL INFORMATION INFRASTRUCTURE General Y.100Y.199 Services, applic
3、ations and middleware Y.200Y.299 Network aspects Y.300Y.399 Interfaces and protocols Y.400Y.499 Numbering, addressing and naming Y.500Y.599 Operation, administration and maintenance Y.600Y.699 Security Y.700Y.799 Performances Y.800Y.899 INTERNET PROTOCOL ASPECTS General Y.1000Y.1099 Services and app
4、lications Y.1100Y.1199 Architecture, access, network capabilities and resource management Y.1200Y.1299 Transport Y.1300Y.1399 Interworking Y.1400Y.1499 Quality of service and network performance Y.1500Y.1599 Signalling Y.1600Y.1699 Operation, administration and maintenance Y.1700Y.1799 Charging Y.18
5、00Y.1899 IPTV over NGN Y.1900Y.1999 NEXT GENERATION NETWORKS Frameworks and functional architecture models Y.2000Y.2099 Quality of Service and performance Y.2100Y.2199 Service aspects: Service capabilities and service architecture Y.2200Y.2249 Service aspects: Interoperability of services and networ
6、ks in NGN Y.2250Y.2299 Enhancements to NGN Y.2300Y.2399 Network management Y.2400Y.2499 Network control architectures and protocols Y.2500Y.2599 Packet-based Networks Y.2600Y.2699 Security Y.2700Y.2799 Generalized mobility Y.2800Y.2899 Carrier grade open environment Y.2900Y.2999 FUTURE NETWORKS Y.30
7、00Y.3499 CLOUD COMPUTING Y.3500Y.3999 INTERNET OF THINGS AND SMART CITIES AND COMMUNITIES General Y.4000Y.4049 Definitions and terminologies Y.4050Y.4099 Requirements and use cases Y.4100Y.4249 Infrastructure, connectivity and networks Y.4250Y.4399 Frameworks, architectures and protocols Y.4400Y.454
8、9 Services, applications, computation and data processing Y.4550Y.4699 Management, control and performance Y.4700Y.4799 Identification and security Y.4800Y.4899 Evaluation and assessment Y.4900Y.4999 For further details, please refer to the list of ITU-T Recommendations. Y series Supplement 40 (07/2
9、016) i Supplement 40 to ITU-T Y-series Recommendations Big data standardization roadmap Summary Supplement 40 to ITU-T Y-series Recommendations provides the standardization roadmap for big data in the telecommunication sector. It describes the landscape and conceptual ecosystem of big data from an I
10、TU-T perspective, related technical areas, activities in standards development organizations (SDOs) and gap analysis. History Edition Recommendation Approval Study Group Unique ID* 1.0 ITU-T Y Suppl. 40 2016-07-08 13 11.1002/1000/13022 Keywords Big data, big data ecosystem, data analytics, roadmap.
11、* To access the Recommendation, type the URL http:/handle.itu.int/ in the address field of your web browser, followed by the Recommendations unique ID. For example, http:/handle.itu.int/11.1002/1000/11830-en. ii Y series Supplement 40 (07/2016) FOREWORD The International Telecommunication Union (ITU
12、) is the United Nations specialized agency in the field of telecommunications, information and communication technologies (ICTs). The ITU Telecommunication Standardization Sector (ITU-T) is a permanent organ of ITU. ITU-T is responsible for studying technical, operating and tariff questions and issu
13、ing Recommendations on them with a view to standardizing telecommunications on a worldwide basis. The World Telecommunication Standardization Assembly (WTSA), which meets every four years, establishes the topics for study by the ITU-T study groups which, in turn, produce Recommendations on these top
14、ics. The approval of ITU-T Recommendations is covered by the procedure laid down in WTSA Resolution 1. In some areas of information technology which fall within ITU-Ts purview, the necessary standards are prepared on a collaborative basis with ISO and IEC. NOTE In this publication, the expression “A
15、dministration“ is used for conciseness to indicate both a telecommunication administration and a recognized operating agency. Compliance with this publication is voluntary. However, the publication may contain certain mandatory provisions (to ensure, e.g., interoperability or applicability) and comp
16、liance with the publication is achieved when all of these mandatory provisions are met. The words “shall“ or some other obligatory language such as “must“ and the negative equivalents are used to express requirements. The use of such words does not suggest that compliance with the publication is req
17、uired of any party. INTELLECTUAL PROPERTY RIGHTSITU draws attention to the possibility that the practice or implementation of this publication may involve the use of a claimed Intellectual Property Right. ITU takes no position concerning the evidence, validity or applicability of claimed Intellectua
18、l Property Rights, whether asserted by ITU members or others outside of the publication development process. As of the date of approval of this publication, ITU had not received notice of intellectual property, protected by patents, which may be required to implement this publication. However, imple
19、menters are cautioned that this may not represent the latest information and are therefore strongly urged to consult the TSB patent database at http:/www.itu.int/ITU-T/ipr/. ITU 2016 All rights reserved. No part of this publication may be reproduced, by any means whatsoever, without the prior writte
20、n permission of ITU. Y series Supplement 40 (07/2016) iii Table of Contents Page 1 Scope . 1 2 References . 1 3 Definitions 1 3.1 Terms defined elsewhere 1 3.2 Terms defined in this Supplement 1 4 Abbreviations and acronyms 1 5 Conventions 2 6 Landscape of big data from an ITU-T perspective . 2 6.1
21、Characteristics and general concepts of big data 2 6.2 Benefits of big data . 4 7 Related technical areas of big data . 4 7.1 Cloud computing 4 7.2 Internet of things . 5 7.3 Security and privacy . 5 7.4 Software-defined networking . 5 7.5 Deep packet inspection . 6 7.6 Big data-driven networking 6
22、7.7 Open data 6 7.8 Standardization areas of big data 6 8 Conceptual model of big data ecosystem . 7 9 Big data SDO activities 8 9.1 ITU-T 8 9.2 ISO/IEC JTC 1 . 9 9.3 W3C 10 9.4 OASIS . 11 9.5 Data Mining Group . 12 9.6 TM Forum 12 10 Gap analysis in big data standardization 12 Appendix I Summaries
23、of referenced standardization work items . 15 I.1 ITU-T references and associated summaries 15 I.2 ISO/IEC JTC 1 References and associated summaries 18 I.3 W3C references and associated summaries 19 I.4 OASIS references and associated summaries . 20 I.5 Data Mining Group references and associated su
24、mmaries . 21 I.6 TM Forum references and associated summaries 21 Bibliography. 22 Y series Supplement 40 (07/2016) 1 Supplement 40 to ITU-T Y-series Recommendations Big data standardization roadmap 1 Scope This Supplement provides the standardization roadmap for big data area in the telecommunicatio
25、n sector. It addresses the following subjects: landscape of big data from an ITU-T perspective; related technical areas of big data; conceptual model of big data ecosystems; big data activities in standards development organizations (SDOs); standardization gap analysis. 2 References ITU-T Y.2060 Rec
26、ommendation ITU-T Y.2060 (2013), Overview of the Internet of things. ITU-T Y.3300 Recommendation ITU-T Y.3300 (2014), Framework of software-defined networking. ITU-T Y.3500 Recommendation ITU-T Y.3500 (2014), Information technology Cloud computing Overview and vocabulary. ITU-T Y.3600 Recommendation
27、 ITU-T Y.3600 (2015), Big data Cloud computing based requirements and capabilities. 3 Definitions 3.1 Terms defined elsewhere This Supplement uses the following term defined elsewhere: 3.1.1 big data ITU-T Y.3600: A paradigm for enabling the collection, storage, management, analysis and visualizatio
28、n, potentially under real-time constraints, of extensive datasets with heterogeneous characteristics. NOTE Examples of datasets characteristics include high-volume, high-velocity, high-variety, etc. 3.2 Terms defined in this Supplement None. 4 Abbreviations and acronyms This Supplement uses the foll
29、owing abbreviations and acronyms: AMQP Advanced Message Queuing Protocol API Application Program Interface BDaaS Big Data as a Service BDC Big Data service Customer bDDN big Data-Driven Networking BDSP Big Data Service Provider 2 Y series Supplement 40 (07/2016) CSV Comma-Separated Values DCAT Data
30、Catalogue Vocabulary DMG Data Ming Group DPI Deep Packet Inspection HTTP Hypertext Transfer Protocol ICT Information and Communications Technology IEC International Engineering Consortium IoT Internet of Things ISO International Organization for Standardization JSON Java Script Object Notation JTC 1
31、 Joint Technical Committee 1 KVDB Key-Value Database Application Interface LDP Linked Data Platform M2M Machine to Machine MQTT Message Queuing Telemetry Transport PMML Predictive Model Markup Language RDF Resource Description Framework SC Subcommittee SDN Software-defined Networking SDO Standards D
32、evelopment Organization SG Study Group TC Technical Committee URL Uniform Resource Locator W3C World Wide Web Consortium WG Working Group XMILE XML Interchange Language XML Extensible Markup Language 5 Conventions None. 6 Landscape of big data from an ITU-T perspective 6.1 Characteristics and genera
33、l concepts of big data ITU-T Y.3600 describes the characteristics and general concepts of the big data ecosystem. With the rapid development of information and communications technology (ICT), Internet technologies and services, huge amount of data are generated, transmitted and stored with explosiv
34、e growth. Data are generated by many sources and not only sensors, cameras, network devices, web pages, email systems, social networks and many other sources. Datasets are becoming so large and Y series Supplement 40 (07/2016) 3 complex or are arriving so fast that traditional data processing method
35、s and tools are inadequate. Efficient analytics of data within tolerable elapsed times becomes very challenging. The paradigm being developed to resolve the above issues are called big data ITU-T Y.3600. Within big data ecosystem, data types include structured, semi-structured and unstructured data.
36、 Structured data are often stored in databases which may be organized in different models, such as relational model, document model, key-value model, graph model etc. Semi-structured data does not conform to the formal structure of data models, but contain tags or markers to identify data. Unstructu
37、red data do not have a pre-defined data model and are not organized in any defined manner. Within all data types data can exist in formats, such as text, spreadsheet, video, audio, image, map, etc. ITU-T Y.3600. Big data is used in many fields, where data processing is characterized by scale (volume
38、), diversity (variety), speed (velocity) and possibly others like credibility (veracity) or business value, if traditional methods and tools are not efficient. These characteristics, usually called vs, can be explained as following ITU-T Y.3600: Volume: refers to the amount of data collected, stored
39、, analyzed and visualized, which big data technologies need to resolve; Variety: refers to different data types and data formats that are processed by big data technologies; Velocity: refers to both how fast the data is collected and how fast the data is processed by big data technologies to deliver
40、 expected results. NOTE Additionally, veracity refers to the uncertainty of data, and value refers to the business results from gaining new information using big data technologies. Other vs can be considered as well. Taking into account the described above vs characteristics, big data technologies a
41、nd services can resolve many new challenges, and can also create more new opportunities than ever before ITU-T Y.3600: Heterogeneity and incompleteness: data processed using big data can miss some attributes or introduce noise into data transmission. Even after data cleaning and error correction, so
42、me incompleteness and some errors in data are likely to remain. These challenges can be managed during data analysis b-CRA. Scale: processing of large and rapidly increasing volumes of data is a challenging task. Using data processing technologies, the data scale challenge is mitigated by evolution
43、of processing and storage resources. However, nowadays data volumes are scaling faster than resources are evolving. Technologies such as parallel databases, in-memory databases, non-SQL databases and analytical algorithms resolve this challenge. Timeliness: the acquisition rate and timeliness, to ef
44、fectively find elements in a limited-time period that meet a specified criterion in a large dataset, are new challenges faced by data processing. Other new challenges are related to the types of criteria specified, and need to devise new index structures and responses to the queries having tight res
45、ponse-time limits. Privacy: data about human individuals, such as: demographic information, Internet activities, commutation patterns, social interactions, energy or water consumption, are being collected and analyzed for different purposes. Big data technologies and services are challenged to prote
46、ct personal identities and sensitive attributes of data throughout the entire data processing process, while respecting applicable data retention policies. Positive resolution of the above challenges opens new opportunities to discover new data relationships, hidden patterns or unknown dependencies
47、ITU-T Y.3600. 4 Y series Supplement 40 (07/2016) 6.2 Benefits of big data Big data technologies can provide many benefits such as data accessibility, productivity of business processes, and cost reduction to private via public sector. Big data technology increases data accessibility by: Unlocking si
48、gnificant value by making information transparent; Creating and storing transactional data in digital form; Reducing time for finding/accessing the correct data. Big data technology improves productivity by: Real-time monitoring and forecasting of events that impact either business performance or op
49、erations; Timely insights from the vast amount of data; Identifying significant information that can improve decision quality or minimize risks; Creating new service models using big data analytics. Big data technology reduces cost by: Scale-out of data storage; Identifying and reducing inefficiencies. 7 Related technical areas of big data 7.1 Cloud computing Cloud computing is a paradigm for enabling network access to a scalable a
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