物联网和智慧城市数据处理与管理概念体系构建

魏玮 安小米

摘 要：概念体系构建和术语工作是制定任何标准的基础，在多学科和多领域的场景中，构建概念体系和术语工作面临不同利益相关方需求不同而难以达成共识的巨大挑战。文章梳理了ITU-T FG-DPM 在促进不同利益相关方和项目组成员之间达成通用概念共识构建统一术语及定義的经验，通过规范概念体系的构建过程，采用术语多维度协同视角来构建统一的数据处理与管理概念体系，促进了工作组和项目组内外在物联网与智慧城市领域数据处理与管理方面达成共识。文章对多学科多领域场景的概念体系构建和术语工作具有一定参考意义，并不局限于物联网和智慧城市领域。

关键词：概念体系构建;术语工作;数据处理与管理;物联网和智慧城市;ITU-T

中图分类号：C37;N04文献标识码：ADOI：10.3969/j.issn.1673-8578.2021.02.010

收稿日期：2021-01-10修回日期：2021-03-07

基金项目：国家自然科学基金项目“面向政府大数据资源治理与共享的数据质量管理标准研究”（92046017）的研究成果之一

1 Introduction

Data processing and management （DPM） increasingly plays an important role for IoT and smart cities and communities （SC&C）. Data processing and management refers to the combination of all activities either directly performed on or indirectly influencing data[1]. With the demands for data，the requirements for data quality， and expectations for capabilities of DPM increasing， DPM is expected to control the complicated data flows from various data sources and then to create the data value for multi-stakeholder.

However， the standardisation of terminology， in the process of terminology work， can encounter a number of challenges when working in a multi-discipline and multi-domain field. For example， a term may have various definitions from specific domain perspective and definitions may not be able to acquire consensus from experts due to different disciplinary standpoints which can then lead to difficulties， confusions and misunderstandings of communication among different stakeholders[2].

Currently， the quality of terminology and standardisation work have numerous problems， especially in lack of integrated concept system to cover multi-discipline so that the terminology which is standardised by related participants have conflicts due to insufficient coordination and collaboration[2].

In order to keep the consistent and common understanding of data processing and management for IoT and SC&C across different working groups and projects， a single and unified concept system building is necessary for shared visions on DPM and their activities. Standardisation of terminology of data processing and management needs to identify the core concepts and their concept relations， which can acquire the consensus from the professional or academic communities[3]， so that a single and unified concept system could be established effectively. Therefore， the terminology work can help find the entry terms from designating the concepts that are defined[4-5].

The establishment of concept system with core concepts by mapping the correspondence of relative terms one by one may not only create a signal to which employs new methods to engage in new or old philosophical problems or subjects such as ontology and methodology， but also may be able to unify the relative terms as a coherent framework and to further analyse and define the existing or new terms from multi-discipline， multi-domain， multi-scenario and multi-dimension[2，6-7]. For instance， ISO 704：2009 provides a framework of thinking for terminology work and its purpose aims to establish a common standard to facilitate the communication among humans[4]. However， this standard is a general specification for the general terminology work. The difficulty of the specific terminology work in international standard organisation is how to achieve the consensus from various information resources crossing different domains and disciplines. Therefore， a method for single and unified concept system building of terminology work in multi-discipline， multi-domain， multi-scenario， multi-dimension， especially in data processing and management for IoT and SC&C is important.

This paper provides a single and unified concept system with concept relations to DPM for IoT and SC&C through five dimensions which are governance， ecosystem， data trust， data lifecycle and data commercialisation. The core concepts and concept system of DPM for IoT and SC&C were established through harmonisation of terminology of different projects and agreements among all the working groups.

The Focus Group on Data Processing and Management （FG-DPM） to support IoT and Smart Cities and Communities was established by ITU-T Study Group 20 at its meeting in Dubai， 13-23 March 2017. There are five working groups under the FG-DPM. Working Group 1 （WG1） is about terminology work， which consists of two major projects：

（1） Technical Specification D0.1： Data processing and Management for IoT and Smart Cities and Communities： Vocabulary （FG-DPM-TS D0.1）. A series of core terms and definitions defined in this technical specification not only are able to reflect the basic concepts of data processing and management for IoT and smart cities and communities， but also those terms and definitions are regarded as the guidance to contribute to the ITU-T FG-DPM deliverables and other relevant standards development organisations （SDOs）[8]. Therefore， the relevant stakeholders ranged from individuals to national public organisations which engage in DPM activities could use this vocabulary.

（2） Technical Report D0.2： Data processing and management for IoT and smart cities and communities： methodology for data processing and management concept building （FG-DPM-TR D0.2）.This document established a systematic methodology for concept building for development of Technical Specification D0.1： Data Processing and Management for IoT and Smart Cities and Communities： Vocabulary. It aims to encourage a mutual and consistent understandings and provides a coherent approach to DPM activities and formal use of terminology[9].

The D0.1 and D0.2 have made contributions for definitions of the same term that have been used coherently and consistently across all the working groups and projects of FG-DPM and they may also have potential values for DPM terminology work in other international standardisation organisations.

2 Methods for building concept system of DPM for IoT and SC&C

There are a few studies on concept system building across disciplines and domains. Current studies on concept system building are mainly focusing on single domain and the relations among those concepts are logically from generic to specific[4，10]. In addition， the cooperation of terminologists and professional experts are vital to the effective terminological standardisation work. Terminologists play an important role for validation of terms and definitions in conformity with the ISO/IEC rules for terminology work， while domain experts play an important role to ensure that terminology work is “in line with subject-field reality”[11]87. According to ISO 10241-1：2011; Song and An（2018） and Zhang and Qin（2016）， the basic processes for building concept system of DPM to IoT and SC&C are separated into 6 steps[3，12-13]（see Figure 1）： （1） The first step was to define the sources， contents and term selection criteria of related disciplines; （2） Conducting the standardised assessment for authoritative terminology databases① based on the current demands was the second step; （3） In the third step， core concepts were identified by theme deconstruction of related domains and disciplines based on the terminological principles and methods; （4） The identified core concepts， in the fourth step， were categorised by the proposed classification criteria; （5） The relationships among the core concepts were mapped based on specific knowledge or the theme of domain; （6） Based on the previous steps， the single and unified concept system was carried out in the final step by analysing the elements of knowledge relaed to the core concepts. Additionally， this process was tracked， checked and updated iteratively.

（1） Principles for including terms： ①Relevancy： Highly relevant and pertinent to DPM for supporting IoT and SC&C. ②Accuracy： Definitions must be accurate， clear and positive. Inaccurate and negative definitions are not acceptable. Nor should definitions be circular or include， or paraphrase， the term being defined. The language used in a definition must be common Eng-Figure 1. Processes of DPM concept system building for IoT and SC&C

lish language terms. ③ Readability： The associated definitions must be able to stand alone. In other words， the meaning should be understandable without reference. This is particularly important since the terms and definitions are being extracted for use by delegates and consumers on the web. ④ Usefulness： In frequent use and applicable throughout all FG-DPM deliverables. ⑤ Consistency： Those identified terms are consisted with the definitions of related concepts. And both existing and new terms should have correspondence and integration into the concept system[7]. ⑥ Reliability： Those definitions of the target terms have authoritative sources or are recommended and agreed upon by experts of FG-DPM. ⑦ Appropriateness： terms should avoid confusion when the terms and definitions are created or modified; the terms also should be neutral without connotation[7].

（2） Principles for excluding terms[9]： ①Terms are not pertinent and relevant to DPM for IoT and SC&C; ② Generally， terms are not frequently used in DPM terminology standards; ③Terms have multiple interpretations with no consensus， and may cause confusion or conflicts， contradictions or inconsistency in the future work; ④The criteria of term selection are not objective and comprehensive or despite the fact that the term or definition can cause conflict， the term and definition could be considered as a component of the concept system because of its importance for DPM;⑤Terms that are no longer used， obsoleted should be replaced or be modified.

3 Development of the terminology work of DPM for IoT and SC&C

3.1 Overview of the whole process of DPM terminology work

The development of terminology work of DPM referred with D0.1 and D0.2 experiences five main stages through eight physical meetings and 69 online meetings which produce 17 deliverables in approximately two years[1]（see Table 1）.

3.2 Main stages of the DPM terminology work

（1） Initial/defining stage. At the very beginning for the initial stage， the existing definitions of data processing （DP） and data management （DM） were collected from ISO， IEC and ITU-T standard database were deconstructed and the terms related to the concept of DPM were identified. In addition， the DPM， i.e.the DP and DM concepts were both matched with the characteristics of curation lifecycle[14].Therefore， the terminology establishment of DPM was built up based on DP and DM from the data curation lifecycle perspective. By this process， 26 terms and concepts were identified and discussed in the second ITU-T meeting②. Furthermore， in the meeting， the document about the terminology work for the whole FG-DPM was established officially.

（2） Extension and selection stage. At this stage， the delivery of the work was separated into two parts including the input document and the output document. The input document resulted from a number of discussions by diverse experts from other working groups of FG-DPM in many online meetings. The input document was also the basis for acquiring common consensus in physical meeting held by FG-DPM. While， the output document was the result of the physical meeting of FG-DPM. This document was the result of common consensus at the physical meeting， but it was still the processed document for improvement over time until the completion of the work of the FG-DPM. At this stage， 26 core terms were extended to 134 terms with added terms from the use case template③， which were identified and discussed via online meetings[15]. Those terms reflected the main viewpoints and relevance of DPM interests and objectives at the time.

In order to respect the diversity and different understanding on the same vocabulary and to build the links of common understandings for multi-stakeholder and users， both generic and specific definitions of each term were collected through ISO， IEC and ITU-T terminology databases. To do so， common understandings and different understandings about the same term and their appropriate use under different contexts were identified. As a result， in the third meeting of FG-DPM④， the number of terms was changed from 134 to 136， which were checked and updated by relevant principles and discussed with experts in other WGs of FG-DPM.

（3） Justification stage. At this stage， the number of terms was changed from 136 to 130. Terms were checked further against the use case template for assurance of their real use from the fourth meeting of FG-DPM⑤.

（4） Continuous justification and application stage. At this stage， 130 terms were extended to 176 terms in total， after mapping with 27 output documents from the fifth meeting of FG-DPM⑥ and then mapped with 12 use cases in the sixth meeting of FG-DPM⑦.

（5） Harmonisation and cooperation stage. At this stage， the vocabulary and the methodology of the terminology were divided into two independent parts. The vocabulary was used to reflect the concepts related to the whole work of FG-DPM. The control and updating of the terms were based on the methodology of the terminology work for DPM. Besides， after several e-meetings， a total of 194 terms were extended from the 176 terms to reflect all the terms used in all the products of FG-DPM.

According to the framework of FG-DPM which contains 5 dimensional views on DPM[16]， the high-level considerations of multiple stakeholders common concerns about DPM were recommended for DPM concept system building and concept mapping in alignment with the interests and objectives of FG-DPM. Those 5 dimensions were agreed upon at the seventh meeting of FG-DPM⑧ which include governance， ecosystem， data trust， data lifecycle and data commercialisation.

Therefore， the 194 terms were used to map with the input documents of all working groups of FG-DPM. In addition， the criteria for selecting terms to be included in a common vocabulary was updated. Eventually， 30 terms were selected as core terms and core concepts of DPM for IoT and SC&C with agreement from all the working groups and projects.

The criteria for selecting terms was based on checking the terminological usage of terms in the input and output documents of other working groups in the FG-DPM and at the same time the importance of the term for working groups was identified . The whole terminological work was then discussed and finally approved at the final physical meeting of FG-DPM⑨. Therefore， the number of terms was changed from 30 to 38 and they were identified with agreement from experts of FG-DPM[17]. All the definitions of the same term in other deliverables were checked against the proposed 38 terms and definitions， keeping the consistent and coherent set of standards that were developed and assured by ITU-T FG-DPM.

4 Core concepts and relations building of DPM for IoT and SC&C

4.1 Core concepts of DPM for IoT and SC&C

Based on the 38 terms and the 5 dimensions of the DPM framework[16]， the core concepts of DPM were categorized into the following 5 dimensions （see Table 2）：

（1） Core concepts related to governance include： blockchain， data governance， data processing and management （DPM）， Internet of things， scenario， Smart Cities and Communities （SC&C）;

（2） Core concepts related to ecosystem include： application， capability， community， ecosystem， requirement， stakeholder， use case， use case template;

（3） Core concepts related to data trust include： risk， safety， security， trust;

（4） Core concepts related to data lifecycle include： closed data， data， data consistency， data management， data processing， data exchange， data sharing， interoperability， lifecycle， minimal interoperability， open data， personal data， processed data， raw data， shared data， thing;

（5） Core concepts related to data commercialization includes： content owner， data commercialization， data marketplace.

4.2 Relation building of core concepts of DPM for IoT and SC&C

In ISO 1087：2019， the “concept” refers to “unit of knowledge created by a unique combination of characteristics”; the “concept system” refers to “set of concepts structured according to the relations among them”; the “term” refers to “verbal designation of general concept in a specific subject field”[18]. Concepts are not independent of one another. Analysis of relations among concepts within the field of DPM and their arrangement into a concept system is a prerequisite of a coherent vocabulary in a subject area to keep the value of core concepts[11]89. A concept in a specific domain is represented by a term. Concepts are not necessarily bound to particular languages. They are， however， influenced by the social or cultural background which often leads to different categorisations.

Meanwhile， there are two types of concept relations： hierarchical relations such as generic and partitive relations; non-hierarchical relations such as associative relations[4，18]：

（1） Hierarchical relations. First， generic relations are genus-species relations which refer to two exists between two concepts when the intension of the subordinate concept includes the intension of the superordinate concept plus at least one additional delimiting characteristic[4，18]. The generic relations are represented by a fan or tree diagram without arrows， which shows superordinate concepts within the hierarchy inherit all the characteristics of the superordinate concept and contain description of these characteristics distinguishing them from the subordinate and coordinate concepts， e.g. the relation of spring， summer， autumn and winter to season[4]. Second， partitive relations are part-whole relations which refer to the relation between superordinate concepts and subordinate concepts or coordinate concepts where one of the concepts constitutes the whole and the other concepts represent parts of that whole[4，18]. Partitive relations are represented by a rake without arrows， which shows subordinate concepts within hierarchy forming constituent parts of the superordinate concept[4].

（2） Non-hierarchical relations. Associative relations are non-hierarchical between subjects which refer to a thematic connection by virtue of experience[4，18]. Associate relations are presented by a line with double sided arrow， which are used to represent the nature of relation between one concept and another within a concept system， e.g. quantity and unit， matter and property， product and composition[4].

In addition， the multi-dimensional framework of DPM provides high-level considerations and capabilities for concept relations and concept system building with meta-synthetic viewpoints of DPM for IoT and SC&C （see Table 3）.

T

Therefore， the relations among the core concepts of DPM for IoT and SC&C were constructed as follow：

（1） The core concept related to governance dimension （see Figure 2）. Governance dimension covers all the viewpoints including policies and strategies at top-level and applies those policies and strategies to the rest of dimensions of DPM. Hence， concepts relating to governance dimension includes data processing and management and data governance. Moreover， the main concepts are specific to these FG-DPM objectives and targeted scenario， including blockchain， smart cities and communities and Internet of things.

（2） The core concept related to ecosystem dimension （see Figure 3）. The activities of DPM can be influenced by the mechanisms and factors directly or indirectly in this dimension. At the macro-level， the ecosystem mainly concerns the demands

of people and will also provide the series of applications to associate the basic factors such as use case， service and community for multi-stakeholder. Additionally， the use cases in DPM for IoT and SC&C which are dealing with a number of requirements and capabilities can form the use case template for application and implementation of DPM for IoT and SC&C.

（3） The core concept relating to data trust dimension （see Figure 4）. Data trust dimension is people-oriented at the micro-level， trusted data to enable trusted DPM for IoT and SC&C and trust of community. However， to ensure data trust， one needs to ensure risk control of security and safety.

（4） The core concept related to data lifecycle dimension （see Figure 5）. The data lifecycle has the main activities such

as data management and data processing from creation to use and disposal. The data is the important entity which consists of data types and data lifecycle activities. Data types of DPM include closed data， open data， raw data， processed data， personal data and shared data. Meanwhile， the data lifecycle activities of DPM refer to data consistency， data sharing and data exchange. More importantly， the interoperability， especially， minimal interoperability， in DPM lifecycle， is fundamental to effective data sharing， data exchange and data use and reuse.

（5） The core concept related to data commercialisation （see Figure 6）. The data commercialisation dimension indicates that the DPM is productivity-oriented process. Core concepts of data commercialisation activities are content owner and data marketplace， which were derived from the output document of Working Group 5 in relation to data commercialization.

5 Concept system building of DPM for IoT and SC&C

A single and unified concept system， based on the core concept relations and 5 high level dimensional concerns， are represented in Figure 7 and Figure 8， which reflect a multi-dimensional viewpoint of DPM and the multi-layer concerns about capabilities， requirements and key components of DPM and their relationships：

6 Contributions of concepts and concept system of DPM for IoT and SC&C

6.1 Contributions to consistency of terms and definitions in all deliverables

An effective process of concept system and terminology building depends on collaborative innovation community capacity building of experts of all the working groups and projects with a multi-dimensional cooperation approach. The core concepts identified by the terminological process make the contribution to all WGs and the deliverables （see Table 4）. Those concepts were used to map with the terminological usage and to check the consistency of the terms and definitions in all deliverables of FG-DPM.

6.2 Contributions to development of use case template

The development of concept system of DPM for IoT and SC&C is an iterative process， it was once used to map use case template of DPM during its development process and has helped to identify core concepts missing and helped adding core factors to the template. Besides， the concept system was used to help build unified elements of use case template of DPM for mapping

and abstracting useful contents and contexts from use cases. Furthermore， the concept system also provided key dimensions to help analyse the generic and specific requirements and capabilities of FG-DPM from use cases collected.

6.3 Contributions to development of new definitions of FG-DPM

（1） Contributions to development of new definitions. The new definitions （e.g. DPM， SC&C， data commercialisation etc.） which are not available from any other terminology sources are created in the terminological work process. The coordination of this process among terminology experts and other experts provides the recommendations and a multi-dimensional cooperation approach to develop a new suitable definition according to the changes of requirements of relevant committees of standardisation explicitly focused on data processing and management for IoT and SC&C.

（2） Contributions to adaptation of existing terms and definitions to new scenarios and specific domains. According to the terminological work principles， if there are definitions that are more or less appropriate and simply need to be modified to meet the needs of DPM for IoT and SC&C， then the preferred option is to add extra notes to the existing definition. There are a number of definitions which are modified and updated to adapt to the context of DPM for IoT and SC&C. Those terms and definitions are also used to adapt the terminology suitable to all the deliverables of the rest of working groups in FG-DPM and at the same time building the links with existing definitions for harmonization of common understandings.

7 Conclusion

The unified concept system of FG-DPM consists of 5 dimensions in consistency with DPM framework with 38 core concepts. This single and unified concept system makes contributions to keep high consistency of definitions of the same term crossing all working groups in the empirical projects of ITU-T FG-DPM， ensuring the same understandings of the same term in all deliverables and use case template， especially， in the establishment of new terms and definitions. This research provides normative process for adaptation and continuous improvement of concept system building based on ISO 704：2009; it provides useful principles to select the core terms and concepts and provides a multi-dimensional cooperation approach to achieve consensus from different working groups and projects to harmonise and cooperate for the existing or new definitions of terms.

Though the concept system and terminology building methods in this paper are limited to the ITU-T FG-DPM for IoT and SC&C empirical work， its implications are not limited to the work done， and its potential values to the concept system and terminology building in a multi-disciplinary and multi-domain scenario have been justified in our other projects e.g. IEC SRD 63235 EDI Smart city system-Methodology for concepts building， and the IEC 60050-831 EDI International Electrotechnical Vocabulary （IEV）-Part 831：Smart city systems.

Commentary

① Generally， the choice of authoritative terminology standards database are：ISO Online browsing platform： available at http：//www.iso.org/obp， IEC Electropedia： available at http：//www.electropedia.org/ITU， Terms and Definitions： available at http：//www.itu.int/net/ITU-R/index.asp？redirect=true&category=information&rlink=terminology-database&lang=en#lang=en.

②This meeting was held in Geneva， 20-25 October， 2017.

③The main elements of use case template have highly consistency with the vocabulary and concept from D0.1.

④This meeting was held in Brussels， 20-23 February 2018.

⑤ This meeting was held in Cairo， 1-3 May 2018.

⑥This meeting was held in Tunis， 17-20 September 2018.

⑦This meeting was held in Seoul， 14-18 January 2019.

⑧This meeting was held in Geneva， 3-7 April， 2019.

⑨This meeting was held in Geneva， 16-19 July， 2019.

References

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First author：

WEI Wei（1988—）， Male， Ph.D. Student of Renmin University of China，Member of IEC SyC Smart Cities WG1， Member of ISO TC268 WG4 on date exchange and sharing. Research focuses on knowledge management and methodology for terminology work of smart city in international standards.E-mail：37952385@qq.com.

第一作者：

魏玮（1988—），男，中國人民大学博士研究生，IEC智慧城市系统委员会术语工作组注册专家，ISO/TC268/SC1/WG4智慧城市基础设施分技术委员会数据交换与共享工作组注册专家，研究方向为知识管理与国际标准中的智慧城市术语构建研究方法论研究。通信方式：37952385@qq.com。

Correspondence author：

AN Xiaomi （1965—）， Female， Professor， Convener of IEC SyC Smart Cities WG1; convener of Data Use in Smart City Task Force in ISO/IEC JTC1/WG11; Member of ISO TC268 WG4 on data exchange and sharing， Member of Chair Advisor Group of ISO/TC46/SC11 and chair of its Terminology Task Force，IEC-ISO-ITU Joint Smart Cities Task Force （J-SCTF）， China Terminology editorial board member.E-mail：anxiaomi@ruc.edu.cn.

通讯作者：

安小米（1965—），女，教授，IEC智慧城市系统委员会术语工作组召集人，ISO/IEC JTC1/WG11智慧城市工作组注册专家及其智慧城市的数据利用任务组召集人、ISO/TC268/SC1/WG4智慧城市基础设施分技术委员会数据交换与共享工作组注册专家、ISO/TC46/SC11主席顾问组委员及其术语任务负责人，IEC-ISO-ITU-T智慧城市通用任务联合工作组委员，《中国科技术语》编委。通信方式：anxiaomi@ruc.edu.cn。