Self-defined report realization method based on minimum dimension data cube

Abstract

The invention discloses a self-defined report realization method based on a minimum dimension data cube, which comprises the following steps: S1, collecting report requirements, and designing a data model according to the requirements; s2, preprocessing the data in the step S1; s5, constructing a dynamic query; s6, data aggregation and caching; s7, multi-dimensional data analysis; s8, performing real-time data processing; s9, performing user interaction and visualization; and S10, maintaining and updating the system. The invention provides a powerful, flexible and visual data exploration and analysis platform for users. According to the system, storage requirements can be minimized, in-depth analysis of real-time and historical data and highly-customized reports and visualization tools are provided at the same time, and the requirements of different users are met.

Description

Technical Field

[0001] The present invention relates to the field of report realization methods, and in particular to a custom report realization method based on a minimum dimensional data cube. Background Art

[0002] With the rapid development of information technology, reporting systems have transformed from traditional fixed templates to modern dynamic systems that are customized, cloud-based, and process real-time data. Key technologies include data warehouses, OLAP, data cubes, and dimensional modeling, which together support a flexible, efficient, and user-friendly custom reporting environment. With the emergence of custom BI tools, cloud reporting services, and real-time data processing technologies, companies can gain deeper insights into data and respond quickly to market changes, but they also face challenges in data scale processing and system maintainability.

[0003] In existing report implementation methods, most systems rely on fixed data models and predefined query logic. These systems usually provide a user interface that allows users to select specific data fields, dimensions, and filter conditions to generate reports.

[0004] Fixed data model: Existing reporting systems are usually based on a fixed data model, which defines how data is organized and the fields that can be queried. User choices are limited to the fields and relationships defined in the model.

[0005] Predefined query logic: The system provides a set of predefined query logic, and users can generate reports by selecting different query options. However, this query logic is usually static and does not support complex custom requirements.

[0006] User interface operation: Users select report parameters such as time range, data dimensions, and filter conditions through the graphical user interface (GUI). The user's operation determines the content and format of the final report.

[0007] Automatic report generation: Based on the user's selection, the system executes predefined queries and displays the results to the user in the form of tables or charts. Report generation is usually automated but lacks flexibility.

[0008] Data display limitations: Existing technologies have limitations in data display, and users cannot freely analyze and explore data in multiple dimensions.

[0009] Technical implementation: The technical implementation of existing reporting systems usually involves relational databases and SQL queries. Some advanced systems may use data warehouses and OLAP technologies to improve performance and analytical capabilities.

[0010] Although existing reporting technologies have met users' basic needs for data presentation to a certain extent, they have the following defects and deficiencies in terms of in-depth analysis and user customization:

[0011] 1. Lack of flexibility: Existing reporting systems are usually based on fixed data models. Users cannot freely define new data analysis dimensions or indicators according to actual needs, which limits the personalization and diversified display of reports.

[0012] 2. High customization cost: When users need special report formats or analysis logic, professional database knowledge or the intervention of developers is often required, which increases the time and economic cost of customizing reports.

[0013] 3. Performance bottleneck: When processing large-scale data sets, the existing reporting system may not be able to provide efficient data processing capabilities, resulting in slow report generation and affecting user experience.

[0014] 4. Difficult maintenance: As business needs change, existing reporting systems may require frequent maintenance and updates. Especially when the underlying data model or business logic changes, maintenance becomes complicated and error-prone.

[0015] 5. User interaction limitations: Existing reporting systems often lack intuitive interactive data analysis tools, making it difficult for users to conduct in-depth data exploration and real-time data analysis.

[0016] 6. Lack of real-time performance: In scenarios that require real-time data updates and instant report generation, existing technologies may not be able to meet the needs of rapid response, resulting in limited timeliness of report data.

[0017] 7. Data integration issues: Existing reporting systems may face compatibility and consistency issues when integrating data from different data sources, affecting the accuracy and reliability of reports.

[0018] 8. Lack of multidimensional analysis capabilities: Although some systems have adopted OLAP technology, it is still difficult for users to implement complex multidimensional data analysis and drill-down operations during use. Summary of the invention

[0019] In order to solve the existing problems, the present invention provides a method for implementing a custom report based on a minimum dimensional data cube. The specific scheme is as follows:

[0020] A method for implementing a custom report based on a minimum dimensional data cube, characterized by comprising the following steps:

[0021] S1, collect report requirements and design data models based on the requirements;

[0022] S2, preprocessing the data in step S1;

[0023] S3, build data cube;

[0024] S4, design custom reports;

[0025] S5, dynamic query construction;

[0026] S6, data aggregation and caching;

[0027] S7, multidimensional data analysis;

[0028] S8, real-time data processing;

[0029] S9, user interaction and visualization;

[0030] S10, system maintenance and update.

[0031] Preferably, designing the data model in step S1 includes defining dimensions and measures of the data cube, designing an entity relationship diagram (ERD) of the data model, and determining the hierarchical structure of the data model.

[0032] Preferably, the data preprocessing in step S2 includes: data cleaning and data conversion;

[0033] The data cleaning includes removing duplicate records and processing missing values, identifying and processing outliers, ensuring data accuracy, and verifying data consistency and completeness;

[0034] The data conversion is to convert data into a format suitable for analysis, convert non-standard data into a unified format, convert data encoding, ensure data compatibility between different systems, and prepare data for data cube construction, including data type conversion.

[0035] Preferably, the step of constructing a data cube in step S3 includes:

[0036] S31, determine the dimension: according to the data model, determine the dimension of the data cube, including time, location, and product;

[0037] S32, metric aggregation: select key metrics, including sales and costs; perform pre-aggregation operations on the selected metrics to reduce the amount of calculation during query;

[0038] S33, Data Cube Construction: Construct data cubes using multidimensional cube technology; determine the storage structure of the data cube, including star model or snowflake model; implement the physical construction process of the data cube, including data loading and index creation.

[0039] Preferably, designing a custom report in step S4 includes: interface design and parameter configuration;

[0040] The interface design is to design an intuitive user interface, allowing users to customize the report structure by dragging and dropping, and provide a prototype design of the user interface and a user interaction flow chart;

[0041] The parameter configuration allows the user to configure report parameters, including time range, organization range, and project attributes; a parameter configuration interface is designed to ensure that the user can understand and operate it.

[0042] Preferably, step S5 includes query construction and query optimization;

[0043] The query construction: according to the parameters configured by the user, the system dynamically constructs SQL query statements; implements the logical construction module of the query statement to support the query requirements of different reports;

[0044] The query optimization: optimizes the generated query statements to improve query efficiency; implements query cache strategy to reduce the load on the database.

[0045] Preferably, step S6 includes data aggregation and result caching;

[0046] The data aggregation: performing data aggregation operations to calculate the required metrics; determining the frequency and timing of the aggregation operations to balance real-time performance and system performance;

[0047] The result cache: stores the aggregated results in the cache to quickly respond to subsequent identical query requests. Design a cache invalidation strategy to ensure the freshness of the data.

[0048] Preferably, step S7 includes OLAP operation and result display;

[0049] The OLAP operations include slicing, dicing, drilling and rotating OLAP operations to meet the multidimensional data analysis needs of users, and provide user interface support so that users can easily perform these operations;

[0050] The result display: the analysis results are displayed to the user in the form of tables or charts, and the result display interface is designed to ensure that the information is clear and easy to understand.

[0051] Preferably, step S8 includes data stream access and real-time aggregation;

[0052] The data stream access: access the real-time data stream and monitor the status of the data stream in real time;

[0053] The real-time aggregation: performs aggregation operations on real-time data, updates the data cube, and ensures the accuracy and consistency of the real-time data.

[0054] Preferably, step S10 includes system monitoring and model updating;

[0055] The system monitoring: monitor system performance, ensure the efficiency and accuracy of report generation, and implement logging and error tracking mechanisms;

[0056] The model update: update the data model and report design according to business development and user feedback; design the model update process to ensure a smooth transition of the update.

[0057] The beneficial effects of the present invention are:

[0058] 1. Improve the degree of customization:

[0059] Since users can directly select or define dimensions and metrics of a report through a graphical interface, the present invention greatly improves the degree of customization of the report and meets personalized data analysis needs.

[0060] 2. Lower the technical threshold:

[0061] Through a simplified interface and drag-and-drop operation, the present invention reduces the user's reliance on professional database knowledge during report customization, thereby lowering the technical threshold.

[0062] 3. Enhanced data processing performance:

[0063] By using data cubes to pre-compute and aggregate data, combined with a caching mechanism, the present invention significantly improves the speed and performance of data processing, especially when processing large-scale data sets.

[0064] 4. Optimize system maintainability:

[0065] The system design takes maintainability into consideration. When business logic or data model changes, it can be quickly adapted without large-scale reconstruction, thus optimizing the maintainability of the system.

[0066] 5. Improve the real-time performance of report generation:

[0067] The present invention supports the processing of real-time data streams and the generation of instant reports, improves the timeliness of report data, and meets the demand for real-time data analysis.

[0068] 6. Enhance data consistency and accuracy:

[0069] Through an efficient data integration solution, the present invention ensures the consistency and accuracy of data from different data sources in the report.

[0070] 7. Enrich user interaction experience:

[0071] Interactive controls and rich data visualization components enhance the user's interactive experience during data analysis, enabling users to explore and understand data more intuitively.

[0072] 8.Support complex multi-dimensional data analysis:

[0073] The present invention supports OLAP operations including slicing, dicing, drilling and pivoting, enabling users to perform complex multidimensional data analysis.

[0074] 9. Minimize storage requirements:

[0075] By constructing a data cube of minimum dimension, the present invention reduces data redundancy, optimizes the use of storage space, and improves storage efficiency.

[0076] 10. Improve query flexibility:

[0077] Dynamic SQL query generation technology allows the system to flexibly generate queries according to user needs, adapt to various report generation scenarios, and improve the flexibility and adaptability of queries.

[0078] 11. Facilitate business decision making:

[0079] In summary, the present invention can promote faster and more accurate business decisions and help enterprises quickly respond to changes in a highly competitive market. Through causal reasoning, it can be seen that the present invention solves multiple key problems of existing reporting systems and significantly improves the flexibility, ease of use, performance and real-time performance of reporting systems, thereby providing enterprises with a powerful data analysis and report generation tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0081] Figure 1 It is a data cube structure diagram of the present invention;

[0082] Figure 2 A user interface design diagram of the present invention;

[0083] Figure 3 Build process diagrams for dimensional modeling. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The present invention builds a flexible data model and an efficient data processing mechanism, allowing users to freely define the dimensions, metrics and display formats of reports through an intuitive graphical interface. At the same time, the system can dynamically generate optimized SQL queries, realize rapid aggregation and analysis of large-scale data sets, support complex OLAP operations, and provide real-time data updates and interactive data visualization, thereby significantly improving the flexibility, performance and user experience of the reporting system and meeting the needs of enterprises for efficient data analysis and decision support.

[0086] like Figures 1 to 3 A method for implementing a custom report based on a minimum dimension data cube, characterized in that it comprises the following steps:

[0087] S1, hold meetings and interviews with various business departments to collect report requirements, determine the key performance indicators (KPIs) and necessary data dimensions of the report; record user preferences for report format and distribution channels.

[0088] Design the data model according to the requirements, define the dimensions and measures of the data cube, and ensure that they can meet the collected requirements; design the entity relationship diagram (ERD) of the data model to clarify the relationship between each data entity; determine the hierarchical structure of the data model to support complex report structures.

[0089] S2, preprocessing the data in step S1;

[0090] Data preprocessing includes: data cleaning and data conversion;

[0091] The data cleaning includes removing duplicate records and processing missing values, identifying and processing outliers, ensuring data accuracy, and verifying data consistency and completeness;

[0092] The data conversion is to convert data into a format suitable for analysis, convert non-standard data into a unified format, convert data encoding, ensure data compatibility between different systems, and prepare data for data cube construction, including data type conversion.

[0093] S3, build a data cube. The specific steps include:

[0094] S31, determine the dimension: according to the data model, determine the dimension of the data cube, including time, location, and product;

[0095] S32, metric aggregation: select key metrics, including sales and costs; perform pre-aggregation operations on the selected metrics to reduce the amount of calculation during query;

[0096] S33, Data Cube Construction: Construct data cubes using multidimensional cube technology; determine the storage structure of the data cube, including star model or snowflake model; implement the physical construction process of the data cube, including data loading and index creation.

[0097] S4, design custom reports, including: interface design and parameter configuration.

[0098] The interface design is to design an intuitive user interface, allowing users to customize the report structure by dragging and dropping, and provide a prototype design of the user interface and a user interaction flow chart;

[0099] The parameter configuration allows the user to configure report parameters, including time range, organization range, and project attributes; a parameter configuration interface is designed to ensure that the user can understand and operate it.

[0100] S5, dynamic query construction. Specifically includes query construction and query optimization;

[0101] The query construction: according to the parameters configured by the user, the system dynamically constructs SQL query statements; implements the logical construction module of the query statement to support the query requirements of different reports;

[0102] The query optimization: optimizes the generated query statements to improve query efficiency; implements query cache strategy to reduce the load on the database.

[0103] S6, data aggregation and result caching.

[0104] The data aggregation: performing data aggregation operations to calculate the required metrics; determining the frequency and timing of the aggregation operations to balance real-time performance and system performance;

[0105] The result cache: stores the aggregated results in the cache to quickly respond to subsequent identical query requests. Design a cache invalidation strategy to ensure the freshness of the data.

[0106] S7, multidimensional data analysis, including OLAP operations and result presentation.

[0107] The OLAP operations include slicing, dicing, drilling and rotating OLAP operations to meet the multidimensional data analysis needs of users, and provide user interface support so that users can easily perform these operations;

[0108] The result display: the analysis results are displayed to the user in the form of tables or charts, and the result display interface is designed to ensure that the information is clear and easy to understand.

[0109] S8, real-time data processing, including data stream access and real-time aggregation;

[0110] The data stream access: access the real-time data stream and monitor the status of the data stream in real time;

[0111] The real-time aggregation: performs aggregation operations on real-time data, updates the data cube, and ensures the accuracy and consistency of the real-time data.

[0112] S9, user interaction and visualization; specifically includes interactive controls and visualization displays.

[0113] Interactive controls: Provide interactive controls, such as sliders, drop-down menus, etc., to enable users to interact with reports. Design the interactive logic and feedback mechanism of the controls.

[0114] The visual display: dynamically updates charts and dashboards based on user operations to display data analysis results.

[0115] S10, system maintenance and update. Including system monitoring and model update.

[0116] The system monitoring: monitor system performance, ensure the efficiency and accuracy of report generation, and implement logging and error tracking mechanisms;

[0117] The model update: update the data model and report design according to business development and user feedback; design the model update process to ensure a smooth transition of the update.

[0118] Meanwhile, the technical implementation details of the present invention include:

[0119] Front-end user interface: Use modern web technology stacks, such as React or Vue.js, to build responsive and user-friendly interfaces that support cross-platform access.

[0120] Backend service: Use the high-performance backend language Java to implement report processing logic, including user request parsing, data query generation, and result aggregation.

[0121] Database interaction: Using the DAMO database, interact with the database through ORM (object-relational mapping) or direct SQL operations to ensure the security and efficiency of data operations.

[0122] Cache layer: Implement a memory cache mechanism, such as Redis, to store hot data and pre-aggregation results to reduce the number of database accesses.

[0123] Through the detailed description of the above technical solution, the invention can solve the shortcomings of the existing report system and provide a flexible, efficient and user-friendly custom report implementation method. This solution is not only applicable to the field of business intelligence, but can also be extended to other fields that require complex data analysis and report display.

[0124] The invention is mainly an efficient data analysis system, which provides users with a powerful, flexible and intuitive data exploration and analysis platform through technical processes such as demand analysis and data model design, data preprocessing, building data cubes, custom report design, dynamic query generation, data aggregation optimization, multidimensional data analysis, real-time data processing, and user interaction and visualization. The system can minimize storage requirements while providing in-depth analysis of real-time and historical data, as well as highly customized reports and visualization tools to meet the needs of different users. Through the detailed description of the above technical solution, the invention can achieve the following beneficial effects:

[0125] 1. Improve the degree of customization:

[0126] Since users can directly select or define dimensions and metrics of a report through a graphical interface, the present invention greatly improves the degree of customization of the report and meets personalized data analysis needs.

[0127] 2. Lower the technical threshold:

[0128] Through a simplified interface and drag-and-drop operation, the present invention reduces the user's reliance on professional database knowledge during report customization, thereby lowering the technical threshold.

[0129] 3. Enhanced data processing performance:

[0130] By using data cubes to pre-compute and aggregate data, combined with a caching mechanism, the present invention significantly improves the speed and performance of data processing, especially when processing large-scale data sets.

[0131] 4. Optimize system maintainability:

[0132] The system design takes maintainability into consideration. When business logic or data model changes, it can be quickly adapted without large-scale reconstruction, thus optimizing the maintainability of the system.

[0133] 5. Improve the real-time performance of report generation:

[0134] The present invention supports the processing of real-time data streams and the generation of instant reports, improves the timeliness of report data, and meets the demand for real-time data analysis.

[0135] 6. Enhance data consistency and accuracy:

[0136] Through an efficient data integration solution, the present invention ensures the consistency and accuracy of data from different data sources in the report.

[0137] 7. Enrich user interaction experience:

[0138] Interactive controls and rich data visualization components enhance the user's interactive experience during data analysis, enabling users to explore and understand data more intuitively.

[0139] 8.Support complex multi-dimensional data analysis:

[0140] The present invention supports OLAP operations including slicing, dicing, drilling and pivoting, enabling users to perform complex multidimensional data analysis.

[0141] 9. Minimize storage requirements:

[0142] By constructing a data cube of minimum dimension, the present invention reduces data redundancy, optimizes the use of storage space, and improves storage efficiency.

[0143] 10. Improve query flexibility:

[0144] Dynamic SQL query generation technology allows the system to flexibly generate queries according to user needs, adapt to various report generation scenarios, and improve the flexibility and adaptability of queries.

[0145] 11. Facilitate business decision making:

[0146] In summary, the present invention can promote faster and more accurate business decisions and help enterprises quickly respond to changes in a highly competitive market. Through causal reasoning, it can be seen that the present invention solves multiple key problems of existing reporting systems and significantly improves the flexibility, ease of use, performance and real-time performance of reporting systems, thereby providing enterprises with a powerful data analysis and report generation tool.

[0147] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

[0148] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for implementing a custom report based on a minimum dimension data cube, characterized in that: The following steps are involved: S1, collect report requirements and design data models based on the requirements; S2, preprocessing the data in step S1; S5, dynamic query construction; S6, data aggregation and caching; S7, multidimensional data analysis; S8, real-time data processing; S9, user interaction and visualization; S10, system maintenance and update.

2. The method according to claim 1, characterized in that: In step S1, the data model is designed, including defining the dimensions and measures of the data cube, designing the entity relationship diagram ERD of the data model, and determining the hierarchical structure of the data model.

3. The method according to claim 1, characterized in that: The data preprocessing in step S2 includes: data cleaning and data conversion; The data cleaning includes removing duplicate records and processing missing values, identifying and processing outliers, ensuring data accuracy, and verifying data consistency and completeness; The data conversion is to convert data into a format suitable for analysis, convert non-standard data into a unified format, convert data encoding, ensure data compatibility between different systems, and prepare data for data cube construction, including data type conversion.

4. The method according to claim 1, characterized in that: The steps of constructing the data cube in step S3 include: S31, determine the dimension: according to the data model, determine the dimension of the data cube, including time, location, and product; S32, metric aggregation: select key metrics, including sales and costs; perform pre-aggregation operations on the selected metrics to reduce the amount of calculation during query; S33, Data Cube Construction: Construct data cubes using multidimensional cube technology; determine the storage structure of the data cube, including star model or snowflake model; implement the physical construction process of the data cube, including data loading and index creation.

5. The method according to claim 1, characterized in that Designing a custom report in step S4 includes: interface design and parameter configuration; The interface design is to design an intuitive user interface, allowing users to customize the report structure by dragging and dropping, and provide a prototype design of the user interface and a user interaction flow chart; The parameter configuration allows the user to configure report parameters, including time range, organization range, and project attributes; a parameter configuration interface is designed to ensure that the user can understand and operate it.

6. The method according to claim 1, characterized in that: Step S5 includes query construction and query optimization; The query construction: according to the parameters configured by the user, the system dynamically constructs SQL query statements; implements the logical construction module of the query statement to support the query requirements of different reports; The query optimization: optimizing the generated query statements to improve query efficiency; Implement query caching strategies to reduce database load.

7. The method according to claim 1, characterized in that: Step S6 includes data aggregation and result caching; The data aggregation: performing data aggregation operations to calculate the required metrics; determining the frequency and timing of the aggregation operations to balance real-time performance and system performance; The result cache stores the aggregated results in the cache so as to quickly respond to subsequent identical query requests. Design cache invalidation strategies to ensure data freshness.

8. The method according to claim 1, characterized in that: Step S7 includes OLAP operation and result display; The OLAP operations include slicing, dicing, drilling and rotating OLAP operations to meet the multidimensional data analysis needs of users, and provide user interface support so that users can easily perform these operations; The result display: the analysis results are displayed to the user in the form of tables or charts, and the result display interface is designed to ensure that the information is clear and easy to understand.

9. The method according to claim 1, characterized in that: Step S8 includes data stream access and real-time aggregation; The data stream access: access the real-time data stream and monitor the status of the data stream in real time; The real-time aggregation: performs aggregation operations on real-time data, updates the data cube, and ensures the accuracy and consistency of the real-time data.

10. The method according to claim 1, characterized in that: Step S10 includes system monitoring and model updating; The system monitoring: monitor system performance, ensure the efficiency and accuracy of report generation, and implement logging and error tracking mechanisms; Model update: Update data model and report design based on business development and user feedback; Design the model update process to ensure a smooth transition of updates.

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