CN115422169A - Data warehouse construction method and device based on commercial scene - Google Patents
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Abstract
The embodiment of the invention discloses a data warehouse construction method based on commercial scenes, which comprises the following steps: the target business is investigated to obtain business processes corresponding to different target businesses respectively; determining a service event or a service action in each target service according to the service flow so as to obtain a corresponding service process; and establishing a data warehouse of the 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 the calculation algorithm of the target service and determining the association table of the target service, the detail table is used for recording the service process corresponding to each target service, and the summary table is used for recording the subject field and the data field of the target service. The characteristics of messy mobile commercial advertisement data, large data volume, disordered format and the like are fully considered, and the advertisement business data can be efficiently managed by acquiring the business process, acquiring the corresponding business process according to the business process and then establishing the data warehouse.
Description
Technical Field
The invention relates to the technical field of computer data management, in particular to a data warehouse construction method and device based on commercial advertising scenes.
Background
At present, with the rapid development of the mobile internet, a new thing, mobile advertisement, comes to the fore, and it is popular with advertisers with the advantages of precision, instantaneity, interactivity, diffusivity, integration and testability, so as to be rapidly developed. Meanwhile, a mobile advertisement platform has been generated, which is a platform or an intermediary connecting an application developer and an advertiser. On the platform, a developer provides an application, an advertiser provides an advertisement, and the mobile advertisement platform provides the SDK of the corresponding mobile phone system. The developer downloads the SDK, the advertisement can be embedded into the application by using a tool in the SDK, the application is uploaded to the mobile Internet through other channels, the application is downloaded by the end user, and after the advertisement is browsed or clicked, the advertiser pays the developer according to a corresponding charging mode.
The data analysis part mainly analyzes data generated in the operation of the existing mobile advertisement platform and generates reports for relevant personnel to view. The method mainly comprises three aspects according to different personnel viewing report data, namely, report statistics from the perspective of developers, and for the developers, the developers mainly want to know the income of each application put on a platform every day; secondly, report statistics is made from the perspective of advertisers, and for the advertisers, the advertisers mainly want to know how many times the advertisements are displayed on the platform and how many times the advertisements are clicked every day, so that the advertisers pay for how much of the advertisements are; and thirdly, report statistics is made from the perspective of decision makers, and for the decision makers of companies, the decision makers mainly concern how many advertisers and developers are added to the platform every day, how many active applications are used every day, how many advertisements are shown and clicked every day, which type of advertisement is clicked most, which type of application has the most users, and the like. It can be seen that the system needs to perform multi-level analysis on these large amounts of heterogeneous data from different dimensions for different users, and if a data warehouse is used, the conventional data warehouse is facing new challenges of information explosion with the rapid growth of data. Such huge data is very time consuming to analyze purely by traditional data warehouse architectures and it is difficult to manage the data efficiently.
Disclosure of Invention
Aiming at the defects, the embodiment of the invention discloses a data warehouse construction method and a data warehouse construction device based on commercial advertising scenes, which can efficiently manage huge and complex advertising business data.
The embodiment of the invention discloses a data warehouse construction method based on commercial scenes in a first aspect, which comprises the following steps:
the target business is investigated to obtain business processes corresponding to different target businesses respectively;
determining a service event or a service action in each target service according to the service flow so as to obtain a corresponding service process;
and establishing a data warehouse of the 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 the calculation algorithm of the target service and determining the association table of the target service, the detail table is used for recording the service process corresponding to each target service, and the summary table is used for recording the subject field and the data field of the target service.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the data warehouse includes an ODS layer, a DW layer, a DMA layer, a DMT layer and a DA layer, the ODS layer is an access layer to raw data, the DW layer is used for storing business processes of target businesses, the DMA layer is used for fusing and summarizing data, the DMT layer is used for summarizing target business topics, and the DA layer is used for responding to personalized data requirements.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the invocation is performed between the ODS level, the DW level, the DMA level, the DMT level, and the DA level of the data warehouse according to a preset rule.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the determining, according to the business process, a business event or a business action in each target business to obtain a corresponding business process includes:
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;
and sorting the service events and the service actions, extracting necessary service operation nodes, and generating corresponding service processes according to the sequence of the necessary service operation nodes in the service flow.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the business processes are abstracted and aggregated to form a data field of a target business.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the main content of the target service is collected to obtain a service topic corresponding to the target service, and a topic domain of the target service is generated.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the method further includes:
and generating a behavior domain bus matrix of the target business based on the data domain and the subject domain of the target business.
A second aspect of the embodiments of the present invention discloses a data warehouse construction apparatus based on a commercial scene, including:
a business investigation module: the system is used for investigating the target business to obtain business processes corresponding to different target businesses respectively;
a process acquisition module: the system comprises a service process, a service processing unit and a service processing unit, wherein the service process is used for determining a service event or a service action in each target service according to the service process so as to acquire a corresponding service process;
a repository creation module: the data warehouse is used for establishing target services, and comprises at least a dimension table, a detail table and a summary table, wherein the dimension table is used for unifying calculation algorithms of the target services and determining an association 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.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the data warehouse includes an ODS layer, a DW layer, a DMA layer, a DMT layer and a DA layer, the ODS layer is an access layer of raw data, the DW layer is used for storing business processes of target businesses, the DMA layer is used for performing fusion aggregation on data, the DMT layer is used for performing aggregation on target business topics, and the DA layer is used for responding to personalized data requirements.
As an alternative implementation manner, in the second aspect of the embodiment of the present invention, the calls between the ODS level, the DW level, the DMA level, the DMT level, and the DA level of the data warehouse are performed according to a preset rule.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the determining, according to the business process, a business event or a business action in each target business to obtain a corresponding business process includes:
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;
and sorting the service events and the service actions, extracting necessary service operation nodes, and generating corresponding service processes according to the sequence of the necessary service operation nodes in the service flow.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the business processes are abstracted and aggregated to form a data field of the target business.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the main content of the target service is collected to obtain the service theme corresponding to the target service, and the theme domain of the target service is generated.
As an optional implementation manner, in the second aspect of the embodiment of the present invention, the method further includes:
and generating a behavior domain bus matrix of the target business based on the data domain and the subject domain of the target business.
A third aspect of an embodiment of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory for executing the commercial scene-based data warehouse construction method disclosed in the first aspect of the embodiment of the present invention.
A fourth aspect of the embodiments of the present invention discloses a computer-readable storage medium storing a computer program, where the computer program enables a computer to execute the data warehouse construction method based on commercial scenes disclosed in the first aspect of the embodiments of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the data warehouse construction method based on the commercial scene disclosed by the embodiment of the invention fully considers the characteristics of messy mobile commercial data, large data volume, disordered format and the like, and can efficiently manage the commercial data by acquiring the business process, acquiring the corresponding business process and then establishing the data warehouse.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram of a commercial scenario-based data warehouse construction method disclosed in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a data warehouse building apparatus based on a commercial scenario according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 4 is a flow chart of a hierarchical call provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", "third", "fourth", and the like in the description and the claims of the present invention are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a data warehouse construction method, a device, electronic equipment and a storage medium based on commercial scenes, fully considers the characteristics of messy mobile commercial data, large data volume, disordered format and the like, acquires a business process according to the business process, and then establishes a data warehouse, so that the embodiment can efficiently manage the commercial data.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a data warehouse building method based on a commercial scenario according to an embodiment of the present invention. The execution main body of the method described in the embodiment of the present invention is an execution main body composed of software or/and hardware, and the execution main body may receive related information in a wired or/and wireless manner and may send a certain instruction. Of course, it may also have certain processing and storage functions. The execution body may control a plurality of devices, such as a remote physical server or a cloud server and related software, or may be a local host or a server and related software for performing related operations on a device installed somewhere. In some scenarios, multiple storage devices may also be controlled, which may be co-located with the device or located in a different location. As shown in fig. 1, the commercial scenario-based data warehouse construction method includes the following steps:
101. and (4) investigating the target services to obtain service flows corresponding to different target services respectively.
The embodiment can be divided into service research and demand research according to the condition of a service line, comprises the steps of constructing a data warehouse of big data and two demand research approaches, and combs out an integral service framework and an integral data framework of the service. The business investigation is a precondition for constructing a big data warehouse, and needs to know what business of each business line is different and identical, and what business modules each business line can be subdivided into, and what business process each business module has. The convex ribs required to be investigated include two types, namely, the convex ribs can be used for knowing the data requirements with related workers, and the convex ribs can be used for researching and analyzing the existing report in the report system. Outputting the investigation based on the investigation result, wherein the investigation comprises the steps of combing out the whole business architecture of the business line, the connection among all the business modules and the flow of information; and (4) combing out the whole data frame of the service line, and the main service functions and data types in each service module.
102. And determining a business event or a business action in each target business according to the business process so as to obtain a corresponding business process.
Embodiments abstract the business process by determining business modules/projects and events or actions in each module in conjunction with the business line research reports. For example, the commercial warehouse business process includes exposure, request, click, download, charging, recharge, consumption, activation, retention, registration, login, installation, etc., and the business process corresponding to the item integrated as the commercial advertisement includes exposure, request, click, download, charging, recharge, consumption, activation, retention, registration, login, installation, etc.
In the embodiment, the step is to determine 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; and sorting the service events and the service actions, extracting necessary service operation nodes, and generating corresponding service processes according to the sequence of the necessary service operation nodes in the service flow.
And abstracting and gathering the business processes to form a data domain of the target business. And acquiring the main content of the target service to obtain a service theme corresponding to the target service, and generating a theme domain of the target service.
The division principle of the data domain is oriented to service data, the service process or the dimensionality is abstracted geometrically, long-term maintenance is needed, transformation and frequent modification are not easy, the data domain must have expansibility, newly added services can not affect expansion or addition, and dimensionality and measurement values which are close in service and compatible in granularity are abstracted and integrated. Illustratively, the data domain partitioning is performed for the mobile commercial traffic line as shown in the following table:
the dividing principle of the theme domain is data application analysis oriented, specific business analysis subjects such as commodity analysis and order analysis are targeted, data have certain correlation or business is similar, and the analyzed theme is highlighted.
Illustratively, the subject domain partitioning of the mobile commercial traffic line is shown as follows:
on this basis, the embodiment further comprises generating a behavior domain bus matrix of the target service based on the data domain and the subject domain of the target service.
After determining which business processes exist in each data field, the embodiment can construct a bus matrix. It is clear which dimensions the business process is related to and defines the business process and dimensions under each data field.
Illustratively, the following table shows a row domain bus matrix for a particular line of service:
103. and establishing a data warehouse of the 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 the calculation algorithm of the target service and determining the association table of the target service, the detail table is used for recording the service process corresponding to each target service, and the summary table is used for recording the subject field and the data field of the target service.
In an embodiment, the data warehouse comprises an ODS layer, a DW layer, a DMA layer, a DMT layer and a DA layer, wherein the ODS layer is an access layer of original data, the DW layer is used for storing business processes of target businesses, the DMA layer is used for fusing and summarizing data, the DMT layer is used for summarizing target business topics, and the DA layer is used for responding to personalized data requirements. And the ODS level, the DW level, the DMA level, the DMT level and the DA level of the data warehouse are called according to a preset rule.
Creating a data warehouse of an embodiment is to create a model, or to create a different table. The method mainly comprises the specification definition of dimensions and attributes, and the model design of a dimension table, a detailed fact table and a summary fact table. The dimension table design is based on a dimension modeling concept, and a data dimension table is established so as to reduce the risk of non-uniformity of data calculation caliber and algorithm. And the dimension table design is combined with the service, the use range of the dimension table is determined, the preliminary definition of the dimension is completed, and the consistency of the dimension is ensured. Determining a main dimension table, wherein the main dimension table is usually an ODS table, directly synchronizing with a service system, determining related dimension tables, determining which tables and main dimension tables have association relations, selecting some tables for generating dimension attributes, determining dimension attributes, and selecting dimension attributes or generating new dimension attributes from the main dimension table and the related dimension tables. The dimension table design principle of the embodiment is that a public dimension table is preferentially used, the dimension table design considers the multiplexing and consistency, the dimension attributes cover the requirements of data statistics, analysis, exploration and the like of the service as much as possible, the dimension attributes except the coding fields also contain the literal description fields as much as possible, and the data of the dimension table is prevented from being updated too frequently.
The detail list serves as the core of the dimensional modeling of the data warehouse and is designed tightly around the business process. In combination with the service data situation, a fact table may be established for each service process, or a fact table may be established for a plurality of similar or similar service processes. Determining a granularity for a business process determines the level of detail expressed by each row in the fact table. Ensuring that all facts are recorded at the same level of detail. If there are fields 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 the greatest flexibility in the application of fact tables. After the business process is selected and the granularity is determined, dimension information can be determined, and dimension information capable of describing the clear business process is selected. After the business process is selected and the granularity is determined, dimension information can be determined, and dimension information capable of describing the clear business process is selected. The fact table should contain all facts relevant to the business process description and the granularity of the facts should be consistent with the determined granularity of the fact table. And determining which relevant dimensions are needed for dimension redundancy. Various types of common dimension information are stored in the fact table, so that the operation of associating a plurality of tables when a downstream user uses the table is reduced, the calculation overhead is reduced, and the use efficiency is improved. The design principle of the detail list is to contain all the facts related to the business process as much as possible, only select the facts related to the business process, and the facts of different granularities cannot be contained in the same fact list. The granularity of all facts in a fact table needs to be consistent with the granularity stated by the table, the units of the facts need to be consistent, and the null values of the facts need to be uniformly processed.
And the summary table takes the analyzed subject object as a modeling drive, and constructs the summary table of the common granularity based on the upper application and the index requirement of the product. The design steps are to determine the subject/data fields of the summary, determine the dimensions of the summary, and determine the facts of the summary. The design principle of the summary table is that data commonality, dimensionality and fact cover the scene of related service use data as much as possible, the summary data with different granularities are not stored in the same table as much as possible, if necessary, the scene of downstream use data is covered as much as possible by using partition storage and model reusability, and the index processing range does not contain compound indexes as much as possible.
In an embodiment, the invoking of the hierarchy is further included, and referring to fig. 4, the preset rule may include that the DW layer depth is not greater than 2; the DMA layer depth is not more than 2; the depth of the DMT layer is not larger than 1, hierarchical reflow calling is not allowed, the application layer calls the DMA/DMT data mart summary layer preferentially, the DMA/DMT layer data already exists, and the application layer is not allowed to repeatedly process data from the ODS/DW layer. The public layer team should actively know the construction requirements of the application layer data, and deposit public data to the DM layer to provide data services for other teams. The application-level team also needs to actively cooperate with the common-level team to perform continuous DM-level construction modification and migration. Excessive ODS layer references and unreasonable data duplication and subset redundancy must be avoided.
And dimension reduction processing is also carried out, wherein dimension reduction refers to that common attributes of all dimensions are degraded into a fact table in model physical implementation so as to greatly improve the efficiency of operations such as filtering query, statistical aggregation and the like on the fact table, dimension attribute data used by a downstream level model sinks in a model layer, and the dimension attribute data used by the downstream level model sinks in a DW/DMA/DMT/DA layer model to sink the dimension attributes from an upper level to a 1-n level model table. The DW layer dimensionality reduction is to sink the conventional and stable dimensionality of a downstream DMA/DMT/DA layer to the layer for storage, so that the DW layer dimensionality reduction is convenient to use, a repeated association dimension table is reduced, the data backtracking calculation cost factor needs to be considered, and the variable dimensionality is not recommended to fall back to the layer. DMT layer dimensionality reduction is to fall back to this layer with the dimension attribute on downstream DA layer, will be related to the dimension of using and sink to this layer as far as possible, solves the easily changeable dimension problem, and nimble application, DIM dimensionality reduction is to do the flattening with the dimension table and handles, and the dimension is violently put into a model table with the form of field all with the dimension that can integrate, contains the easily changeable dimension.
Illustratively, the ad exposure, click, and billing table comm _ dw.dw _ ssp _ pop _ click _ hi, degrades the ad (dim _ ad _ marking _ ad _ info _ hf), the ad spot (dim _ dim.dim _ ad _ pst _ info _ h), the creative (com _ dim.dim _ ad _ info _ h), the ad group (dim _ ad _ marking _ ad _ group _ info _ hf), the plan (dim _ ad _ play _ info _ hf), and the stable dimension attributes (media ID, ad pay form, ad group ID, OCPC conversion target, plan ID, ad form, ad spot type, etc.) of the advertiser (dim _ ad _ advertisement _ info _ h) to the detail table.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a data warehouse building apparatus based on a commercial scenario according to an embodiment of the present invention. As shown in fig. 2, the commercial scenario-based data warehouse construction apparatus may include: a business investigation module 201, a process acquisition module 202, and a repository creation module 203, wherein the business investigation module 201: the system is used for investigating the target business to obtain business processes corresponding to different target businesses respectively; the process acquisition module 202: the system comprises a service process, a service processing unit and a service processing unit, wherein the service process is used for determining a service event or a service action in each target service according to the service process so as to acquire a corresponding service process; the repository creation module 203: the data warehouse is used for establishing target services, and comprises at least a dimension table, a detail table and a summary table, wherein the dimension table is used for unifying calculation algorithms of the target services and determining an association 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.
In an embodiment, the data warehouse comprises an ODS layer, a DW layer, a DMA layer, a DMT layer and a DA layer, wherein the ODS layer is an access layer of original data, the DW layer is used for storing business processes of target businesses, the DMA layer is used for fusing and summarizing data, the DMT layer is used for summarizing target business topics, and the DA layer is used for responding to personalized data requirements. And calling among the ODS level, the DW level, the DMA level, the DMT level and the DA level of the data warehouse according to preset rules.
The technical means and technical effects of this embodiment are substantially the same as those of the first embodiment, and are not described herein again.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. The electronic device may be a computer, a server, or the like, and may also be an intelligent device such as a mobile phone, a tablet computer, a monitoring terminal, or the like, and an image acquisition device having a processing function. As shown in fig. 3, the electronic device may include:
a memory 301 storing executable program code;
a processor 302 coupled to the memory 301;
the processor 302 calls the executable program code stored in the memory 301 to execute some or all of the steps in the commercial scenario-based data warehouse building method in the first embodiment.
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program, wherein the computer program enables a computer to execute part or all of the steps in the data warehouse construction method based on commercial scenes in the first embodiment.
The embodiment of the invention also discloses a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the data warehouse construction method based on the commercial scene in the first embodiment.
The embodiment of the invention also discloses an application publishing platform, wherein the application publishing platform is used for publishing the computer program product, and when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the data warehouse construction method based on the commercial advertisement scene in the first embodiment.
In various embodiments of the present invention, it should be understood that the sequence numbers of the processes do not imply a necessary order of execution, and the order of execution of the processes should be determined by functions and internal logics of the processes, and should not limit the implementation processes of the embodiments of the present invention in any way.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units, if implemented as software functional units and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the method according to the embodiments of the present invention.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that some or all of the steps in the methods of the embodiments described herein may be implemented by hardware associated with a program that may be stored in a computer-readable storage medium, including a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable Programmable Read-Only Memory (EEPROM), an optical Disc-Read-Only Memory (CD-ROM) or other storage medium capable of storing data, a magnetic tape, or any other computer-readable medium capable of carrying a computer program or computer-readable data.
The data warehouse construction method, the data warehouse construction device, the electronic equipment and the storage medium based on the commercial advertising scene disclosed by the embodiment of the invention are introduced in detail, a specific embodiment is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A data warehouse construction method based on commercial scenes is characterized by comprising the following steps:
the target business is investigated to obtain business processes corresponding to different target businesses respectively;
determining a service event or a service action in each target service according to the service flow so as to obtain a corresponding service process;
and establishing a data warehouse of the 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 the calculation algorithm of the target service and determining the association table of the target service, the detail table is used for recording the service process corresponding to each target service, and the summary table is used for recording the subject field and the data field of the target service.
2. The method according to claim 1, wherein the data warehouse comprises an ODS layer, a DW layer, a DMA layer, a DMT layer and a DA layer, the ODS layer is an access layer of raw data, the DW layer is used for storing business processes of target businesses, the DMA layer is used for fusing and summarizing data, the DMT layer is used for summarizing target business topics, and the DA layer is used for responding to personalized data requirements.
3. The data warehouse building method according to claim 2, wherein calls are made between the ODS level, the DW level, the DMA level, the DMT level, and the DA level of the data warehouse according to a preset rule.
4. The data warehouse building method according to claim 1, wherein the determining a business event or a business action in each target business according to the business process to obtain a corresponding business process comprises:
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;
and sorting the service events and the service actions, extracting necessary service operation nodes, and generating corresponding service processes according to the sequence of the necessary service operation nodes in the service flow.
5. The data warehouse construction method of claim 4, wherein the business processes are abstractly aggregated to form data fields of a target business.
6. The data warehouse construction method according to claim 5, wherein the subject content of the target business is collected to obtain a business topic corresponding to the target business, and a topic domain of the target business is generated.
7. The data warehouse construction method of claim 6, further comprising:
and generating a behavior domain bus matrix of the target business based on the data domain and the subject domain of the target business.
8. A data warehouse building apparatus based on commercial scenes, comprising:
a business investigation module: the system is used for investigating the target business to obtain business processes corresponding to different target businesses respectively;
a process acquisition module: the system comprises a service process, a service processing unit and a service processing unit, wherein the service process is used for determining a service event or a service action in each target service according to the service process so as to acquire a corresponding service process;
a repository creation module: the data warehouse is used for establishing target services and at least comprises a dimension table, a detail table and a summary table, wherein the dimension table is used for unifying calculation algorithms of the target services and determining association tables of the target services, the detail table is used for recording service processes corresponding to each target service, and the summary table is used for recording subject domains and data domains of the target services.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory for executing the commercial scenario based data warehouse construction method of any of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the commercial scenario-based data warehouse construction method according to any one of claims 1 to 7.
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