Custom multi-dimensional analysis configuration method, system, device and medium based on constellation model

Abstract

The application discloses a method, system, device and medium for custom multi-dimensional analysis configuration based on a constellation model, and belongs to the technical field of data analysis.The technical problem to be solved by the application is that the construction of basic data such as dimensions, indexes and data models in multi-dimensional analysis is complicated, and multiple mappings such as a main dimension table, a role-playing dimension and a dictionary dimension are adopted.The technical scheme is as follows: the method is configured by importing a model table, and the dimensions, indexes, fact table and dimension table are associated, the related tables, attributes and model relationships are automatically imported and mapped between the dimension tables, the model maintenance cost and time are reduced, and the ordered model construction, the multi-fact table dimension screening machine fact table attribute reverse mapping application configuration scene are realized through the dimensions, indexes, fact table, dimension table, data model and custom multi-dimensional query, meanwhile, the required indexes are flexibly selected from multiple fact tables through a drag operation, and the common dimensions are automatically screened out and displayed according to the cascading mode of the indexes and the dimensions.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data analysis, in particular to a self-defined multi-dimensional analysis configuration method, system, device and medium based on a constellation model. BACKGROUND

[0002] In the digital era, data analysis has penetrated into all aspects of various industries, and with its powerful data processing and analysis capabilities, it has brought unprecedented opportunities and challenges to various industries. In the process of constructing enterprise digitalization, there are often problems such as that a large amount of marketing data is not reasonably utilized, decision makers lack intelligent multi-dimensional data analysis information, and precise control and strategic decision making are difficult to start. Data multi-dimensional analysis has important applications in finance, medical treatment, education and other fields, which can help decision makers to analyze, serve and manage, improve data use efficiency and quality, and promote the development and progress of related industries.

[0003] At present, the construction and optimization of multi-dimensional data models are too dependent on data experts, and when the data scale increases and the data analysis requirements change frequently, the manual modeling method will consume a lot of manpower; there are too many statistical classifications of dimension tables, there are multi-level mixed associations between dimension tables, and the mapping of dictionary dimensions and role-playing dimensions is chaotic, which leads to easy omission of associated items in configuration.

[0004] When a user actually configures a multi-dimensional analysis index model, there are too many dimension table and fact table field attributes, the dimension table maps multiple dimensions according to business needs, and a single index maps multiple dimensions, which leads to the situation that operation and maintenance personnel excessively rely on data experts and the configuration work is prone to error and low efficiency.

[0005] At present, when multiple index statistical analysis is involved in the industry, the fact table and dimension table are usually merged, and the dimensions and indexes are set in the same fact table, which leads to the situation that users cannot compare and analyze indexes in multiple dimensions, multiple perspectives and multiple levels. SUMMARY

[0006] The technical task of the application is to provide a self-defined multi-dimensional analysis configuration method, system, device and medium based on a constellation model, to solve the problems of complicated construction of basic data such as dimensions, indexes and data models in multi-dimensional analysis, and multiple mapping of main dimension tables, role-playing dimensions and dictionary dimensions.

[0007] The technical task of the present application is achieved in the following way: a self-defined multi-dimensional analysis configuration method based on a constellation model, which is configured by importing model table configuration dimensions, index, fact table and dimension table association relationship, and automatically importing related tables, attributes and model relationship between mapping (main dimension table, role-playing dimension, dictionary dimension) tables, reducing model maintenance cost and time, and realizing ordered model construction, multi-fact table dimension screening machine fact table attribute reverse mapping application configuration scene through dimensions, index, fact table, dimension table, data model and self-defined multi-dimensional query, at the same time, through drag operation, the required index is flexibly selected from multiple fact tables, and according to the cascading mode of index and dimension, common dimensions are automatically screened out for display; the specific implementation is as follows:

[0008] Data source access;

[0009] Configuration import model;

[0010] Configure index model;

[0011] Self-defined multi-dimensional analysis query.

[0012] As preferred, the data source access is specifically as follows:

[0013] Select the corresponding data source type, and provide examples of necessary information for different data source types;

[0014] According to the connection information of the database, modify the information in the form, and input the user password, then test, if the information is correct, prompt the database connection success, realize the data source access.

[0015] More preferably, the configuration import model is specifically as follows:

[0016] Configure mapping relationship: through configuring table type, source table type, source table name, source table field name, target table name, target table field name and dictionary code value, the field association relationship between fact table and dimension table, the field association relationship between dimension table and dimension table, and the dictionary dimension classification are automatically formed into configuration model; wherein, the table type includes code table and dimension table; the source table type includes fact table and dimension table;

[0017] Configure basic model: by configuring data table name, data table type code, data table type name, dictionary name, field type, get fact table statistical index, dimension table statistical measure and dimension management and data model configuration; wherein, the field type includes index and measure.

[0018] More preferably, the configuration index model is specifically as follows:

[0019] The fact table is managed by configuring code, name, table name, field Chinese name, field association type, field length, decimal place, whether null, description and format specification, and the basic attributes of the fact table are defined; or an existing table is selected, and an existing field is configured;

[0020] The dimension table is managed by configuring code, name, table name, field Chinese name, field association type, field length, decimal place, whether null, description and format specification, and the basic attributes of the dimension table are defined; or an existing table is selected, and an existing field is configured;

[0021] The association relationship between the fact table and the dimension table is defined by attributes, mapping dimension tables, dimension table attributes, connection mode, whether a dictionary dimension, and dictionary dimension attribute;

[0022] The dimension table is managed by configuring dimension attribute type, dimension table attribute name, dimension attribute name, dimension field name, field alias and custom query condition, and the mapping relationship between the dimension tables is defined;

[0023] The dimension is managed by configuring master table attribute, slave table name, slave table attribute, slave table display field, connection mode and custom query condition, and the mapping relationship between the dimension attribute and the dimension table is defined;

[0024] The index is managed by configuring index name, index type, summary basis, unit, fact table, measure, custom query condition, and the index calculation formula and model are defined; wherein the index type includes atomic index and calculation index;

[0025] The data model configures the association relationship between the index and the dimension, which is used for custom multi-dimensional query.

[0026] More preferably, the custom multi-dimensional analysis query is as follows:

[0027] By selecting an index model, an index and dimension tree menu are displayed;

[0028] By dragging an index to the right data display area, related dimension information is automatically selected, and the dimension tree is updated;

[0029] The user freely places the selected dimension into the horizontal or vertical area by dragging operation, and easily constructs the required multi-dimensional analysis framework;

[0030] Supporting dragging dimension query condition, configuring dimension filtering condition and sorting, and querying the required result;

[0031] The configuration information is persisted to the database, and export and sharing functions are provided.

[0032] A custom multi-dimensional analysis configuration system based on a constellation model, the system comprising:

[0033] The data source access module is used to configure the basic properties of the database, connect to different data sources, and achieve rapid data source access.

[0034] The import model configuration module is used to automatically form a configuration model by configuring the field relationships between fact tables and dimension tables, the field relationships between dimension tables, and the dictionary dimension classification by configuring the table type, source table type, source table name, source table field name, target table name, target table field name, and dictionary code value. It also obtains fact table statistical indicators, dimension table statistical measures, dimension management, and data model configuration by configuring the data table name, data table type code, data table type name, dictionary name, and field type.

[0035] The indicator model configuration module is used to configure fact tables, dimension tables, dimensions, indicators and data models, and to build indicator models by configuring the mapping between fact tables and dimension tables, the mapping between dimension tables, and the mapping between dimensions and dimension tables.

[0036] The custom multidimensional analysis query module allows users to define drag-and-drop and combine different dimensions and indicator models according to their needs, and configure query dimension conditions and dimension filtering to realize business data display and switching.

[0037] Preferably, the data source access module includes:

[0038] The selection submodule is used to select the corresponding data source type and provides examples of necessary information for different data source types.

[0039] The Modify and Connect submodule is used to modify the information in the form based on the database connection information, enter the user password, and then test. If the information is correct, it will indicate that the database connection is successful, thus realizing the data source access.

[0040] More preferably, the indicator model configuration module includes:

[0041] The Fact Table Management submodule is used to manage fact tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the fact table; or to select an existing table and configure existing fields.

[0042] The dimension table management submodule is used to manage dimension tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the dimension table; or to select an existing table and configure existing fields.

[0043] The relationship definition submodule is used to define the relationship between the fact table and the dimension table through attributes, mapping dimension tables, dimension table attributes, join method, whether it is a dictionary dimension and dictionary dimension attributes;

[0044] The first submodule, Mapping Relationship Definition, is used to manage dimension tables and define the mapping relationships between dimension tables by configuring dimension attribute types, dimension table attribute names, dimension attribute names, dimension field names, field aliases, and custom query conditions.

[0045] The mapping relationship definition submodule 2 is used to manage dimensions by configuring master table attributes, slave table names, slave table attributes, slave table display fields, join methods, and custom query conditions, and to define the mapping relationship between dimension attributes and dimension tables;

[0046] The indicator model definition submodule is used to manage indicators by configuring indicator names, indicator types, summary basis, units, fact tables, measures, and custom query conditions, and to define indicator calculation formulas and models; among which, indicator types include atomic indicators and calculated indicators;

[0047] The indicator and dimension relationship configuration submodule is used to configure the relationship between indicators and dimensions in the data model and to customize multidimensional queries;

[0048] The custom multidimensional analysis query module includes:

[0049] The display submodule is used to display a tree menu of metrics and dimensions by selecting a metric model;

[0050] The filtering submodule is used to automatically filter out related dimension information by dragging and dropping indicators into the right data display area and updating the dimension tree.

[0051] The multidimensional analysis framework construction submodule allows users to freely drag and drop selected dimensions directly into the horizontal or vertical dimension area, easily building the required multidimensional analysis framework.

[0052] The query submodule supports dragging and dropping dimension query conditions, configuring dimension filtering conditions and sorting, and retrieving the required results.

[0053] The persistence submodule is used to persist configuration information to the database and provides export and sharing functions.

[0054] Constellation models are advanced multidimensional data models in the data warehousing field. They are extensions of star and snowflake schemas, supporting multiple fact tables sharing dimension table information. Constellation models are used as the foundation for building custom multidimensional analysis frameworks, meeting the data analysis needs of complex business scenarios through their flexibility and scalability.

[0055] An electronic device includes: a memory and at least one processor;

[0056] The memory contains computer programs;

[0057] The at least one processor executes the computer program stored in the memory, causing the at least one processor to perform the custom multidimensional analysis configuration method based on the constellation model as described above.

[0058] A computer-readable storage medium storing a computer program that can be executed by a processor to implement the constellation-based custom multidimensional analysis configuration method described above.

[0059] The custom multidimensional analysis configuration method, system, device, and medium based on constellation model of the present invention have the following advantages:

[0060] (i) By introducing drag-and-drop operation and consistent dimensional models and indicator standards, this invention solves the problems of cumbersome construction of basic data such as dimensions, indicators, and data models and complex multiple mappings in multidimensional analysis. It not only improves the efficiency and accuracy of data analysis, but also realizes the ability to generate standardized, efficient, cross-business, and full-dimensional statistical analysis reports, providing strong support for data analysts.

[0061] (II) This invention relates to the technical fields of data modeling, storage, mining, analysis, and processing, aiming to address the needs for multi-dimensional analysis and in-depth insights in complex data environments. By integrating functions such as data modeling, efficient storage, intelligent mining, multi-dimensional analysis, and refined processing, it provides users with a comprehensive, multi-perspective, multi-dimensional data platform, which not only improves the efficiency of data processing and analysis but also enhances the depth and breadth of data insights, providing strong support for enterprise decision-making.

[0062] (III) This invention automatically imports related tables, attributes, and model relationships by importing the dimensions, metrics, fact table relationships, and dimension table mappings (main dimension table, role-playing dimension, dictionary dimension) configured in the model table, thereby reducing model maintenance costs and time. Furthermore, it solves application configuration scenarios such as chaotic model construction, multi-fact table dimension filtering, and reverse mapping of fact table attributes through dimensions, metrics, fact tables, dimension tables, data models, and custom multi-dimensional queries.

[0063] (iv) This invention introduces an indicator model import function, which greatly simplifies the data modeling process. Users only need to go through simple configuration steps, including specifying the fact table, defining dimension table attributes, and setting the mapping relationship between the fact table and the dimension table. The system can automatically complete the process from extracting attributes from the dimension table and fact table, establishing the relationship, to building the dimension, indicator, and even the complete data model. This not only improves modeling efficiency but also ensures the consistency and accuracy of the data model. More importantly, when business needs change and new table field attributes need to be added to the existing model or the relationship needs to be adjusted, users only need to update the corresponding template configuration. The system can intelligently identify and automatically execute the indicator model update operation without manually adjusting the complex database structure or rewriting the code, thereby significantly reducing maintenance costs and time consumption. This invention provides users with a flexible, efficient, and easy-to-maintain data modeling tool, enabling data analysis and insights to respond more quickly and accurately to business changes and providing strong support for enterprise decision-making.

[0064] (V) This invention introduces a powerful multi-indicator filtering and comparison function, which completely revolutionizes the convenience and efficiency of data analysis. Users can flexibly select the required indicators from multiple fact tables through intuitive drag-and-drop operations, and automatically filter out common dimensions for display based on the cascading mode of indicators and dimensions. This not only simplifies the complex data filtering process, but also allows users to easily build a data analysis view that meets their own needs through the hierarchical display of business menus. At the same time, for complex scenarios where a single calculated indicator may span different fact tables, this invention also provides an efficient solution. That is, by intelligently identifying and associating the dimensions involved in these indicators, it can automatically filter dimensions, ensuring that users can conduct analysis based on a unified dimension framework when comparing indicators across fact tables, thereby effectively solving complex business needs that are difficult to handle in traditional methods.

[0065] (vi) The multi-indicator screening and comparison function of the present invention not only improves the flexibility and depth of data analysis, but also greatly reduces the user's operational difficulty and learning cost, so that data analysis can serve the enterprise's decision-making process more efficiently and accurately.

[0066] (vii) Users can easily configure table attributes and relationships between tables, and import the configuration model through the indicator model, avoiding the tedious configuration of dimension tables and fact tables, and enabling users to quickly build models.

[0067] (viii) The indicator model module of this invention supports the configuration of dimension tables, fact tables, indicators, dimensions, and data models. Through the above model configuration, the association between fact tables and dimension tables, the relationship between dimension tables, especially the mapping relationship between role-playing dimensions and dictionary dimensions, can be broken down from the business logic; while the data model configuration enables the business connection between dimensions and indicators, making it convenient for users to use in custom multidimensional analysis queries;

[0068] (ix) In the custom multidimensional query module, the present invention allows users to query relevant business data by dragging and dropping indicators and dimensions. It supports multi-indicator and dimension filtering, and the indicators can come from different fact tables, enabling users to analyze and process data in multiple business, multiple dimensions, and multiple iteration levels. Attached Figure Description

[0069] The invention will be further described below with reference to the accompanying drawings.

[0070] Appendix Figure 1 A flowchart illustrating the configuration method for a custom multidimensional analysis based on a constellation model;

[0071] Appendix Figure 2 A diagram illustrating the configuration of dimensional metrics;

[0072] Appendix Figure 3 Example diagram for configuring dimensional metrics. Detailed Implementation

[0073] The following detailed description of the custom multidimensional analysis configuration method, system, device, and medium based on the constellation model of the present invention is provided with reference to the accompanying drawings and specific embodiments.

[0074] Example 1:

[0075] As attached Figure 1 and 2 As shown, this embodiment provides a custom multidimensional analysis configuration method based on a constellation model. This method automatically imports relevant tables, attributes, and model relationships by importing dimensions, metrics, fact table relationships with dimension tables, and dimension table mappings (main dimension table, role-playing dimension, dictionary dimension) configured in the model table. This reduces model maintenance costs and time. It achieves orderly model construction and application configuration scenarios for multi-fact table dimension filtering and reverse mapping of fact table attributes through dimensions, metrics, fact tables, dimension tables, data models, and custom multidimensional queries. Furthermore, it allows for flexible selection of desired metrics from multiple fact tables through drag-and-drop operations and automatically filters out common dimensions for display based on the cascading pattern of metrics and dimensions. Specifically:

[0076] S1, Data source access;

[0077] S2. Configure and import the model;

[0078] S3, Configuration Indicator Model;

[0079] S4. Custom multidimensional analysis query.

[0080] The data source access in step S1 of this embodiment is as follows:

[0081] S101. Select the corresponding data source type and provide examples of necessary information for different data source types;

[0082] S102. Based on the database connection information, modify the information in the form, enter the user password, and then test. If the information is correct, prompt that the database connection is successful and the data source access is realized.

[0083] The specific configuration import model in step S2 of this embodiment is as follows:

[0084] S201. Configure mapping relationships: By configuring table type, source table type, source table name, source table field name, target table name, target table field name, and dictionary code value, the field association relationships between fact tables and dimension tables, the field association relationships between dimension tables, and the dictionary dimension classification are automatically formed into a configuration model; where table types include code tables and dimension tables; source table types include fact tables and dimension tables.

[0085] S202. Configure the basic model: By configuring the data table name, data table type code, data table type name, dictionary name, and field type, obtain the fact table statistical indicators, dimension table statistical measures, dimension management, and data model configuration; among which, the field type includes indicators and measures.

[0086] The specific configuration index model in step S3 of this embodiment is as follows:

[0087] S301. Manage fact tables by configuring codes, names, table names, Chinese field names, field association types, field lengths, decimal places, whether to be nullable, descriptions, and format descriptions, and define the basic attributes of the fact tables; or select an existing table and configure existing fields.

[0088] S302. Manage dimension tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and define the basic attributes of the dimension tables; or select an existing table and configure existing fields.

[0089] S303. The fact table defines the relationship between the fact table and the dimension table through attributes, mapping dimension tables, dimension table attributes, join method, whether it is a dictionary dimension and dictionary dimension attributes;

[0090] S304. Manage dimension tables and define the mapping relationships between dimension tables by configuring dimension attribute types, dimension table attribute names, dimension attribute names, dimension field names, field aliases, and custom query conditions.

[0091] S305. Manage dimensions by configuring master table attributes, slave table name, slave table attributes, slave table display fields, join method, and custom query conditions, and define the mapping relationship between dimension attributes and dimension tables;

[0092] S306. Manage indicators by configuring indicator names, indicator types, summary basis, units, fact tables, measures, and custom query conditions, and define indicator calculation formulas and models; among which, indicator types include atomic indicators and calculated indicators;

[0093] S307, Data model configuration indicators and dimension relationships, used for custom multidimensional queries.

[0094] The custom multidimensional analysis query in step S4 of this embodiment is as follows:

[0095] S401. By selecting the indicator model, a tree menu of indicators and dimensions is displayed;

[0096] S402. Drag and drop indicators to the right data display area to automatically filter out related dimension information and update the dimension tree.

[0097] S403. Users can freely drag and drop selected dimensions directly into the horizontal or vertical dimension area to easily build the required multidimensional analysis framework.

[0098] S404 supports dragging and dropping dimension query conditions, configuring dimension filtering conditions and sorting, and querying the required results;

[0099] S405 persists configuration information to a database and provides export and sharing functions.

[0100] As attached Figure 3 As shown, this is an example of a cross-fact table model based on a constellation model. It starts with the service application fact table and the service approval fact table and expands outwards. To the left, the configuration is the regional service object dimension. Both fact tables have a field that maps to the birthplace dimension and the location dimension. These two dimension tables share common dimension attributes that map to the region dimension. The region dimension's multi-level dimension attributes, which divide regions into multiple levels, can be associated with the fact tables. To the right, the configuration maps to the dictionary dimension. The two fact tables correspond to the service object dimension attributes. The department identifier in the service object dimension maps to the department dimension, and the department type in the department dimension maps to the dictionary dimension. This configuration enables the application of a multi-indicator cross-fact table dimensional model.

[0101] Example 2:

[0102] This embodiment provides a custom multidimensional analysis configuration system based on a constellation model, the system including:

[0103] The data source access module is used to configure the basic properties of the database, connect to different data sources, and achieve rapid data source access.

[0104] The import model configuration module is used to automatically form a configuration model by configuring the field relationships between fact tables and dimension tables, the field relationships between dimension tables, and the dictionary dimension classification by configuring the table type, source table type, source table name, source table field name, target table name, target table field name, and dictionary code value. It also obtains fact table statistical indicators, dimension table statistical measures, dimension management, and data model configuration by configuring the data table name, data table type code, data table type name, dictionary name, and field type.

[0105] The indicator model configuration module is used to configure fact tables, dimension tables, dimensions, indicators and data models, and to build indicator models by configuring the mapping between fact tables and dimension tables, the mapping between dimension tables, and the mapping between dimensions and dimension tables.

[0106] The custom multidimensional analysis query module allows users to define drag-and-drop and combine different dimensions and indicator models according to their needs, and configure query dimension conditions and dimension filtering to realize business data display and switching.

[0107] The data source access module in this embodiment includes:

[0108] The selection submodule is used to select the corresponding data source type and provides examples of necessary information for different data source types.

[0109] The Modify and Connect submodule is used to modify the information in the form based on the database connection information, enter the user password, and then test. If the information is correct, it will indicate that the database connection is successful, thus realizing the data source access.

[0110] The indicator model configuration module in this embodiment includes:

[0111] The Fact Table Management submodule is used to manage fact tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the fact table; or to select an existing table and configure existing fields.

[0112] The dimension table management submodule is used to manage dimension tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the dimension table; or to select an existing table and configure existing fields.

[0113] The relationship definition submodule is used to define the relationship between the fact table and the dimension table through attributes, mapping dimension tables, dimension table attributes, join method, whether it is a dictionary dimension and dictionary dimension attributes;

[0114] The first submodule, Mapping Relationship Definition, is used to manage dimension tables and define the mapping relationships between dimension tables by configuring dimension attribute types, dimension table attribute names, dimension attribute names, dimension field names, field aliases, and custom query conditions.

[0115] The mapping relationship definition submodule 2 is used to manage dimensions by configuring master table attributes, slave table names, slave table attributes, slave table display fields, join methods, and custom query conditions, and to define the mapping relationship between dimension attributes and dimension tables;

[0116] The indicator model definition submodule is used to manage indicators by configuring indicator names, indicator types, summary basis, units, fact tables, measures, and custom query conditions, and to define indicator calculation formulas and models; among which, indicator types include atomic indicators and calculated indicators;

[0117] The indicator and dimension relationship configuration submodule is used to configure the relationship between indicators and dimensions in the data model and to customize multidimensional queries.

[0118] The custom multidimensional analysis query module in this embodiment includes:

[0119] The display submodule is used to display a tree menu of metrics and dimensions by selecting a metric model;

[0120] The filtering submodule is used to automatically filter out related dimension information by dragging and dropping indicators into the right data display area and updating the dimension tree.

[0121] The multidimensional analysis framework construction submodule allows users to freely drag and drop selected dimensions directly into the horizontal or vertical dimension area, easily building the required multidimensional analysis framework.

[0122] The query submodule supports dragging and dropping dimension query conditions, configuring dimension filtering conditions and sorting, and retrieving the required results.

[0123] The persistence submodule is used to persist configuration information to the database and provides export and sharing functions.

[0124] Example 3:

[0125] This embodiment also provides an electronic device, including: a memory and a processor;

[0126] The memory stores the instructions executed by the computer.

[0127] The processor executes computer execution instructions stored in the memory, causing the processor to execute the custom multidimensional analysis configuration method based on the constellation model in any embodiment of the present invention.

[0128] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can be a microprocessor or any conventional processor.

[0129] Memory is used to store computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function, etc.; the data storage area can store data created based on the use of the terminal, etc. In addition, memory can also include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart memory cards (SMC), secure digital cards (SD cards), flash memory cards, at least one disk storage device, flash memory devices, or other volatile solid-state storage devices.

[0130] Example 4:

[0131] This embodiment also provides a computer-readable storage medium storing multiple instructions, which are loaded by a processor to cause the processor to execute the constellation-based custom multidimensional analysis configuration method according to any embodiment of the present invention. Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the above embodiments is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0132] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0133] Storage media embodiments for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RYM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0134] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0135] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion unit connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion unit execute some and all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A custom multidimensional analysis configuration method based on a constellation model, characterized in that, This method automates the import of related tables, attributes, and model relationships by importing dimensions, metrics, fact tables, and inter-dimensional table mappings from the model table configuration. This reduces model maintenance costs and time. It enables ordered model construction and application configuration scenarios for multi-fact table dimension filtering and reverse mapping of fact table attributes through dimensions, metrics, fact tables, dimension tables, data models, and custom multidimensional queries. Furthermore, it allows for flexible selection of desired metrics from multiple fact tables via drag-and-drop operations and automatically filters out common dimensions for display based on the cascading pattern of metrics and dimensions. Details are as follows: Data source access; Configure and import the model; Configure indicator models; Custom multidimensional analysis queries; The specific details of data source access are as follows: Select the corresponding data source type and provide examples of necessary information for different data source types; Based on the database connection information, modify the information in the form, enter the user password, and then test. If the information is correct, a message will be displayed indicating that the database connection is successful, and the data source access is realized. The specific configuration for importing the model is as follows: Configure mapping relationships: By configuring table type, source table type, source table name, source table field name, target table name, target table field name, and dictionary code value, the field association relationships between fact tables and dimension tables, the field association relationships between dimension tables, and the dictionary dimension classification are automatically formed into a configuration model; among them, table types include code tables and dimension tables; source table types include fact tables and dimension tables; Configure the basic model: By configuring the data table name, data table type code, data table type name, dictionary name, and field type, obtain the fact table statistical indicators, dimension table statistical measures, dimension management, and data model configuration; among which, the field types include indicators and measures; The specific configuration indicator model is as follows: Manage fact tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description, and format, and define the basic attributes of the fact tables; or select an existing table and configure existing fields. Manage dimension tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description, and format specifications, and define the basic attributes of the dimension tables; or select an existing table and configure existing fields. The fact table defines the relationship between the fact table and the dimension table through attributes, mapping dimension tables, dimension table attributes, join method, whether it is a dictionary dimension and dictionary dimension attributes; Dimension tables can be managed by configuring dimension attribute types, dimension table attribute names, dimension attribute names, dimension field names, field aliases, and custom query conditions, and the mapping relationships between dimension tables can be defined. Dimensions are managed by configuring master table attributes, slave table names, slave table attributes, slave table display fields, join methods, and custom query conditions, defining the mapping relationship between dimension attributes and dimension tables; Indicators can be managed by configuring indicator names, indicator types, summary basis, units, fact tables, measures, and custom query conditions, and defining indicator calculation formulas and models; among them, indicator types include atomic indicators and calculated indicators. The data model is configured with metrics and dimensional relationships for custom multidimensional queries. The specific details of the custom multidimensional analysis query are as follows: By selecting an indicator model, a tree-like menu of indicators and dimensions will be displayed; By dragging and dropping indicators into the data display area on the right, the system automatically filters out the related dimension information and updates the dimension tree. Users can freely drag and drop selected dimensions directly into the horizontal or vertical dimension area to easily build the required multidimensional analysis framework. It supports dragging and dropping dimension query conditions, configuring dimension filtering conditions and sorting, and retrieving the desired results; The configuration information is persisted to the database, and export and sharing functions are provided.

2. A custom multidimensional analysis configuration system based on a constellation model, characterized in that, The system includes: The data source access module is used to configure the basic properties of the database, connect to different data sources, and achieve rapid data source access. The import model configuration module is used to automatically form a configuration model by configuring the field relationships between fact tables and dimension tables, the field relationships between dimension tables, and the dictionary dimension classification by configuring the table type, source table type, source table name, source table field name, target table name, target table field name, and dictionary code value. It also obtains fact table statistical indicators, dimension table statistical measures, dimension management, and data model configuration by configuring the data table name, data table type code, data table type name, dictionary name, and field type. The indicator model configuration module is used to configure fact tables, dimension tables, dimensions, indicators and data models, and to build indicator models by configuring the mapping between fact tables and dimension tables, the mapping between dimension tables, and the mapping between dimensions and dimension tables. The custom multidimensional analysis query module allows users to define drag-and-drop and combine different dimensions and indicator models according to their needs, and configure query dimension conditions and dimension filtering to realize business data display and switching. The data source access module includes: The selection submodule is used to select the corresponding data source type and provides examples of necessary information for different data source types. The Modify and Access submodule is used to modify the information in the form based on the database connection information, enter the user password, and then test. If the information is correct, it will indicate that the database connection is successful and the data source access is realized. The indicator model configuration module includes: The Fact Table Management submodule is used to manage fact tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the fact table; or to select an existing table and configure existing fields. The dimension table management submodule is used to manage dimension tables by configuring encoding, name, table name, Chinese field names, field association types, field length, decimal places, whether to be nullable, description and format description, and to define the basic attributes of the dimension table; or to select an existing table and configure existing fields. The relationship definition submodule is used to define the relationship between the fact table and the dimension table through attributes, mapping dimension tables, dimension table attributes, join method, whether it is a dictionary dimension and dictionary dimension attributes; The first submodule, Mapping Relationship Definition, is used to manage dimension tables and define the mapping relationships between dimension tables by configuring dimension attribute types, dimension table attribute names, dimension attribute names, dimension field names, field aliases, and custom query conditions. The mapping relationship definition submodule 2 is used to manage dimensions by configuring master table attributes, slave table names, slave table attributes, slave table display fields, join methods, and custom query conditions, and to define the mapping relationship between dimension attributes and dimension tables; The indicator model definition submodule is used to manage indicators by configuring indicator names, indicator types, summary basis, units, fact tables, measures, and custom query conditions, and to define indicator calculation formulas and models; among which, indicator types include atomic indicators and calculated indicators; The indicator and dimension relationship configuration submodule is used to configure the relationship between indicators and dimensions in the data model and to customize multidimensional queries; The custom multidimensional analysis query module includes: The display submodule is used to display a tree menu of metrics and dimensions by selecting a metric model; The filtering submodule is used to automatically filter out related dimension information by dragging and dropping indicators into the right data display area and updating the dimension tree. The multidimensional analysis framework construction submodule allows users to freely drag and drop selected dimensions directly into the horizontal or vertical dimension area, easily building the required multidimensional analysis framework. The query submodule supports dragging and dropping dimension query conditions, configuring dimension filtering conditions and sorting, and retrieving the required results. The persistence submodule is used to persist configuration information to the database and provides export and sharing functions.

3. An electronic device, characterized in that, include: Memory and at least one processor; The memory contains computer programs; The at least one processor executes the computer program stored in the memory, causing the at least one processor to perform the custom multidimensional analysis configuration method based on the constellation model as described in claim 1.

4. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that can be executed by a processor to implement the custom multidimensional analysis configuration method based on the constellation model as described in claim 1.

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