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JR/T 0176.1-2019: Securities and futures industry data model - Part 1: Abstract model design method
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Securities and futures industry data model - Part 1: Abstract model design method
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JR/T 0176.1-2019
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Basic data | Standard ID | JR/T 0176.1-2019 (JR/T0176.1-2019) | | Description (Translated English) | Securities and futures industry data model - Part 1: Abstract model design method | | Sector / Industry | Finance Industry Standard (Recommended) | | Classification of Chinese Standard | A11 | | Classification of International Standard | 03.060 | | Word Count Estimation | 29,215 | | Date of Issue | 2019 | | Date of Implementation | 2019-11-18 | | Issuing agency(ies) | People's Bank of China | JR/T 0176.1-2019: Securities and futures industry data model - Part 1: Abstract model design method
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Securities and futures industry data model-Part 1. Abstract model design method
JR
Financial Industry Standards of the People's Republic of China
Securities and Futures Industry Data Model
Part 1. Abstract model design method
Securities and futures industry data model-
Part 1. Abstract model design method
2019-11-18 released
2019-11-18 implementation
Published by China Securities Regulatory Commission
ICS 03.060
A11
Contents
Foreword ... III
Introduction ... IV
1 Scope ... 1
2 Normative references ... 1
3 Terms and definitions ... 1
4 Overall Design Architecture ... 3
5 Overall Design Approach ... 4
6 Common part design method ... 4
7 Design Method of Trading Part ... 4
7.1 Overview ... 5
7.2 Behavior and Process Carding ... 5
7.3 Data item combing ... 8
7.4 Data Model Design ... 8
7.5 Model verification ... 10
8 Regulatory design methodology ... 10
8.1 Overview ... 10
8.2 Method description ... 10
8.3 Brief description of steps ... 10
8.4 Model verification ... 11
9 Information Disclosure Design Method ... 11
9.1 Overview ... 11
9.2 Template ... 11
9.3 Reflect ... 12
9.4 Refine ... 12
9.4.1 Design of common dimensions ... 13
9.4.2 Atomic data combing ... 13
9.4.3 Combing the Composite Data ... 14
9.4.4 Sorting out reusable data tables ... 14
9.5 ReCombine ... 15
9.6 Regress ... 15
9.7 Brief description of steps ... 15
10 Definition of Yuan Language ... 15
10.1 Definition of data primitives at all levels ... 15
10.1.1 Definition of data primitives ... 15
10.1.2 Definition of Table Primitives ... 17
10.2 Definition of code primitives ... 19
11 Output description ... 20
Appendix A (Normative) Data Types ... 21
Appendix B (Normative Appendix) References ... 22
References ... 23
Foreword
JR/T 0176 "Data Model of Securities and Futures Industry" is divided into 8 parts.
-Part 1. Abstract model design method;
-Part 2. Industry model of the public part of the logical model;
-Part 3. Logical model of securities companies;
-Part 4. Fund company logic model;
-Part 5. Futures company logical model;
-Part 6. Stock exchange logical model;
-Part 7. The logical model of the futures exchange;
-Part 8. The logical model of the regulatory body.
This part is the first part of JR/T 0176.
This section is drafted in accordance with the rules given in GB/T 1.1-2009.
This section is proposed by the Securities Technical Committee of the National Financial Standardization Technical Committee (SAC/TC180/SC4).
This section is under the jurisdiction of the National Financial Standardization Technical Committee (SAC/TC180).
This section was drafted by. China Securities Regulatory Commission Information Center, China Securities Regulatory Commission Securities Fund Institutional Supervision
Ministry, China Securities Information Technology Services Co., Ltd., China Futures Market Monitoring Center Co., Ltd., Shenwan Hongyuan Securities Co., Ltd.,
CSI Inter-Agency Quotation System Co., Ltd., ChinaSoft International Technology Services Co., Ltd., Shenzhen Zhiyuan Sulian Information Technology Co., Ltd.,
Shanghai Jibeike Information Technology Co., Ltd. and Shanghai Lixin Weiyi Software Co., Ltd.
The main drafters of this section. Zhang Ye, Liu Tiebin, Zhou Yunhui, Xie Chen, Luo Liming, Sun Hongwei, Huang Lu, Wang Meng, Zhang Chunyan, Wang Hui,
Cao Lei, Chen Nan, Fu Ziqi, Qiao Wei, Huang Wenlu, Zhu Xu, Liu Jia, Li Guangtao, Liu Guoyong, Yang Cheng, Li Tingting, Li Hai, and Yang Hongfeng.
Introduction
The data level of the securities and futures industry is relatively high, with many institutions, wide types, and various trading methods.
The business is developing rapidly. In order to improve the efficiency of data exchange, standardize the construction of data application systems of industry institutions, and improve the standardization of industry data,
The securities and futures industry organization has carried out industry data model construction work, which aims to clearly describe the data flow, data name, and data definition of the entire market.
Definitions, structure types, code values, and associations, etc., to provide guidance for the construction of internal systems and data exchange between institutions. This section
It is the first part of a series of standards for the data model of the securities and futures industry. an abstract model design method based on which a set of regulatory regulations can be formed
Fan's model framework, and a set of industry data dictionaries based on regulatory rules. Related results are an important part of the industry data model.
It is an industry standard data review basis and a mapping basis for the industry logic model. It regulates the language of industry data, promotes industry data governance,
It is of great significance to assist the industry to supervise the construction of science and technology.
Securities and Futures Industry Data Model
Part 1. Abstract model design methodology
1 Scope
This section specifies the design method of the abstract model of the securities and futures industry, including the overall design architecture, the overall design method, the design method of the public part
Law, transaction part design method, supervision part design method, information disclosure part design method, meta-language definition and output description.
This section applies to the construction of abstract models for the securities and futures industry.
2 Normative references
The following documents are essential for the application of this document. For dated references, only the version corresponding to the date indicated applies
In this document. For undated references, the latest version (including all amendments) applies to this document.
Accounting Standards for Business Enterprises
3 terms and definitions
The following terms and definitions apply to this document.
3.1 Business
3.1.1
Abstract model
Based on relevant laws and regulations, departmental regulations, business rules, and guidance documents of the securities and futures industry, a top-down combing
Method, traversing various types of business activities in the capital market, identifying key business processes and data elements
Usability, stability and scalability data collection.
3.1.2
Subject identity
The main object of participation in the capital market business.
Note. Includes institutions, individuals and products.
3.1.3
Variety
Various financial instruments and services involved in capital market business.
3.1.4
Finance
Funding activities and capital relations involved in the participation of various institutions in the capital market business process.
3.1.5
Trading
All types of entities involved in the transfer of capital market business.
3.1.6
Regulation
The competent authorities shall, in accordance with laws and regulations, uniformly supervise and manage the securities and futures markets nationwide, maintain market order, and ensure their legal operation.
3.1.7
Information disclosure
The disclosure entity reports its own financial changes, operating conditions and other information and information to the regulatory authorities and exchanges, and makes it public or
Announced behavior.
3.1.8
Behavior
A series of actions by various market entities in the process of trading, supervision, and information disclosure.
3.1.9
Process
Links in behavioral development.
3.1.10
Regulation object
Competent authorities are responsible for the objects of supervision in accordance with the law.
3.1.11
Regulation theme
Supervision duties on a certain aspect or area of the subject of supervision.
3.1.12
Regulation method
Regulatory means or measures adopted for a subject of supervision of the subject of supervision.
Note. Including on-site inspection, administrative permission, etc.
3.1.13
Regulation business
Specific work content for supervising a supervision subject of the supervision object.
Note. Including the formulation of regulations, administrative permits, audit penalties, daily supervision, macro supervision and internal management.
3.1.14
Regulation process
The process of supervising a regulatory subject of a subject using a certain regulatory approach.
3.2 Data
3.2.1
Meta semantics
The description of the data.
3.2.2
Entity-relationship diagram
A way to graphically describe the entity relationships of a data model.
Note. The entity relationship diagram is referred to as the ER diagram.
3.2.3
Data dictionary
A collection of data that defines or describes the types of data involved in the standard.
3.2.4
Atomic data
Independent and indivisible smallest data unit.
3.2.5
Compound data
Data that is composed or processed by atomic data through some relationship.
3.2.6
Reusable table
A data set consisting of atomic data or composite data that is commonly used in multiple business processes.
3.2.7
Semantically specified table
A dataset consisting of atomic or composite data describing a particular business logic.
3.2.8
Camel-case
A method of naming variables and functions with a mix of uppercase and lowercase letters.
4 overall design architecture
The abstract model follows the overall design architecture of "1 3 N" as shown in Figure 1, where.
-"1" refers to the industry's public part of the abstract model, including the three subjects of subject, variety, finance, and other public parts. industry
The public part can be reused by at least two main lines of business in "transaction", "regulation", and "disclosure", and maintain its own syntax,
Semantic consistency.
-"3" refers to the model formed by the three main lines of business. "transaction", "regulation" and "disclosure".
This degree of coupling is not high. The personalized part of the group allows conflict situations with the same name and different meanings. It is not required to maintain a syntactic and semantic meaning.
To. Each group of models is further divided into the public part of the business category and a dedicated data model covering the application systems under it. Of which public
Parts can be shared within this business category, and semantic and grammatical consistency within the group should be maintained.
-"N" refers to the dedicated data model of a specific application system in the model formed by each business line, and the data in this part
The sum table is specific to an application and is only used in that application, and is not reused with models of other applications in the main line of the business. Each "N"
The data and tables involved in it are not required to be consistent in syntax and semantics.
Figure 1 "1 3 N" design architecture
The design pattern of "1 3 N" can be expressed as.
In the formula.
SDOM-abstract model;
P1 --subject, variety, finance, other public parts;
P2i-
3 disclose the public part,
2 regulation of the public sector,
1 trading public part,
P3ij --j represents a dedicated data model for a specific application system under each business line.
5 Overall design method
The abstract model as a whole adopts the "subject-behavior-relevance" (IBRity) design method
Design, that is, taking the identity as the core, extending its various market behaviors, and summing up the relevant relationships
(Relevance), extract and divide into a series of data tables for specific business scenarios and applications, and finally form the overall frame of the data model
frame. The specific content is shown in Figure 2.
Figure 2 IBR methodology
6 Common part design method
The public part of the abstract model mainly includes three themes. variety, subject, and finance. The specific design methods are as follows.
a) Variety theme. With reference to GB/T 35964-2018, combined with the actual situation of the domestic capital market to adjust and supplement the classification system of varieties,
Further refinement by adding entities, attributes, codes, etc., to form a variety theme model.
b) subject matter. Combined with the actual situation of the capital market, according to the latest regulatory documents, the main classification system formed was supplemented and supplemented
Entities, attributes, codes, etc., form the main subject model.
c) financial topics. Combining with the practical application of capital markets in the fields of information disclosure, trading, and supervision in accordance with the Accounting Standards for Business Enterprises
It forms a financial statement system and supplements entities, attributes, codes, etc. to form a financial theme model.
7 Design method of transaction part
7.1 Overview
The trading model of the abstract model uses "Securities-Process-Behavior" (SPB)
The design method is to first select the varieties to be sorted according to the variety theme of the public part, and then to sort out the main business processes and convection based on each variety.
Summarize and extract the various transaction links in the process chart to sort out the behavior list; conduct each behavior according to the various processes before, during, and after the transaction.
Segmentation to form a two-dimensional map of the behavior of each variety. For the behavior in the two-dimensional diagram of the behavior process, sort out the links of the business process and each link.
Output to form a swim lane diagram of the business process; refine the data items involved in the output in the business process swim lane map, and clarify the definition and attribute of the data item
Sexual information. Data items are merged and refined according to topics to form data objects, and data objects and their relationships are expressed in the form of entity relationship diagrams.
Form a theoretical data model. Finally, the theoretical model is verified and perfected by the data model of the real system. The specific combing steps are shown in Figure
3 shown.
Figure 3 SPB carding method
7.2 Behavior and Process Carding
The main business processes involved in each product are sorted out. Taking the margin financing and securities lending as an example, the specific behavior and process sorting steps are as follows.
a) According to the analysis of the three main processes of investor financing purchase, investor stock selling, and securities company services, three major processes are formed.
The main flow chart and an example of the carding results are shown in Figure 4.
Figure 4 Sample backbone process
b) Summarize and extract each transaction link in the main flow chart, and sort out the list of behaviors involved in each variety;
In the extraction process, the independence of each behavior must be guaranteed, and there should be no inclusion relationship between behaviors. An example of carding results is shown in Figure 5.
As shown.
Figure 5 Sample behavior list
c) Segment each behavior according to the time sequence before, during and after the transaction, and identify the basic operations involved in each behavior.
Finally, a two-dimensional matrix diagram of the behavior process of the variety is formed. An example of carding results is shown in Figure 6.
behavior
process
Figure 6 Example of two-dimensional diagram of behavior process
d) After the two-dimensional matrix diagram of the behavior process is formed, the specific detailed flowchart is sorted out by referring to the relevant business process and rule definitions, which is convenient
Data items will be sorted out and refined subsequently. An example of carding results is shown in Figure 7.
Figure 7 Sample business flowchart
7.3 Data item combing
Refer to the business flow chart to sort out and refine the data items for each business link and classify them according to the business characteristics of the data items. Comb the knot
An example of the results is shown in Figure 8.
Figure 8 Sample data item extraction
7.4 Data Model Design
The data model design steps are as follows.
a) Classification of data items sorted out in all business processes, aggregated according to the principle of merging similar items to form independent data
entity;
b) Construct the relationship between entities based on the relevance of the business;
c) Describe and express the model in the form of entity relationship diagrams.
An example of model design results is shown in Figure 9.
Explanation.
Identification relationship. When a foreign key is migrated from the parent entity to the primary key area of the child entity, an identification relationship is formed between the two entities. The identification relationship is represented by a solid line connecting the two entities.
Non-identifying relationship. When a foreign key migrates from the parent entity to the non-primary key area of the child entity, a non-identifying relationship is formed between the two entities. The non-identifying relationship is represented by a dashed line connecting the two entities.
Figure 9 Example of entity relationship diagram of margin financing
7.5 Model validation
Using the existing business system as the object of regression verification, check the model's coverage of the entire data range of the business system. Main investigation
The model conforms to the actual business situation, including the coverage of existing services and compatibility with future services.
Type for verification.
8 Regulatory design methods
8.1 Overview
The supervision part mainly adopts the “Theme/Method” (T/M) method, which is based on relevant laws and regulations
To build a regulatory model based on the principle of law.
8.2 Method description
In accordance with laws and regulations, the supervision object (such as an accounting firm), the supervision topic (such as obtaining the qualification of the securities and futures industry), and the supervision method
(Such as administrative license) carry out three-dimensional analysis, and then sort out the corresponding regulatory process and related information on this basis, so as to derive a regulatory model.
Supervision data combing space = TM, where T = IBV
Among them, T (Theme), the supervision theme
M (Method), supervision method
I (Identity), the regulatory body
B (Business), regulatory business
V (Variety), variety
The principle of the T/M method is shown in Figure 10.
Figure 10 T/M method
8.3 Steps
The T/M carding method uses four steps to tease out the regulatory model.
a) Refer to the main part of the abstract model of the industry to sort out the regulatory entities (I), and sort out the corresponding regulatory business (B) and related issues for each entity.
Variety (V), the point where IBV meets is the subject of supervision (T);
b) sort out the supervision methods according to the matter before, during and after (M);
c) T and M are orthogonally traversed. For each item of TM, sort out the supervision process according to laws and regulations and detailed cases.
Find the relevant data set of each process according to the extraction architecture;
d) According to the model design method, according to each supervision method, extract common processes and common data tables and data items.
8.4 Model validation
Investigate how the regulatory model fits the actual business, including coverage of existing business and compatibility with future business.
Take several representative businesses as an example, check the model, check how the business is reflected in the model, and the data involved in the business.
Sorting and refining according to items and data objects.
9 Information Disclosure Design Method
9.1 Overview
The information disclosure part mainly adopts T4R (Templete Reflect Refine Recombine Regress, T4R) method. The
The method is to use a model combing method based on information disclosure templates, including templates (Templete), mapping (Reflect), refinement (Refine),
Recombine, Regress and other five important links. The template is the basis of information disclosure, and the mapping is based on the template system
Determine semantically independent data tables, further refine atomic, composite data and reusable data tables, and reorganize for logical deduction and testing
After several iterations, the final disclosure information model is formed. The relationship of each link is shown in Figure 11.
Figure 11 Relationship between links in the disclosure model
9.2 Template
A template is a concrete manifestation of information disclosure. To build a template, first, based on a unified subject and variety classification, combine the subject and product.
This kind of operation cycle is to sort out the laws and regulations related to information disclosure, and standardize the information disclosure business rules and requirements.
The resulting document is the information disclosure template, as shown in Figure 12.
Figure 12 Information disclosure template
The template is used to guide the application of each step of the subsequent information disclosure process. Under normal circumstances, one type of information disclosure business corresponds to one
Information disclosure template. The template consists of headings, paragraphs, text and tables. It defines both the business subjects and the business subjects.
The scenario is described.
The business subjects and tables in the template can be reused. Subjects with the same business meaning and completely the same form can be defined first.
In principle, it is defined only once, and the template defined later only refers to this subject, and does not repeat the definition. Template is a comb of information disclosure model
The starting point and basis for the management and creation, and the subsequent modeling methods to complete the conversion of information disclosure templates and standards to information models.
9.3 Mapping (Reflect)
After the information disclosure template is selected, the structure of the template is combed to define the semantically independent data tables and the multiple levels between the data tables.
Constitute a relationship. For example, the annual reports of listed companies of general enterprises include reports of the board of directors, reports of the board of supervisors, and financial reports, among which financial reports include
Audit reports, financial statements, notes to financial statements, etc.
Semantic independent data tables can be modeled for applications. It can be used by other semantically independent data tables, reusable data tables, composite data,
Atomic data together. Taking the general business consolidated financial statements as an example, the specific composition based on the business is shown in Figure 13.
Figure 13 Sample Composition of General Business Consolidated Financial Statements
9.4 Refine
9.4.1 Design of Common Dimensions
The work of refining atomic data is to extract the smallest data sheet that does not need to be divided according to the business meaning of the disclosed information in each template.
yuan.
In the specification requirements for information disclosure, a common dimension is used for background information such as the report type, data status, and classification category of the data.
The data of different background information in the same report are labeled and distinguished by common dimensions.
In the model definition, atomic data and composite data with multiple background information are labeled in the form of "atomic or composite data [dimensions]".
For example, the general dimension labeling form of "fund short name of subordinate tiered funds" in fund information disclosure is shown in Figure 14.
Figure 14 "Dimensions of Funds of Subordinate Graded Funds" General Dimension Design
9.4.2 Atomic Data Carding
Judge the appropriateness of the granularity of atomic data according to the following principles.
-Financial data uses atomic data as independent financial subjects and related independent concepts in the financial statements and notes to the financial statements;
--Non-financial data selects concepts with statistical and analytical value as atomic data. When a concept can be subdivided theoretically, its subdivision
When the concept does not have statistical and analytical value, it is no longer subdivided;
-When information disclosure laws and regulations explicitly list a content to be disclosed, the content should be defined as at least one atomic number
According to data, if further subdivision is necessary, it can be defined as composite data.
Note. Most of the financial data listed in the above segmentation principles can be calculated as composite data.
An example of atomic data is shown in FIG. 15.
Figure 15 Sample atomic data
9.4.3 Combing the Composite Data
The combing method of compound data is based on the combing of atomic data, which can be processed...
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