CN115422169B - Data warehouse construction method and device based on commercial advertisement scene - Google Patents

Abstract

The embodiment of the invention discloses a data warehouse construction method based on commercial advertisement scenes, which comprises the following steps: investigation is carried out on the target business to obtain business processes respectively corresponding to different target businesses; determining a business event or business action in each target business according to the business process to acquire a corresponding business process; the method comprises the steps of establishing a data warehouse of target services, wherein the data warehouse at least comprises a dimension table, a detail table and a summary table, the dimension table is used for unifying a calculation algorithm of the target services and determining a correlation table of the target services, the detail table is used for recording a service process corresponding to each target service, and the summary table is used for recording a subject domain and a data domain of the target services. By fully considering the characteristics of messy mobile commercial advertisement data, large data volume, chaotic format and the like, the advertisement service data can be efficiently managed by acquiring the service flow, acquiring the corresponding service process according to the service flow and then establishing a data warehouse.

Description

Method and device for constructing data warehouse based on commercial advertising scene

technical field

The invention relates to the technical field of computer data management, in particular to a method and device for constructing a data warehouse based on a commercial advertisement scene.

Background technique

At present, with the rapid development of the mobile Internet, a new thing - mobile advertising has emerged, which is favored by advertisers for its accuracy, immediacy, interactivity, diffusion, integration and measurability. , so as to develop rapidly. At the same time, there is also the emergence of a mobile advertising platform, which is a platform or intermediary that connects application developers and advertisers. On the platform, developers provide applications, advertisers provide advertisements, and mobile advertising platforms provide SDKs for corresponding mobile phone systems. Developers download the SDK, and by using the tools in the SDK, they can embed advertisements into applications, and upload these applications to the mobile Internet through other channels. After end users download applications, browse or click on advertisements, advertisers will Fees are paid to developers.

The part of data analysis is mainly to analyze the data generated in the operation of the existing mobile advertising platform, and generate reports for relevant personnel to view. According to the different personnel who view the report data, it can be mainly divided into three aspects. One is the report statistics from the perspective of developers. For developers, they mainly want to know how much money is earned by each application launched on the platform every day; The second is report statistics from the perspective of advertisers. For advertisers, they mainly want to know how many times their advertisements are displayed on the platform every day, how many times they are clicked, and how much they have paid to developers; It is a report statistics from the perspective of decision makers. For the decision makers of the company, they mainly care about how many advertisers and developers are added to the platform every day, how many applications are active every day, how many ads are displayed and clicked every day, which Which types of ads are clicked the most, which app has the most users, and so on. It can be seen that for different users, the system needs to perform multi-level analysis on these large amounts of heterogeneous data from different dimensions. If a data warehouse is used, then with the rapid growth of data, the traditional data warehouse is facing information Explosive new challenges. It will be very time-consuming to analyze such a huge amount of data purely by traditional data warehouse architecture, and it is difficult to manage these data efficiently.

Contents of the invention

In view of the above defects, the embodiment of the present invention discloses a data warehouse construction method and device based on a commercial advertisement scene, which can efficiently manage huge and complex advertisement business data.

The first aspect of the embodiment of the present invention discloses a data warehouse construction method based on a commercial advertisement scene, including:

Conduct research on the target business to obtain the business processes corresponding to different target businesses;

Determine the business event or business action in each target business according to the business process, so as to obtain the corresponding business process;

Establish a data warehouse for the target business, the data warehouse includes at least a dimension table, a detailed table and a summary table, the dimension table is used to unify the calculation algorithm of the target business and determine the associated table of the target business, and the detailed table is used to record each A business process corresponding to a target business, the summary table is used to record the subject domain and data domain of the target business.

As an optional implementation, in the first aspect of the embodiment of the present invention, the data warehouse includes ODS level, DW level, DMA level, DMT level and DA level, and the ODS level is the access layer of raw data , the DW level is used to store the business process of the target business, the DMA level is used to integrate and summarize data, the DMT level is used to summarize target business topics, and the DA level is used to respond to personalized data requirements .

As an optional implementation, in the first aspect of the embodiment of the present invention, the ODS level, DW level, DMA level, DMT level and DA level of the data warehouse are called according to preset rules.

As an optional implementation manner, in the first aspect of the embodiments of the present invention, the determining the business event or business action in each target business according to the business process to obtain the corresponding business process includes:

Determine the corresponding business operation node of the target business according to the business process, and the business operation node includes business events and business actions;

Organize the business events and business actions, extract necessary business operation nodes, and generate corresponding business processes according to the order of the necessary business operation nodes in the business process.

As an optional implementation manner, in the first aspect of the embodiments of the present invention, the business processes are abstracted and assembled to form the data domain of the target business.

As an optional implementation manner, in the first aspect of the embodiments of the present invention, the subject content of the target business is collected to obtain a business theme corresponding to the target business, and a subject field of the target business is generated.

As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:

A behavioral domain bus matrix of the target business is generated based on the data domain and the subject domain of the target business.

The second aspect of the embodiment of the present invention discloses a data warehouse construction device based on a commercial advertisement scene, including:

Business research module: used to conduct research on the target business to obtain the business processes corresponding to different target businesses;

Process acquisition module: used to determine the business event or business action in each target business according to the business process, so as to obtain the corresponding business process;

Warehouse creation module: used to establish a data warehouse for the target business, the data warehouse at least includes a dimension table, a detailed table and a summary table, the dimension table is used to unify the calculation algorithm of the target business and determine the associated table of the target business, the The detailed table is used to record the business process corresponding to each target business, and the summary table is used to record the subject field and data field of the target business.

As an optional implementation, in the second aspect of the embodiment of the present invention, the data warehouse includes ODS level, DW level, DMA level, DMT level and DA level, and the ODS level is the access layer of raw data , the DW level is used to store the business process of the target business, the DMA level is used to integrate and summarize data, the DMT level is used to summarize target business topics, and the DA level is used to respond to personalized data requirements .

As an optional implementation, in the second aspect of the embodiment of the present invention, the ODS level, DW level, DMA level, DMT level and DA level of the data warehouse are called according to preset rules.

As an optional implementation manner, in the second aspect of the embodiments of the present invention, determining the business event or business action in each target business according to the business process to obtain the corresponding business process includes:

Determine the corresponding business operation node of the target business according to the business process, and the business operation node includes business events and business actions;

Organize the business events and business actions, extract necessary business operation nodes, and generate corresponding business processes according to the order of the necessary business operation nodes in the business process.

As an optional implementation manner, in the second aspect of the embodiments of the present invention, the business processes are abstracted and assembled to form the data domain of the target business.

As an optional implementation manner, in the second aspect of the embodiments of the present invention, the subject content of the target business is collected to obtain the business theme corresponding to the target business, and the subject field of the target business is generated.

As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:

A behavioral domain bus matrix of the target business is generated based on the data domain and the subject domain of the target business.

The third aspect of the embodiments of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory , which is used to implement the commercial advertisement scene-based data warehouse construction method disclosed in the first aspect of the embodiment of the present invention.

The fourth aspect of the embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes the computer to execute the method for constructing a data warehouse based on a commercial advertisement scene disclosed in the first aspect of the embodiment of the present invention.

Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

The data warehouse construction method based on the commercial advertisement scene disclosed in the embodiment of the present invention fully considers the characteristics of mobile commercial advertisements such as messy data, large data volume, and chaotic formats, and establishes a data warehouse by obtaining the business process and corresponding business process accordingly , the embodiment can efficiently manage advertising service data.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is a schematic flow diagram of a data warehouse construction method based on a commercial advertisement scene disclosed in an embodiment of the present invention;

Fig. 2 is a schematic structural diagram of a data warehouse construction device based on a commercial advertisement scene provided by an embodiment of the present invention;

Fig. 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention;

FIG. 4 is a flow chart of hierarchical calls provided by the embodiment of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

It should be noted that the terms "first", "second", "third", and "fourth" in the specification and claims of the present invention are used to distinguish different objects, rather than to describe specific order. The terms "comprising" and "having" and any variations thereof in the embodiments of the present invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to Those steps or elements are not explicitly listed, but may include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.

The embodiment of the present invention discloses a data warehouse construction method, device, electronic equipment, and storage medium based on a commercial advertisement scene, and fully considers the characteristics of mobile commercial advertisement data messy, large data volume, and chaotic format. Corresponding business processes, and then establish a data warehouse, the embodiment can efficiently manage advertising business data.

Embodiment one

Please refer to FIG. 1 . FIG. 1 is a schematic flowchart of a method for constructing a data warehouse based on a commercial advertisement scenario disclosed in an embodiment of the present invention. Wherein, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software or/and hardware, and the execution subject can receive relevant information through wired or/and wireless means, and can send certain instructions. Of course, it may also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or a local host or server and related software that perform related operations on devices placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage device and the device can be placed in the same place or in a different place. As shown in Figure 1, the data warehouse construction method based on the commercial advertisement scenario includes the following steps:

101. Conduct research on target businesses to obtain business processes corresponding to different target businesses.

The embodiment can be divided into business research and demand research according to the business line situation, including building a big data data warehouse and two demand research approaches, and sorting out the overall business structure and overall data framework of the business. Among them, business research is the premise of building a big data data warehouse. It is necessary to understand the similarities and differences of the business of each business line, as well as which business modules each business line can be subdivided into, and what is the business process of each business module. There are two types of convex tendons in demand research, one is to understand the data demands with relevant staff, and the other is to conduct research and analysis on the existing reports in the report system. Based on the research results, the output research includes sorting out the overall business structure of the business line, the connection between various business modules and the flow of information flow; sorting out the overall data framework of the business line, the main business functions and data types in each business module.

102. Determine a business event or business action in each target business according to the business process, so as to obtain a corresponding business process.

The embodiment combines business line research reports to determine business modules/items and events or actions in each module, and abstracts business processes. For example, the commercial data warehouse business process includes exposure, request, click, download, billing, recharge, consumption, activation, retention, registration, login, installation, etc., and the business process corresponding to the project integrated into commercial advertising includes exposure , request, click, download, billing, recharge, consumption, activation, retention, registration, login, installation.

In the embodiment, this step is specifically to determine the business operation node of the corresponding target business according to the business process, the business operation node includes business events and business actions; organize the business events and business actions, extract the necessary business operation nodes, and follow the The order of the necessary business operation nodes in the business process generates a corresponding business process.

Wherein, the business process is abstracted and assembled to form the data domain of the target business. The main content of the target business is collected to obtain the business theme corresponding to the target business, and the subject field of the target business is generated.

The principle of dividing the data domain is to face the business data, and abstract the business process or dimension. It needs long-term maintenance, and it is not easy to change and modify frequently. The data domain must be scalable, and the new business can be expanded or added without affecting it. Abstract and integrate dimensions and metrics that are similar in business and compatible in granularity. Exemplarily, as shown in the following table, data domains are divided for the mobile commercial advertisement business line:

The principle of subject domain division is data application analysis, for specific business analysis subjects, such as commodity analysis, order analysis, data with certain relevance or similar business, highlighting the subject of analysis.

Exemplarily, the subject domain division of the mobile commercial advertisement business line is shown in the following table:

On this basis, the embodiment further includes generating a behavior domain bus matrix of the target business based on the data domain and the subject domain of the target business.

The embodiment specifies the business processes under each data domain, and then the bus matrix can be constructed. Clarify which dimensions the business process is related to, and define the business process and dimensions under each data domain.

As an example, the following table shows the behavior domain bus matrix of a business line:

103. Establish a data warehouse for the target business. The data warehouse includes at least a dimension table, a detailed table, and a summary table. The dimension table is used to unify the calculation algorithm of the target business and determine the associated table of the target business. The detailed table is used for The business process corresponding to each target business is recorded, and the summary table is used to record the subject domain and data domain of the target business.

In an embodiment, the data warehouse includes an ODS level, a DW level, a DMA level, a DMT level and a DA level, the ODS level is the access level of the original data, the DW level is used to store the business process of the target business, and the DMA level The level is used to integrate and summarize data, the DMT level is used to summarize target business topics, and the DA level is used to respond to personalized data requirements. Moreover, the ODS level, DW level, DMA level, DMT level and DA level of the data warehouse are called according to preset rules.

The creation of the data warehouse in the embodiment is also the creation of the model, or the creation of different tables. It mainly includes the standard definition of dimensions and attributes, and the model design of dimension tables, detailed fact tables and summary fact tables. Among them, the dimension table design is based on the concept of dimensional modeling, and the data dimension table is established to reduce the risk of inconsistent data calculation caliber and algorithm. Dimension table design is combined with business to determine the scope of use of dimension tables, complete the preliminary definition of dimensions, and ensure the consistency of dimensions. Determine the main dimension table. The main dimension table is usually an ODS table, which is directly synchronized with the business system, determines the related dimension tables, determines which tables are associated with the main dimension table, and selects some of the tables to generate dimension attributes to determine the dimension Attributes, select dimension attributes from the main dimension table and related dimension tables or generate new dimension attributes. The dimension table design principle of the embodiment is to give priority to the use of public dimension tables. Dimension table design considers reusability and consistency. Dimension attributes cover business data statistics, analysis, and exploration as much as possible. It may contain textual description fields to avoid updating the data of the dimension table too frequently.

As the core of the dimensional modeling of the data warehouse, the detailed table is designed around the business process. Combined with the business data situation, a fact table can be established for each business process, or a fact table can be created for multiple similar or similar business processes. Identifying a granularity for the business process determines the level of detail expressed by each row in the fact table. Ensure that all facts are documented with the same level of detail. If there is a field that can express this granularity, it can be defined as the primary key of the fact table. The finest level of granularity should be chosen as much as possible to ensure maximum flexibility in the application of the fact table. After the business process is selected and the granularity is determined, the dimension information can be determined, and the dimension information that can describe the business process clearly can be selected. After the business process is selected and the granularity is determined, the dimension information can be determined, and the dimension information that can describe the business process clearly can be selected. The fact table should contain all the facts related to the description of the business process, and the granularity of the facts should be consistent with the granularity of the determined fact table. Determine which related dimensions are needed for dimension redundancy. Store various types of commonly used dimension information in the fact table, reduce the operation of associating multiple tables when used by downstream users, reduce computing overhead, and improve usage efficiency. The design principle of the detailed table is to include all the facts related to the business process as much as possible, and only select the facts related to the business process. In the same fact table, facts of different granularities cannot be included. The granularity of all facts in the fact table needs to be consistent with the granularity of the table declaration, the unit of the fact must be consistent, and the null value of the fact must be handled uniformly.

The summary table takes the subject object of analysis as the modeling drive, and builds a public granularity summary table based on the upper-level application and product indicator requirements. Its design steps are to determine the subject domain/data domain of the summary, determine the dimension of the summary, and determine the fact of the summary. The design principle of the summary table is data commonality. Dimensions and facts cover relevant business use data scenarios as much as possible. Try not to store summary data of different granularities in the same table. If necessary, partition storage and model reusability can be used. Cover as many downstream use data scenarios as possible, and the scope of index processing does not include composite indicators as much as possible.

In the embodiment, it also includes layer calls. See Figure 4. The preset rules can include that the depth of the DW layer is not greater than 2; the depth of the DMA layer is not greater than 2; the depth of the DMT layer is not greater than 1. Layer reflow calls are not allowed, and the application layer calls preferentially The DMA/DMT data mart summary layer already has DMA/DMT layer data, and the application layer is not allowed to repeatedly process data from the ODS/DW layer. The public layer team should actively understand the construction requirements of the application layer data, deposit the public data to the DM layer, and provide data services for other teams. The application layer team also needs to actively cooperate with the public layer team to carry out continuous transformation and migration of DM layer construction. Excessive ODS layer references and unreasonable data duplication and sub-collection redundancy must be avoided.

And there is also dimension reduction processing. Dimension reduction refers to degenerating the common attributes of each dimension into the fact table in the physical implementation of the model, so as to greatly improve the efficiency of filtering query, statistical aggregation and other operations on the fact table. The downstream hierarchical model uses Dimension attribute data sinking is carried out in the model of this layer. Here, it refers to the sinking of dimension attributes in the DW/DMA/DMT/DA layer model, and sinks the dimension attributes from the upper layer to the 1-n layer model table. Among them, the DW layer dimensionality reduction is to sink the conventional and stable dimensions of the downstream DMA/DMT/DA layer into this layer for storage, which is convenient to use and reduces repeated associated dimension tables. It is recommended to retreat to this layer. Dimensionality reduction in the DMT layer is to return the dimension attributes of the downstream DA layer to this layer, and sink the dimensions that can be used in association to this layer as much as possible, so as to solve the problem of variable dimensions and apply them flexibly. DIM dimensionality reduction is to flatten the dimension table Processing, dimension horizontalization, and flattening are to put all the dimensions that can be integrated into a model table in the form of fields, including variable dimensions.

Exemplary, ad exposure, click, billing table comm_dw.dw_ssp_expo_click_hi, ad (dim_ad_marketing_ad_info_hf), ad slot (comm_dim.dim_ad_pst_info_h), creative (comm_dim.dim_adad_info_h), ad group (dim_ad_marketing_ad_group_info_ hf), plan (dim_ad_plan_info_hf), advertiser (dim_ad_advertiser_info_h)'s stable dimension attributes (media ID, advertising payment form, ad group ID, OCPC conversion goal, plan ID, advertising promotion form, ad slot type, etc.) degenerate into a detailed table.

Embodiment two

Please refer to FIG. 2 . FIG. 2 is a schematic structural diagram of a data warehouse construction device based on a commercial advertisement scene disclosed in an embodiment of the present invention. As shown in Figure 2, the data warehouse construction device based on the commercial advertisement scene may include: a business research module 201, a process acquisition module 202, and a warehouse creation module 203, wherein the business research module 201: is used to conduct research on the target business to obtain Business processes corresponding to different target businesses; process acquisition module 202: used to determine business events or business actions in each target business according to the business process, so as to obtain corresponding business processes; warehouse creation module 203: used to establish The data warehouse of the target business, the data warehouse includes at least a dimension table, a detailed table and a summary table, the dimension table is used to unify the calculation algorithm of the target business and determine the associated table of the target business, and the detailed table is used to record each The business process corresponding to the target business, the summary table is used to record the subject domain and data domain of the target business.

In an embodiment, the data warehouse includes an ODS level, a DW level, a DMA level, a DMT level and a DA level, the ODS level is the access level of the original data, the DW level is used to store the business process of the target business, and the DMA level The level is used to integrate and summarize data, the DMT level is used to summarize target business topics, and the DA level is used to respond to personalized data requirements. The ODS level, DW level, DMA level, DMT level and DA level of the data warehouse are called according to preset rules.

The technical means and technical effects of this embodiment are essentially the same as those of Embodiment 1, and will not be repeated here.

Embodiment three

Please refer to FIG. 3 . FIG. 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present invention. Electronic equipment can be computers and servers, etc. Of course, under certain circumstances, it can also be smart equipment such as mobile phones, tablet computers, and monitoring terminals, as well as image acquisition devices with processing functions. As shown in Figure 3, the electronic equipment may include:

A memory 301 storing executable program codes;

a processor 302 coupled to the memory 301;

Wherein, the processor 302 invokes the executable program code stored in the memory 301 to execute some or all of the steps in the method for constructing a data warehouse based on a commercial advertisement scene in Embodiment 1.

The embodiment of the present invention discloses a computer-readable storage medium, which stores a computer program, wherein the computer program causes the computer to execute some or all of the steps in the method for constructing a data warehouse based on a commercial advertisement scene in Embodiment 1.

The embodiment of the present invention also discloses a computer program product, wherein when the computer program product is run on a computer, the computer is made to execute some or all of the steps in the method for constructing a data warehouse based on a commercial advertisement scene in the first embodiment.

The embodiment of the present invention also discloses an application distribution platform, wherein the application distribution platform is used to distribute computer program products, wherein, when the computer program products run on the computer, the computer executes the data based on the commercial advertisement scene in the first embodiment Some or all of the steps in a warehouse build method.

In various embodiments of the present invention, it should be understood that the sequence numbers of the processes described above do not imply the necessary order of execution, and the execution order of each process should be determined by its functions and internal logic, rather than by the present invention. The implementation of the examples constitutes no limitation.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, located in one place, or distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a memory , including several requests to make a computer device (which may be a personal computer, a server, or a network device, etc., specifically, a processor in the computer device) execute part or all of the steps of the method described in each embodiment of the present invention.

In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B based on A does not mean determining B only based on A, and B can also be determined based on A and/or other information.

Those skilled in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes only Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-only Memory (PROM), Erasable Programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), CD-ROM ( CompactDisc Read-Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other computer-readable medium that can be used to carry or store data.

The above is a detailed introduction to the data warehouse construction method, device, electronic equipment and storage medium based on the commercial advertisement scene disclosed in the embodiment of the present invention. In this paper, specific examples are used to illustrate the principle and implementation of the present invention. The above embodiments The description is only used to help understand the method of the present invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation and scope of application. In summary, As stated above, the content of this specification should not be construed as limiting the present invention.

Claims (6)

1. A method of building a data warehouse based on commercial scenes, comprising:

investigation is carried out on the target business to obtain business processes respectively corresponding to different target businesses;

determining a service operation node of a corresponding target service according to a service flow, wherein the service operation node comprises a service event and a service action; the business event and the business action are tidied, necessary business operation nodes are extracted, and corresponding business processes are generated according to the sequence of the necessary business operation nodes in the business process;

establishing a data warehouse of a target service, wherein the data warehouse at least comprises a dimension table, a detail table and a summary table, the dimension table is used for unifying a calculation algorithm of the target service and determining an associated table of the target service, the detail table is used for recording a service process corresponding to each target service, the summary table is used for recording a theme domain and a data domain of the target service, the data warehouse comprises an ODS (open system) layer, a DW (direct system) layer, a DMA (direct system) layer and a DA (direct system) layer, the ODS layer is an access layer of original data, the DW layer is used for storing the service process of the target service, the DMA layer is used for fusion summarizing the data, the DMT layer is used for summarizing a target service theme, the DA layer is used for responding to personalized data requirements, call is carried out among the ODS layer, the DW layer, the DMA layer, the DMT layer and the DA layer of the data warehouse according to preset rules, and the service process is abstracted to form a data domain of the target service;

the preset rules include that the depth of the DW hierarchy is not greater than a first set value, the DMA hierarchy depth is not greater than a second set value, the DMT hierarchy depth is not greater than a third set value, hierarchy reflow calls are not allowed, and an application layer is not allowed to repetitively process data from the ODS/DW hierarchy in the presence of DMA/DMT hierarchy data already.

2. The data warehouse construction method according to claim 1, wherein the subject content of the target service is collected to obtain a service theme corresponding to the target service, and a theme zone of the target service is generated.

3. The data warehouse construction method as claimed in claim 2, further comprising:

and generating a behavior domain bus matrix of the target service based on the data domain and the theme domain of the target service.

4. A data warehouse construction apparatus based on a commercial scene, comprising:

and a service investigation module: the method is used for researching the target business to obtain business processes respectively corresponding to different target businesses;

the process acquisition module: the service operation node is used for determining a corresponding target service according to the service flow, and comprises a service event and a service action; the business event and the business action are tidied, necessary business operation nodes are extracted, and corresponding business processes are generated according to the sequence of the necessary business operation nodes in the business process;

and a warehouse creation module: the data warehouse is used for establishing a target service and comprises at least a dimension table, a detail table and a summary table, wherein the dimension table is used for unifying a calculation algorithm of the target service and determining an association table of the target service, the detail table is used for recording a service process corresponding to each target service, the summary table is used for recording a theme domain and a data domain of the target service, the data warehouse comprises an ODS (open system) layer, a DW (direct system) layer, a DMT (direct system) layer and a DA (direct system) layer, the ODS layer is an access layer of original data, the DW layer is used for storing the service process of the target service, the DMA layer is used for fusion summary of the data, the DMT layer is used for summarizing the target service theme, the DA layer is used for responding to personalized data requirements, call is carried out among the ODS layer, the DW layer, the DMA layer, the DMT layer and the DA layer of the data warehouse according to preset rules, and the service process is abstracted to form the data domain of the target service;

the preset rules include that the depth of the DW hierarchy is not greater than a first set value, the DMA hierarchy depth is not greater than a second set value, the DMT hierarchy depth is not greater than a third set value, hierarchy reflow calls are not allowed, and an application layer is not allowed to repetitively process data from the ODS/DW hierarchy in the presence of DMA/DMT hierarchy data already.

5. An electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor invokes the executable program code stored in the memory for performing the commercial scene based data warehouse construction method of any of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the data warehouse construction method based on a commercial scene as claimed in any one of claims 1 to 3.

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