CN105893522B - Big data analysis model business development generation and management system - Google Patents
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Abstract
The invention discloses a big data analysis model service development generation and management system, which comprises a user side, a server side and a cloud computing and storage platform, wherein the user side comprises a user model design and generation subsystem and a model local database, and the server side comprises: the system comprises a tenant management subsystem, a model audit transaction subsystem, a model interpretation and compiling subsystem, a model execution scheduling subsystem, a model metadata management subsystem, a calculation result display subsystem, a model transaction database, a metadata database and a model API database. The framework adopts a centralized distributed controllable, manageable and tradable high-efficiency big data operation and maintenance mode of calculation, model and storage resources, and specifically comprises six functions of a distributed data storage calculation platform, model design and generation, model management and trading, model quality audit and verification, model interpretation and execution, tenant management and access control and the like.
Description
Technical Field
The invention belongs to the field of big data analysis and development processing, and particularly relates to a big data analysis model service development generation and management system.
Background
In recent years, with the increasing maturity of technologies related to large-scale big data, research on big data models has achieved much effort and is applied to aspects of life such as electronic commerce, stock market finance fields, social networks, and medical health fields.
Therefore, while a set of big data analysis model is formed, a big data model development generation and management framework needs to be developed, and the development and generation of the targeted and intelligent big data model are provided for different users, so that the personalized requirements of the users are met.
The construction technology of the current big data storage and calculation center is mature, commercial products such as Cloudera, GBase and TeraData are formed, and good technology and product preparation are provided for data center construction. However, big data analytics, particularly in terms of industry business oriented, customizable big data analytics models, are product scarcity. The main reasons are that: (1) industry business thresholds lead to difficulties in analytical model design. The data analysis model designer lacks industrial business knowledge, so that a model product meeting the requirement is difficult to design; (2) the potential value of the data is not clear. The potential value of the business personnel to the data, especially the analysis value is not clear, so that the business personnel are difficult to put forward the specific data analysis requirement; (3) the business difference between industries is obvious, and a data analysis model across industries is difficult to form. The research and development team thinks that the business intelligent analysis market needs a data storage, analysis and model management system which can exert business specialties of industry practitioners and connect big data analysis designers and the industry practitioners.
Disclosure of Invention
The invention aims to overcome the defects of the existing big data analysis product in the aspects of industry-service-oriented customizable big data analysis model, and provides a big data analysis model service development, generation and management system, which adopts a centralized distributed controllable, manageable and tradable high-efficiency big data operation and maintenance mode of calculation, model and storage resources, and specifically covers six functions of a distributed data storage calculation platform, model design and generation, model management and tradition, model quality audit and verification, model interpretation and execution, tenant management and access control and the like.
The purpose of the invention is realized by the following technical scheme: a big data analysis model service development generation and management system comprises a user side, a server side and a cloud computing and storage platform, wherein the user side comprises a user model design and generation subsystem and a model local database, the user model design and generation subsystem provides visual model analysis, design, test, release and execution result graphical display functions, and the model local database stores local model data;
the server side comprises:
the tenant management subsystem provides the functions of multi-tenant model use management, charging and statistical analysis;
the model auditing transaction subsystem provides functions of model issuing, background auditing, running verification, order submitting, order auditing and order payment;
the model interpretation and compilation subsystem provides functions of model function interpretation analysis, model compilation and job generation;
the model execution scheduling subsystem provides a multi-user distributed model execution scheduling function;
the model metadata management subsystem provides metadata management functions including model tagging description, model library API association and model transaction information;
the calculation result display subsystem provides a diversified model calculation result display function;
a model transaction database for storing model transaction related data;
a metadata repository storing metadata;
and the model API database stores the model API.
The user model design and generation subsystem provides a graphical user big data analysis model development and generation interface, meanwhile, the system opens the service capability calling of the existing analysis model for the user to use, provides the functions of analysis model operation, result display, model release and purchase, and promotes the sharing and multiplexing of the big data analysis model.
The model auditing transaction subsystem provides manual and rule-based model auditing functions, provides a model operation verification function based on a calculation result, provides a model integrity guarantee function based on hash calculation, and provides a model transaction function based on a third-party payment platform.
The model interpretation and compilation subsystem converts the model calling relationship into a DAG (direct current) diagram on the basis of customizing an XML (extensible markup language) marking mode, analyzes and converts the DAG diagram into an operation script which can be identified by a big data computing storage platform, and can perform model nesting relationship check, model calling consistency check and model calling permission check.
The model metadata management subsystem self-defines an XML mark model and provides functions of searching and recommending model keywords and tracing model sources on the basis of model marking.
The calculation result display subsystem provides graph dynamic and static display functions of relational data and non-relational data by using a general data driving interface and supports user-defined data interpretation and presentation; and a chart display function supporting user dragging is realized.
The scheduling function of the model execution scheduling subsystem comprises model static analysis and scheduling, model execution history analysis and scheduling, execution request submission and result return.
The modes of the user for designing and generating the subsystem by using the user model comprise a payment mode and a free mode.
The model transaction comprises a model ownership transaction and a model usage right transaction.
The model tags include the main functions, data processing objects, ownership, source, and resource requirements of the model.
The invention has the beneficial effects that: the invention provides a big data analysis model service development generation and management system, which adopts a centralized distributed controllable, manageable and tradable high-efficiency big data operation and maintenance mode of calculation, model and storage resources, and specifically covers six functions of a distributed data storage calculation platform, model design and generation, model management and tradition, model quality audit and verification, model interpretation and execution, tenant management and access control and the like.
Drawings
FIG. 1 is a block diagram of a big data analysis model business development generation and management system architecture;
FIG. 2 is a block diagram of a user model design and generation subsystem architecture;
FIG. 3 is a workflow diagram of a visualization model designer;
fig. 4 is a user registration flowchart.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a big data analysis model service development, generation and management system includes a user side, a server side, and a cloud computing and storage platform, where the user side includes a user model design and generation subsystem and a model local database, the user model design and generation subsystem provides visual model analysis, design, test, release, and execution result graphical display functions, and the model local database stores local model data;
the server side comprises:
the tenant management subsystem provides the functions of multi-tenant model use management, charging and statistical analysis;
the model auditing transaction subsystem provides functions of model issuing, background auditing, running verification, order submitting, order auditing and order payment;
the model interpretation and compilation subsystem provides functions of model function interpretation analysis, model compilation and job generation;
the model execution scheduling subsystem provides a multi-user distributed model execution scheduling function;
the model metadata management subsystem provides metadata management functions including model tagging description, model library API association and model transaction information;
the calculation result display subsystem provides a diversified model calculation result display function;
a model transaction database for storing model transaction related data;
a metadata repository storing metadata;
and the model API database stores the model API.
The user model design and generation subsystem provides a graphical user big data analysis model development and generation interface, meanwhile, the system opens the service capability calling of the existing analysis model for the user to use, provides the functions of analysis model operation, result display, model release and purchase, and promotes the sharing and multiplexing of the big data analysis model.
The model auditing transaction subsystem provides manual and rule-based model auditing functions, provides a model operation verification function based on a calculation result, provides a model integrity guarantee function based on hash calculation, and provides a model transaction function based on a third-party payment platform.
The model interpretation and compilation subsystem converts the model calling relationship into a DAG (direct current) diagram on the basis of customizing an XML (extensible markup language) marking mode, analyzes and converts the DAG diagram into an operation script which can be identified by a big data computing storage platform, and can perform model nesting relationship check, model calling consistency check and model calling permission check.
The model metadata management subsystem self-defines an XML mark model and provides functions of searching and recommending model keywords and tracing model sources on the basis of model marking.
The calculation result display subsystem provides graph dynamic and static display functions of relational data and non-relational data by using a general data driving interface and supports user-defined data interpretation and presentation; and a chart display function supporting user dragging is realized.
The scheduling function of the model execution scheduling subsystem comprises model static analysis and scheduling, model execution history analysis and scheduling, execution request submission and result return.
The modes of the user for designing and generating the subsystem by using the user model comprise a payment mode and a free mode.
The model transaction comprises a model ownership transaction and a model usage right transaction.
The model tags include the main functions, data processing objects, ownership, source, and resource requirements of the model.
The user model design and generation subsystem includes the following terms and basic concepts:
1) model: one model is a logical DAG network formed by multiple steps through hop-joining; the model is composed of steps, jumps, model entries and model settings. A single model entry may be placed on the canvas multiple times; for example, a single model entry may be picked up and placed on the canvas in a different configuration. Model setup is an option for methods of controlling a model's behavior and recording a model's operation; the model is composed of a plurality of combined models; the combined model is composed of a plurality of sub-combined models; the minimum sub-composition model consists of a plurality of atom models; a minimum composition model may contain a plurality of sequentially executed atomic models; the atomic model is composed of one or more base models. Basic model: the method comprises the following steps of batch processing and streaming processing, wherein the streaming processing model is a MapReducce or Spark data processing model component. The individual base models that make up the atomic model may be batch or streaming.
2) The steps and jumps: the steps are MapReduce or Spark data processing model components, such as text file input or Hive table output. Providing more than 140 groups by function in a user model design and generation subsystem; such as input, output, scripts, etc. Hops are data paths connecting steps, allowing data to be transferred between two steps, a step may have many hops connecting, some connecting two steps together, some as input or output for only one step, data flowing from one step to a different step in a conversion, a user model design and generation subsystem representing hops with an arrow, hops allowing data to be transferred between steps, and determining the direction of flow between data flows, data either being copied to each subsequent step or distributed over subsequent steps if a step sends output to more than one subsequent step.
3) A user group: the system comprises a group of a plurality of users, and the users in the group can realize the sharing of data, services and models.
4) The user: system users and managers.
5) And (3) user roles: system administrator, model design administrator, business design administrator, data administrator, business user, data user.
6) Service: a data loading, analysis and presentation service includes a plurality of models.
7) Data authority: including readable, writable, and searchable categories.
8) Service authority: including four classes that are readable, writable, searchable, executable.
The system provides a multi-tenant-oriented business model management platform framework, which aims to construct a business model library SaaS application with the characteristic of multiple tenants, enables the tenants to effectively manage model versions through the application, and provides corresponding model customization services for individual requirements of tenant business models.
As shown in fig. 2, the system structure of the user model design and generation subsystem is divided into three layers: an application layer, an engine layer, and a kernel layer. The application layer comprises a visualization model designer module used for providing a visualization model designer; the model submitting, managing and trading module is used for submitting tests; and the model display module is used for realizing the graphical display of the analysis result. The engine layer comprises a metadata management module, and the metadata management module manages the metadata through a metadata engine; the basic model library comprises basic model controls and custom model controls; and the operation tracking module is used for performing operation tracking on the model. The kernel layer comprises a structured database module and an unstructured database module, and data management is carried out through the metadata storage kernel; the interface layer module provides a multifunctional operation interface to the outside through the resolver kernel, the log kernel and the exception handling kernel; and the third party payment interface is used for carrying out payment management through user authentication and a security encryption kernel. The user model design and generation subsystem also provides support for a mainstream operating system; supporting data sources in a plurality of CDHs; the method comprises the steps of integrating a script development environment and supporting editing, running and debugging of scripts; comprehensive data acquisition, conversion, analysis and output (data integration) functions; providing CDH for submitting, releasing, executing and analyzing functions of a big data model process of a calculation and storage engine; the metadata management, import and export of the model are supported, and convenient deployment and transplantation functions are provided; and various field-level mapping conversions are supported, such as type conversion, field operation, reference conversion, character string processing, character set conversion, null value processing, date conversion, aggregation operation, set value taking, field segmentation, field combination and the like.
As shown in fig. 3, which is a work flow diagram of a visualization model designer, the visualization model designer realizes combination of multiple basic models through a visualization means, performs secondary visualization editing on the models, and generates customized complex models for specific services. The basic model comprises a start model, an end model, a basic calculation analysis model, a skip model and a control model; implementing abstract definitions of binding relationships.
When developing and generating a big data model, a user authenticated by the user obtains corresponding development authority and access authority, and a registration flow of the tenant is shown in fig. 4.
Claims (9)
1. A big data analysis model business development generation and management system comprises a user side, a server side and a cloud computing and storage platform, and is characterized in that: the user side comprises a user model designing and generating subsystem and a model local database, wherein the user model designing and generating subsystem provides visual model analyzing, designing, testing, issuing and executing result graphical display functions, and the model local database stores local model data;
the user model design and generation subsystem provides a graphical user big data analysis model development and generation interface, and meanwhile, the system opens the service capability calling of the existing analysis model for the user to use, provides the functions of analysis model operation, result display, model release and purchase, and promotes the sharing and multiplexing of the big data analysis model;
the user model design and generation subsystem includes the following terms and basic concepts:
1) model: one model is a logical DAG network formed by multiple steps through hop-joining; the model is composed of steps, jumps, a model entry and model settings; a single model entry may be placed on the canvas multiple times; model setup is an option for methods of controlling a model's behavior and recording a model's operation; the model is composed of a plurality of combined models; the combined model is composed of a plurality of sub-combined models; the minimum sub-composition model consists of a plurality of atom models; a minimum composition model may contain a plurality of sequentially executed atomic models; the atom model is composed of one or more base models; basic model: the system comprises two basic models, namely a batch processing model and a streaming MapReduce model and a Spark model, wherein the single basic model for forming an atomic model can be a batch processing model or a streaming processing model;
2) the steps and jumps: the method comprises the steps of processing a model component by MapReduce or Spark data, wherein the model component comprises text file input or Hive table output; providing more than 140 groups by function in a user model design and generation subsystem; hops are data paths connecting steps, allowing data to be transferred between two steps, a step may have many hops connecting, some connecting two steps together, some as input or output for only one step, data flows from one step to a different step in a conversion, user model design and generation subsystem represents hops with an arrow, hops allow data to be transferred between steps, and also determines flow direction between data flows, data is either copied to each subsequent step or distributed over subsequent steps if a step sends output to more than one subsequent step;
3) a user group: the system comprises a group containing a plurality of users, wherein the users in the group can realize the sharing of data, services and models;
4) the user: system users and managers;
5) and (3) user roles: a system administrator, a model design administrator, a business design administrator, a data administrator, a business user, and a data user;
6) service: a data loading, analyzing and displaying service, wherein the service comprises a plurality of models;
7) data authority: including readable, writable, and searchable categories;
8) service authority: the system comprises four types of reading, writing, checking and executing;
the server side comprises:
the tenant management subsystem provides the functions of multi-tenant model use management, charging and statistical analysis;
the model auditing transaction subsystem provides functions of model issuing, background auditing, running verification, order submitting, order auditing and order payment;
the model interpretation and compilation subsystem provides functions of model function interpretation analysis, model compilation and job generation;
the model execution scheduling subsystem provides a multi-user distributed model execution scheduling function;
the model metadata management subsystem provides metadata management functions including model tagging description, model library API association and model transaction information;
the calculation result display subsystem provides a diversified model calculation result display function;
a model transaction database for storing model transaction related data;
a metadata repository storing metadata;
and the model API database stores the model API.
2. The big data analysis model business development generation and management system according to claim 1, wherein: the model auditing transaction subsystem provides manual and rule-based model auditing functions, provides a model operation verification function based on a calculation result, provides a model integrity guarantee function based on hash calculation, and provides a model transaction function based on a third-party payment platform.
3. The big data analysis model business development generation and management system according to claim 1, wherein: the model interpretation and compilation subsystem converts the model calling relationship into a DAG (direct current) diagram on the basis of customizing an XML (extensible markup language) marking mode, analyzes and converts the DAG diagram into an operation script which can be identified by a big data computing storage platform, and can perform model nesting relationship check, model calling consistency check and model calling permission check.
4. The big data analysis model business development generation and management system according to claim 1, wherein: the model metadata management subsystem self-defines an XML mark model and provides functions of searching and recommending model keywords and tracing model sources on the basis of model marking.
5. The big data analysis model business development generation and management system according to claim 1, wherein: the calculation result display subsystem provides graph dynamic and static display functions of relational data and non-relational data by using a general data driving interface and supports user-defined data interpretation and presentation; and a chart display function supporting user dragging is realized.
6. The big data analysis model business development generation and management system according to claim 1, wherein: the scheduling function of the model execution scheduling subsystem comprises model static analysis and scheduling, model execution history analysis and scheduling, execution request submission and result return.
7. The big data analysis model business development generation and management system according to claim 1, wherein: the modes of the user for designing and generating the subsystem by using the user model comprise a payment mode and a free mode.
8. The big data analysis model business development generation and management system according to claim 2, wherein: the model transaction comprises a model ownership transaction and a model usage right transaction.
9. The big data analysis model business development generation and management system of claim 4, wherein: the model tags include the main functions, data processing objects, ownership, source, and resource requirements of the model.
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| CN106776326B (en) * | 2016-12-20 | 2020-07-28 | 中国农业银行股份有限公司 | Modeling method and system of data analysis model |
| CN108108209A (en) * | 2017-12-28 | 2018-06-01 | 中交水运规划设计院有限公司 | Interworking architecture, exchange method and the storage medium of computation model |
| CN108874395B (en) * | 2018-05-22 | 2022-03-18 | 四川创意信息技术股份有限公司 | Hard compiling method and device in modular stream processing process |
| CN109002548A (en) * | 2018-07-31 | 2018-12-14 | 长沙龙生光启新材料科技有限公司 | A kind of couple of multi-user carries out the method and system of big data analysis |
| CN109711825A (en) * | 2018-12-29 | 2019-05-03 | 北京航天数据股份有限公司 | A kind of comprehensive method of commerce of industry pattern and the comprehensive transaction platform of industry pattern |
| CN110231939B (en) * | 2019-05-16 | 2024-05-28 | 平安科技（深圳）有限公司 | Model generation method, system, computer device and storage medium |
| CN110750530B (en) * | 2019-09-11 | 2022-08-02 | 口碑(上海)信息技术有限公司 | Service system and data checking method thereof |
| US11556821B2 (en) | 2019-09-17 | 2023-01-17 | International Business Machines Corporation | Intelligent framework updater to incorporate framework changes into data analysis models |
| CN110837620A (en) * | 2019-11-14 | 2020-02-25 | 帝国理工创新有限公司 | Advanced online database system for publishing and running model and hosting data |
| CN112947919A (en) * | 2019-11-26 | 2021-06-11 | 北京京东振世信息技术有限公司 | Method and device for constructing service model and processing service request |
| CN111523808B (en) * | 2020-04-24 | 2022-12-16 | 中国建设银行股份有限公司 | Model centralized management method, system, equipment and storage medium |
| CN111523810A (en) * | 2020-04-24 | 2020-08-11 | 同盾控股有限公司 | Enterprise-level model management method, system, device and storage medium |
| CN111866177B (en) * | 2020-08-04 | 2023-03-24 | 蝉鸣科技(西安)有限公司 | Function management method and system on SaaS platform |
| CN114925045B (en) * | 2022-04-11 | 2024-05-03 | 杭州半云科技有限公司 | PaaS platform for big data integration and management |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942089A (en) * | 2014-04-11 | 2014-07-23 | 北京理工大学 | Simulation resource model base management system |
| CN104573063A (en) * | 2015-01-23 | 2015-04-29 | 四川中科腾信科技有限公司 | Data analysis method based on big data |
| CN104699777A (en) * | 2015-03-10 | 2015-06-10 | 中国联合网络通信集团有限公司 | Association method and system of management plane and service plane of big data analysis and mining |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080256186A1 (en) * | 2007-04-12 | 2008-10-16 | Hartmann Thomas W | Collaboration system |
| US20090288066A1 (en) * | 2008-02-12 | 2009-11-19 | Electronics And Telecommunications Research Institute | Editing apparatus and method for test driven development |
| US9128804B2 (en) * | 2011-02-10 | 2015-09-08 | Software Ag | Systems and/or methods for identifying and resolving complex model merge conflicts based on atomic merge conflicts |
| CN104881491A (en) * | 2015-06-11 | 2015-09-02 | 广州市云润大数据服务有限公司 | Software development system based on big data platform |
-
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103942089A (en) * | 2014-04-11 | 2014-07-23 | 北京理工大学 | Simulation resource model base management system |
| CN104573063A (en) * | 2015-01-23 | 2015-04-29 | 四川中科腾信科技有限公司 | Data analysis method based on big data |
| CN104699777A (en) * | 2015-03-10 | 2015-06-10 | 中国联合网络通信集团有限公司 | Association method and system of management plane and service plane of big data analysis and mining |
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