CN114265887A - Dimension data processing method and device, storage medium and electronic equipment - Google Patents
Dimension data processing method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN114265887A CN114265887A CN202111678356.7A CN202111678356A CN114265887A CN 114265887 A CN114265887 A CN 114265887A CN 202111678356 A CN202111678356 A CN 202111678356A CN 114265887 A CN114265887 A CN 114265887A
- Authority
- CN
- China
- Prior art keywords
- data
- dimension
- original
- classification
- query request
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The application discloses a dimension data processing method, a dimension data processing device, a storage medium and electronic equipment. Based on the above, according to the dimension types such as dates and clients, the original dimension data in each fact table is collected to obtain the data dimension table, so that other useless data are reduced, the load of the database is reduced, the data dimension table does not need to be collected again, and the query efficiency is improved when index data in the data dimension table is queried.
Description
Technical Field
The present application relates to the field of dimensional data processing technologies, and in particular, to a dimensional data processing method and apparatus, a storage medium, and an electronic device.
Background
Customer Relationship Management (CRM) systems are widely used in various futures companies, and the CRM systems undertake data query and data analysis.
Some companies have used the CRM system for more than ten years and have a large number of customers, and as time goes on, the historical data stored in the database in the CRM system is more and more, and when the historical data reaches a certain amount, the query efficiency is affected, and a large burden is imposed on the database in the data query process.
Therefore, the existing data query through the database of the CRM system is inefficient.
Disclosure of Invention
In view of this, the present application discloses a dimension data processing method, device, storage medium and electronic device, which aim to reduce the burden of a database and improve the query efficiency when querying index data in a data dimension table.
In order to achieve the purpose, the technical scheme is as follows:
the application discloses a dimension data processing method in a first aspect, and the method comprises the following steps:
classifying the obtained original dimensional data through a pre-constructed dimensional data model to obtain a classification result; the original dimension data are dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data;
summarizing the classification result to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after classification and collection;
and when a query request is received, acquiring index data corresponding to the query request in the data dimension table.
Preferably, the classifying the acquired original dimensional data through a pre-constructed dimensional data model to obtain a classification result includes:
acquiring original dimension data from each fact table;
determining each dimension type corresponding to the original dimension data;
and classifying the dimension types through a pre-constructed dimension data model to obtain a classification result.
Preferably, the process of building the dimensional data model includes:
and constructing a dimension data model by a normal form modeling method and a dimension modeling method.
Preferably, the collecting the classification results to obtain the data dimension table includes:
determining a summary dimension of the classification result through a dimension data index layer of the dimension data model;
and summarizing the classification results through the summarizing dimension to obtain a data dimension table.
Preferably, when receiving a query request, the obtaining of index data corresponding to the query request in the data dimension table includes:
when a query request is received, analyzing the query request to obtain query dimension data;
matching the query dimension data with preset dimension data in the data dimension table;
and if the query dimension data are consistent with the preset dimension data in the data dimension table, obtaining index data corresponding to the query request in the data dimension table.
Preferably, the method further comprises the following steps:
configuring the data dimension table;
the process of configuring the data dimension table is as follows:
acquiring each service requirement; the business requirements are requirements corresponding to business types of all companies;
extracting preset service indexes from the service requirements; the preset service index is a service index with the same service type under different dimensionalities;
and configuring the preset service index into the data dimension table.
The second aspect of the present application discloses a device for processing dimension data, wherein the method includes:
the classification unit is used for classifying the acquired original dimensional data through a pre-constructed dimensional data model to obtain a classification result; the original dimension data are dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data;
the summarizing unit is used for summarizing the classification result to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after classification and collection;
and the acquisition unit is used for acquiring index data corresponding to the query request in the data dimension table when the query request is received.
Preferably, the classification unit includes:
the first acquisition module is used for acquiring original dimension data from each fact table;
the first determining module is used for determining each dimension type corresponding to the original dimension data;
and the classification module is used for classifying the dimension types through a pre-constructed dimension data model to obtain a classification result.
A third aspect of the present application discloses a storage medium, where the storage medium includes stored instructions, where when the instructions are executed, a device in which the storage medium is located is controlled to execute the dimensional data processing method according to any one of the first aspect.
A fourth aspect of the present application discloses an electronic device, comprising a memory, and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by the one or more processors to perform the method for processing dimension data according to any one of the first aspect.
According to the technical scheme, the obtained original dimension data are classified through a pre-constructed dimension data model to obtain classification results, the original dimension data are dimension data before being classified, the classification results are used for representing results obtained after the dimension types of the original dimension data are classified, the classification results are summarized to obtain a data dimension table, the data dimension table is a data table of the dimensions after being classified and summarized, and when a query request is received, if the query request meets preset query conditions, index data corresponding to the query request in the data dimension table are obtained. According to the scheme, the original dimension data in each fact table are collected to obtain the data dimension table according to the dimension types such as dates and clients, other useless data are reduced, the load of the database is reduced, the data dimension table does not need to be collected again, and the query efficiency is improved when index data in the data dimension table are queried.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flowchart of a dimensional data processing method disclosed in an embodiment of the present application;
FIG. 2 is a schematic classification diagram of a dimensional data model disclosed in an embodiment of the present application;
FIG. 3 is a diagram illustrating association between fact tables and dimension tables, as disclosed in an embodiment of the present application;
FIG. 4 is a schematic diagram of a summarized dimension disclosed in embodiments of the present application;
fig. 5 is a schematic diagram illustrating configuration of fields in each index data by formula configuration disclosed in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a dimensional data processing apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As known from the background art, some companies have used the CRM system for more than ten years, and have a large number of customers, and as time goes on, the historical data stored in the database in the CRM system is more and more, and when the historical data reaches a certain amount, the query efficiency is affected, and a large burden is imposed on the database in the data query process. Therefore, the existing data query through the database of the CRM system is inefficient.
In order to solve the above problem, an embodiment of the present application discloses a dimension data processing method, an apparatus, a storage medium, and an electronic device, where original dimension data in each fact table is summarized according to a dimension type such as a date and a customer to obtain a data dimension table, so as to reduce other useless data, reduce a load of a database, eliminate the need to summarize the data dimension table again, and improve query efficiency when querying index data in the data dimension table. The specific implementation is illustrated by the following examples.
Referring to fig. 1, a schematic flow chart of a dimensional data processing method disclosed in an embodiment of the present application is shown, where the dimensional data processing method mainly includes the following steps:
s101: classifying the obtained original dimensional data through a pre-constructed dimensional data model to obtain a classification result; each original dimension data is dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data.
In S101, since the original dimension data are randomly and irregularly distributed in the Fact tables (Fact tables), the original dimension data need to be classified in order to improve the reading efficiency.
Specifically, the process of classifying the obtained original dimensional data through a pre-constructed dimensional data model to obtain a classification result includes the following steps:
first, raw dimension data is acquired from each fact table.
The fact table is a fact data table, and the fact table is mainly characterized by containing a large amount of data, and the data can be summarized and recorded.
Then, determining each dimension type corresponding to the original dimension data.
And finally, classifying each dimension type through a pre-constructed dimension data model to obtain a classification result.
The process of constructing the dimension data model comprises the following steps:
and constructing a dimension data model by a normal form modeling method and a dimension modeling method.
The dimension data model classifies each dimension type, as shown in fig. 2.
In fig. 2, the dimension data model (business system data model) includes a topic domain logic model and a topic domain model.
The topic domain logic model is used for executing topic domain logic, the topic domain logic is as follows, for example, data related to a customer is an account topic, data related to a public type such as dictionary data is a public topic, data related to a transaction of the customer is a transaction topic, data related to a fund of the customer is a fund topic and the like.
And classifying the dimension data models to obtain a data warehouse domain model and a logic model.
The service data is a component of a data warehouse model, and the original data is used for establishing data models with various dimensions through the dimension data model.
The dimension modeling method starts to construct a model according to the requirements of analysis decision, the constructed data model serves the analysis requirements, and the constructed dimension data index layer (DM layer) integrates various data required under different requirements, namely, the data are available and ready, so that the dimension modeling method mainly solves the problem that how a user can complete the analysis requirements more quickly, and has better response performance of large-scale complex query.
The dimension data index layer is used for summarizing data of common fields under different decisions so as to improve query response, and a normal form modeling method and a dimension modeling method are adopted for building. The normal form modeling method mainly solves the technical problem of data storage and utilization of a relational database. At present, most of modeling methods in the relational database adopt a three-model modeling method. A paradigm is a collection of relational patterns that conform to a certain level. Constructing a database must follow certain rules, which are normal in a relational database, and this process is also called normalization. There are currently six paradigms for relational databases: a first norm (1NF), a second norm (2NF), a third norm (3NF), a Boyce-Codd norm (BCNF), a fourth norm (4NF), and a fifth norm (5 NF). In the model design of a data warehouse, a third paradigm is generally employed. A relationship conforming to the third paradigm must have three conditions: each attribute value is unique and has no ambiguity; each non-primary property must depend entirely on the primary key, rather than a portion of the primary key; each non-primary attribute cannot depend on attributes in other relationships to which it should belong.
Typical representatives include a Star model (Star-schema), and a snowflake model (Snow-schema) that is applicable in some special scenarios.
In the construction process of the dimension data model, a fact table and a dimension table (dimension table) are involved, and a data warehouse and a data mart are constructed according to the fact table and the dimension table.
The relationships between the concrete fact tables and the dimension tables are shown in fig. 3.
In fig. 3, a plurality of dimension tables (dimension table 1, dimension table 2, dimension table 3, dimension table 4, dimension table 5, dimension table 6, and the like) are generated from fact tables.
For example, by virtue of fact tables: the fund reconciliation table, the position taking summary table, the delivery table, the row right table, the transaction detail table, the leveling detail table and the deposit and withdrawal detail table are mutually associated according to the dimension of each day and the customer, the data is summarized, and the dimension table is generated: daily customer dimension table.
S102: summarizing the classification result to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after the classification and the collection.
The data dimension table is composed of data such as a plurality of dimension tables.
The specific data dimension table is shown in table 1.
| Name of classification | Classification code | English name |
| Daily capital account dimension | 99 | Dayfundacc |
| Daily customer variety dimension | 98 | Dayclientpro |
| Dimension of day customer | 97 | Dayclient |
| Dimension of daily variety | 96 | Daypro |
| Dimension of day branch office | 95 | Daybranch |
| Daily exchange dimension | 94 | Daymarket |
| Monthly funding account dimension | 93 | Monfundacc |
| Monthly customer variety dimension | 92 | Monclientpro |
| Monthly customer dimension | 91 | Monclient |
| Dimension of moon variety | 90 | Monpro |
| Dimension of moon branch office | 89 | Monbranch |
| Monthly exchange dimension | 88 | Monmarket |
| Monthly marketer customer dimension | 87 | Moncspclient |
| Monthly marketer dimension | 86 | Moncsp |
TABLE 1
In table 1, each dimension in the category name corresponds to a mapping table. The mapping table stores data related to each dimension, such as a mapping table corresponding to a daily exchange dimension, and the mapping table stores transaction data, fund data, deposit and withdrawal data, position data and the like maintained by the daily exchange.
Specifically, the classification results are summarized, and the process of obtaining the data dimension table is as follows:
firstly, determining the summarizing dimension of a classification result through a dimension data index layer of a dimension data model.
The dimensions of the summary include the dimensions of the date (day) of extraction, the currency, the business department, the customer number and the like. As shown in particular in fig. 4.
In fig. 4, each dimension in the aggregation dimensions is obtained by the customer demand, and the aggregation dimensions include a day dimension and a month dimension. The dimensionality can be expanded, and new index data is added.
The daily dimension includes daily clients, daily business departments, daily client categories, daily categories, and daily exchanges. The daily client comprises a date, a currency, a client number and a business department; the daily business department comprises a date, a currency and a business department; the daily customer class comprises date, currency and customer class; the daily client variety comprises date, currency, client number, business department, transaction type, exchange and variety; daily varieties include date, currency, transaction type and variety.
The month dimension comprises month customers, a month business department, a month customer category, a month customer variety, a month variety and a month exchange; the monthly clients are the same-day clients; the monthly business department is the same-day business department; the monthly customer class is the same-day customer class; the monthly client variety is the same-day client variety; the moon variety is the same day variety; the monthly exchange is the same day exchange.
And then, summarizing the classification result through a summarizing dimension to obtain a data dimension table.
And executing daily customer dimension logic after acquiring the data after the disk is finished every day.
For convenience of understanding, the process of summarizing the classification result through a summarizing dimension to obtain the data dimension table is described here by way of example:
example 1, taking the daily customer dimension as an example, the date (day), currency, business department and customer number are extracted as dimension indexes of the collection dimension, transaction data, capital data, money and money data, position data, right data and delivery data under the dimension are collected, after the data after the market is collected every day, the data after the market stops trading, daily customer dimension logic (statistics of the daily dimension data related to the customer dimension) is executed, and the daily customer dimension data of the current day is generated.
Example 2, taking a month variety dimension as an example, extracting a date (month), a currency, a transaction type and a variety as a dimension index of a summary dimension, summarizing transaction data, position data, row weight data and delivery data in the dimension, executing month customer dimension logic (counting month dimension data related to the customer dimension) after collecting the data after the disk every day, and generating month variety dimension data from the beginning of the month to the current day.
Optionally, a data dimension table is configured.
The process of configuring the data dimension table specifically is as follows:
firstly, acquiring each service requirement; the business requirements are requirements corresponding to business types of all companies.
Then, extracting a preset service index from the service requirement; the preset service index is a service index with the same service type under different dimensions.
The method comprises the steps of collecting service requirements of each futures company, and extracting common service fields required by the service requirements.
And finally, configuring a data dimension table for the preset service index.
The method comprises the steps of designing indexes and index logics required by all dimensions in a database (for example, the index logic of a net income index is income deduction), and finally carrying out logic development and data landing according to each dimension index, wherein the dimension indexes support expansion and modification.
The data to the ground is persistent data, and the data is generally placed in a hard disk or other persistent storage device, for example: pictures, system logs, data displayed on pages, data stored in a relational database, and the like, the landing data has a fixed carrier, and the landing data cannot disappear instantly.
S103: when a query request is received, index data corresponding to the query request in the data dimension table is obtained.
For example, if the query request is transaction data of a customer dimension, the index data is related data of query "a day customer dimension", and if the query is variety transaction data of a customer, the index data is related data of query "a day customer variety dimension".
According to business requirements, floor logic of different dimensionality indexes is designed in a database, and the latest index data of each dimensionality is generated through daily execution of tasks and used in the process of generating a front-end report, and the logic can also be used for generating historical data (data of historical transaction dates).
When a query request is received, corresponding index data is queried, so that summary calculation from detailed data is avoided, report query efficiency is improved, database burden is reduced, and use experience of a client is improved.
For convenience of understanding of the index data, the description is given by way of example with reference to fig. 5, and fig. 5 is a schematic diagram illustrating the arrangement of fields in each index data by formula arrangement.
In fig. 5, for the best-before-profit and loss of taken position, the fields in each index data are configured by formula configuration, for example, if the calculation logic of the index needs to be modified, a button in an operator may be selected, calculation logic between different indexes may be added, or other calculation schemes in an extension scheme, such as a scheme of taking an end-of-term value, a judgment whether the data is the end-of-term data or not may be added.
Specifically, when a query request is received, if the query request meets the preset query condition, the process of obtaining the index data corresponding to the query request in the data dimension table is shown as a 1-A3.
A1: when receiving the query request, analyzing the query request to obtain query dimension data.
A2: and matching the query dimension data with preset dimension data in the data dimension table.
The preset dimension data is dimension data stored in a database in advance. The preset dimension data can be expanded.
A3: and if the query dimension data are consistent with the preset dimension data in the data dimension table, obtaining index data corresponding to the query request in the data dimension table.
Index data of each dimension is suitable for different service scenes, and the utilization rate is guaranteed.
The collected index data can be used during report development, so that the report development efficiency is improved, and data errors are reduced.
According to the data dimension table query method and device, original dimension data in each fact table are collected to obtain the data dimension table according to the dimension types such as dates and clients, other useless data are reduced, the burden of a database is reduced, the data dimension table does not need to be collected again, and query efficiency is improved when index data in the data dimension table are queried.
Based on the dimension data processing method disclosed in fig. 1 in the foregoing embodiment, an embodiment of the present application further discloses a dimension data processing apparatus, and as shown in fig. 6, the dimension data processing apparatus includes a classifying unit 601, a summarizing unit 602, and an obtaining unit 603.
A classification unit 601, configured to classify the obtained original dimensional data through a pre-constructed dimensional data model to obtain a classification result; each original dimension data is dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data.
A summarizing unit 602, configured to summarize the classification results to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after the classification and the collection.
An obtaining unit 603, configured to, when receiving the query request, obtain index data corresponding to the query request in the data dimension table.
Further, the classification unit comprises a first obtaining module, a first determining module and a classification module.
And the first acquisition module is used for acquiring original dimension data from each fact table.
And the first determining module is used for determining each dimension type corresponding to the original dimension data.
And the classification module is used for classifying all dimension types through a pre-constructed dimension data model to obtain a classification result.
Further, the classification unit 601 of the process of building the dimension data model is specifically configured to build the dimension data model through a paradigm modeling method and a dimension modeling method.
Further, the summarizing unit 602 includes: the device comprises a second determining module and a summarizing module.
And the second determination module is used for determining the summarizing dimension of the classification result through the dimension data index layer of the dimension data model.
And the summarizing module is used for summarizing the classification results through summarizing dimensions to obtain the data dimension table.
Further, the obtaining unit 603 includes an analyzing module, a matching module, and a second obtaining module.
The analysis module is used for analyzing the query request to obtain query dimension data when the query request is received;
and the matching module is used for matching the query dimension data with preset dimension data in the data dimension table.
And the second acquisition module is used for acquiring index data corresponding to the query request in the data dimension table if the query dimension data is consistent with the preset dimension data in the data dimension table.
Optionally, the dimension data processing apparatus further includes a configuration unit.
And the configuration unit is used for configuring the data dimension table.
The configuration unit comprises a third acquisition module, an extraction module and a configuration module.
The third acquisition module is used for acquiring each service requirement; the business requirements are requirements corresponding to business types of all companies.
The extraction module is used for extracting preset service indexes from the service requirements; the preset service index is a service index with the same service type under different dimensions.
And the configuration module is used for configuring the preset service index into the data dimension table.
According to the data dimension table query method and device, original dimension data in each fact table are collected to obtain the data dimension table according to the dimension types such as dates and clients, other useless data are reduced, the burden of a database is reduced, the data dimension table does not need to be collected again, and query efficiency is improved when index data in the data dimension table are queried.
The embodiment of the application also provides a storage medium, wherein the storage medium comprises stored instructions, and when the instructions are executed, the equipment where the storage medium is located is controlled to execute the dimensional data processing method.
The present embodiment further provides an electronic device, whose schematic structural diagram is shown in fig. 7, specifically including a memory 701 and one or more instructions 702, where the one or more instructions 702 are stored in the memory 701, and are configured to be executed by one or more processors 703 to execute the one or more instructions 702 to perform the above-mentioned dimensional data processing method.
While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present application is not limited by the order of acts or acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
It should be noted that, in the present specification, the embodiments are all described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. For the system-class embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The steps in the method of the embodiments of the present application may be sequentially adjusted, combined, and deleted according to actual needs.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method for processing dimension data, the method comprising:
classifying the obtained original dimensional data through a pre-constructed dimensional data model to obtain a classification result; the original dimension data are dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data;
summarizing the classification result to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after classification and collection;
and when a query request is received, acquiring index data corresponding to the query request in the data dimension table.
2. The method according to claim 1, wherein the classifying the acquired original dimensional data through the pre-constructed dimensional data model to obtain a classification result comprises:
acquiring original dimension data from each fact table;
determining each dimension type corresponding to the original dimension data;
and classifying the dimension types through a pre-constructed dimension data model to obtain a classification result.
3. The method of claim 2, wherein the process of building a dimensional data model comprises:
and constructing a dimension data model by a normal form modeling method and a dimension modeling method.
4. The method of claim 1, wherein the aggregating the classification results to obtain a data dimension table comprises:
determining a summary dimension of the classification result through a dimension data index layer of the dimension data model;
and summarizing the classification results through the summarizing dimension to obtain a data dimension table.
5. The method according to claim 1, wherein when receiving a query request, acquiring metric data corresponding to the query request in the data dimension table comprises:
when a query request is received, analyzing the query request to obtain query dimension data;
matching the query dimension data with preset dimension data in the data dimension table;
and if the query dimension data are consistent with the preset dimension data in the data dimension table, obtaining index data corresponding to the query request in the data dimension table.
6. The method of claim 1, further comprising:
configuring the data dimension table;
the process of configuring the data dimension table is as follows:
acquiring each service requirement; the business requirements are requirements corresponding to business types of all companies;
extracting preset service indexes from the service requirements; the preset service index is a service index with the same service type under different dimensionalities;
and configuring the preset service index into the data dimension table.
7. An apparatus for processing dimension data, the method comprising:
the classification unit is used for classifying the acquired original dimensional data through a pre-constructed dimensional data model to obtain a classification result; the original dimension data are dimension data before being classified; the classification result is used for representing the result of classifying the types of the dimensionality types of the original dimensionality data;
the summarizing unit is used for summarizing the classification result to obtain a data dimension table; the data dimension table is a data table of various types of dimensions after classification and collection;
and the acquisition unit is used for acquiring index data corresponding to the query request in the data dimension table if the query request meets a preset query condition when the query request is received.
8. The apparatus of claim 7, wherein the classification unit comprises:
the first acquisition module is used for acquiring original dimension data from each fact table;
the first determining module is used for determining each dimension type corresponding to the original dimension data;
and the classification module is used for classifying the dimension types through a pre-constructed dimension data model to obtain a classification result.
9. A storage medium, characterized in that the storage medium comprises stored instructions, wherein the instructions, when executed, control a device on which the storage medium is located to perform the dimensional data processing method according to any one of claims 1 to 6.
10. An electronic device comprising a memory, and one or more instructions, wherein the one or more instructions are stored in the memory and configured to be executed by the one or more processors to perform the dimensional data processing method of any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111678356.7A CN114265887A (en) | 2021-12-31 | 2021-12-31 | Dimension data processing method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111678356.7A CN114265887A (en) | 2021-12-31 | 2021-12-31 | Dimension data processing method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114265887A true CN114265887A (en) | 2022-04-01 |
Family
ID=80832558
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111678356.7A Pending CN114265887A (en) | 2021-12-31 | 2021-12-31 | Dimension data processing method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114265887A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115544337A (en) * | 2022-09-01 | 2022-12-30 | 睿智合创(北京)科技有限公司 | Data processing method and system starting from data origin |
-
2021
- 2021-12-31 CN CN202111678356.7A patent/CN114265887A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115544337A (en) * | 2022-09-01 | 2022-12-30 | 睿智合创(北京)科技有限公司 | Data processing method and system starting from data origin |
| CN115544337B (en) * | 2022-09-01 | 2023-06-27 | 睿智合创(北京)科技有限公司 | Data processing method and system starting from data origin |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103748579B (en) | Data are handled in MapReduce frame | |
| US10540375B2 (en) | Systems and methods for self-pairing databases | |
| US9858326B2 (en) | Distributed data warehouse | |
| US20120005153A1 (en) | Creation of a data store | |
| CN103605651A (en) | Data processing showing method based on on-line analytical processing (OLAP) multi-dimensional analysis | |
| CN112632405B (en) | Recommendation method, recommendation device, recommendation equipment and storage medium | |
| CN110851667A (en) | Integrated analysis method and tool for multi-source large data | |
| CN111026801A (en) | Method and system for assisting operation quick decision-making work of insurance type e-commerce | |
| CN113032420A (en) | Data query method and device and server | |
| CN105359172A (en) | Calculating a probability of a business being delinquent | |
| CN114265887A (en) | Dimension data processing method and device, storage medium and electronic equipment | |
| KR20170094935A (en) | System for providing enterprise information and method | |
| WO2013086409A1 (en) | Portfolio risk manager | |
| CN116468011A (en) | Report generation method, device, equipment and storage medium | |
| CN113535966A (en) | Knowledge graph creating method, information obtaining method, device and equipment | |
| CN116561345A (en) | Information knowledge graph construction method based on multi-mode data company | |
| CN116483822A (en) | Service data early warning method, device, computer equipment and storage medium | |
| WO2023098034A1 (en) | Business data report classification method and apparatus | |
| CN115965468A (en) | Transaction data-based abnormal behavior detection method, device, equipment and medium | |
| CN115423361A (en) | Data processing method and device for risk view, storage medium and equipment | |
| CN109614416A (en) | A kind of invoice management method and device based on data statistic analysis | |
| CN112632381B (en) | Information query method and device, computer equipment and storage medium | |
| CN111460052B (en) | Low-security fund supervision method and system based on supervised data correlation analysis | |
| CN113901046A (en) | Virtual dimension table construction method and device | |
| CN111858598A (en) | Mass data comprehensive management system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |