Multi-dimensional data report determination method and system

Abstract

The application provides a multi-dimensional data report determination method and system. The method comprises the following steps: obtaining a target data set input by a user; the target data set comprises a target dimension table; based on the target dimension table, concatenating and caching instances related to the target dimension table to determine an initial data cache report; based on the target data set, determining a target query statement; and based on the initial data cache report, determining a target multi-dimensional data report according to the target query statement. The multi-dimensional data report can be generated without the participation of a program coding personnel, the communication cost of demand transmission between a business analysis personnel and the program coding personnel can be effectively reduced, and the efficiency of multi-dimensional data analysis is improved.

Description

Technical Field

[0001] This invention relates to the field of database technology, and in particular to a method and system for determining multidimensional data reports. Background Technology

[0002] With the continuous improvement of data acquisition technology and the decreasing cost of storage media, the use of multidimensional data models to manage production data is being increasingly accepted by various industries. Multidimensional data models store data of extremely high value to business operators, and the results of multidimensional statistical analysis are often used as a basis for production and marketing decisions. However, conducting business analysis on multidimensional data requires both business knowledge and coding skills.

[0003] However, in existing technologies, business knowledge and coding skills are often assigned to different roles. Business analysts are not responsible for writing query programs, and programmers, lacking business background, have difficulty understanding the requirements for dimensional correlation. The process of developing a multidimensional correlation statistical analysis requirement often requires multiple rounds of requirement confirmation and coding debugging. When statistical tasks are transferred from business analysts to programmers lacking business background knowledge, significant communication costs may be incurred on key issues, resulting in a substantial waste of human resources and time.

[0004] Therefore, how to provide a method and system for determining multidimensional data reports that can generate multidimensional data reports without the involvement of programmers, reduce the communication costs of demand transmission between business analysts and programmers, and improve the efficiency of multidimensional data analysis has become an urgent problem to be solved. Summary of the Invention

[0005] The multidimensional data report determination method and system provided by this invention are used to solve the above-mentioned problems in the prior art, reduce the communication cost of requirement transmission between business analysts and programmers, and improve the efficiency of multidimensional data analysis.

[0006] This invention provides a method for determining multidimensional data reports, comprising:

[0007] Obtain the target dataset input by the user; wherein, the target dataset includes: a target dimension table;

[0008] Based on the target dimension table, the instances related to the target dimension table are chained together and cached to determine the initial data cache report;

[0009] Based on the target dataset, determine the target query statement;

[0010] Based on the initial data cache report, the target multidimensional data report is determined according to the target query statement.

[0011] According to the multidimensional data report determination method provided by the present invention, the target dataset further includes: a target fact table, a target fact field list, a target aggregation function, a target aggregation dimension, and a target time granularity;

[0012] The target fact field list includes several fact fields corresponding to the target fact table; the target aggregation function is the aggregation rule when aggregating the target fact fields; the target aggregation dimension is the dimension corresponding to the generation of the target multidimensional data report; and the target time granularity is the time granularity corresponding to the generation of the target multidimensional data report.

[0013] According to the multidimensional data report determination method provided by the present invention, the step of obtaining the target dataset input by the user specifically includes:

[0014] Obtain target dimension information input by the user; wherein, the target dimension information includes: a starting dimension and an ending dimension;

[0015] Based on the starting dimension and the ending dimension, determine the set of dimension chains;

[0016] A first interface is generated according to a preset dimensional chain arrangement rule; wherein, the first interface displays a set of dimensional chains arranged according to the preset dimensional chain arrangement rule;

[0017] Obtain the target dimension table input by the user on the first interface; wherein, the target dimension table is the target dimension chain determined by the user in the dimension chain set.

[0018] According to the multidimensional data report determination method provided by the present invention, the target dimension information further includes: at least one intermediate dimension;

[0019] The process of determining a set of dimension chains based on the starting dimension and the ending dimension, and generating a first interface according to a preset dimension chain arrangement rule, specifically includes:

[0020] According to the order of the starting dimension, the intermediate dimension, and the ending dimension, the set of dimension chains is determined based on the path generation algorithm;

[0021] Based on the set of dimensional chains, a first interface is generated according to a preset dimensional chain arrangement rule;

[0022] The first interface displays a set of dimension chains arranged according to the preset dimension chain arrangement rules. The preset dimension chain arrangement rules are as follows: the fewer dimensions a dimension chain includes, the higher its priority; and among dimension chains with the same number of dimensions, the higher the number of instances, the higher its priority.

[0023] According to the multidimensional data report determination method provided by the present invention, the step of obtaining the target dataset input by the user specifically includes:

[0024] Obtain the target dimension table entered by the user on the first interface;

[0025] Based on each dimension in the target dimension table, a second interface is generated according to a preset dimension arrangement order; wherein, the second interface displays a list of dimensions arranged according to the preset dimension arrangement order;

[0026] Obtain the target aggregation dimension input by the user on the second interface; wherein the target aggregation dimension is the target dimension determined in the dimension list.

[0027] According to the multidimensional data report determination method provided by the present invention, the step of determining the target query statement based on the target dataset specifically includes:

[0028] Based on the target dimension table, the target fact table, and the target fact field list, a query statement fragment for associating the dimension table with the fact table is determined; wherein, the query statement fragment for associating the dimension table with the fact table is used to associate the dimension table and the fact table in the initial data cache report;

[0029] Based on the target fact field list and the target aggregation function, a fact table indicator query statement fragment is determined; wherein, the fact table indicator query statement fragment is used to determine the aggregation rules corresponding to the fact fields in the initial data cache report;

[0030] Based on the target aggregation dimension and the target time granularity, an aggregation statement fragment is determined; wherein, the aggregation statement fragment is used to determine the corresponding dimension and time granularity when determining the target multidimensional data report;

[0031] Based on the query fragments relating the dimension table and the fact table, the query fragments relating the fact table metrics, and the aggregation fragments, the target query statement is determined.

[0032] According to the multidimensional data report determination method provided by the present invention, the target dataset further includes: a target time range;

[0033] Based on the target fact field list and the target aggregation function, determine the fact table indicator query statement fragment, specifically including:

[0034] Based on the target fact field list and the target aggregation function, determine the initial fact table indicator query statement fragment;

[0035] The fact table indicator query statement fragment is determined by limiting the start and end times of the data in the initial fact table indicator query statement fragment according to the target time range.

[0036] The present invention also provides a multi-dimensional data report determination system, comprising: an input data acquisition unit, a cached report determination unit, a query statement determination unit, and a target report determination unit;

[0037] The input data acquisition unit is used to acquire the target dataset input by the user; wherein, the target dataset includes: a target dimension table;

[0038] The cache report determination unit is used to determine the initial data cache report by concatenating and caching instances related to the target dimension table based on the target dimension table.

[0039] The query statement determination unit is used to determine the target query statement based on the target dataset;

[0040] The target report determination unit is used to determine the target multidimensional data report based on the initial data cache report and the target query statement.

[0041] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the multidimensional data report determination method described above.

[0042] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multidimensional data report determination method described above.

[0043] The multidimensional data report determination method and system provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines the target query statement based on computational logic. By determining the initial data cache report and target query statement based on data input by business analysts, multidimensional data reports can be generated without the involvement of programmers, reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the multidimensional data report determination method provided by the present invention;

[0046] Figure 2 This is a schematic diagram of the multidimensional data report determination method provided by the present invention;

[0047] Figure 3 This is a schematic diagram of the structure of the multidimensional data report determination system provided by the present invention;

[0048] Figure 4 This is a schematic diagram of the physical structure of the electronic device provided by the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0050] Communication costs include not only human resources but also time. Increased communication costs can severely impact a company's competitiveness. Based on experience, the main communication stages that may incur significant costs include: conveying the scope of communication and conveying the methods for summarizing and calculating data.

[0051] (1) Dimension range communication: Data users and program developers need to reach a consensus on the range of statistical dimensions in multiple dimensions. For example, when aggregating "logical volumes" according to "business systems", the routing algorithm can output several selectable association schemes based on the head dimension "business systems" and the tail dimension "logical volumes" as input parameters, but both parties still need to repeatedly confirm before finally selecting a scheme.

[0052] For example, Option 1 associates "business system" with "virtual machine" with "logical volume". Option 2 associates "business system" with "physical server" with "storage pool" with "logical volume". Both options can find the "logical volume" based on the "business system", but the sets of "logical volumes" obtained by the two options are significantly different, resulting in incorrect IO latency data in the final calculation.

[0053] (2) To convey the summary calculation method, data users and program developers need to reach a consensus on the calculation rules and aggregation methods of fact fields.

[0054] For example, details such as which field in which fact table the "IO latency" is taken from, whether unit conversion is needed, whether the data from multiple days is aggregated into weekly granularity by calculating the average or the maximum value, and whether the data from multiple logical volumes is aggregated into business system granularity by calculating the average or the maximum value, need to be clarified by the data requester to the program developer. However, since the data requester does not have direct access to the raw data, the communication cost will increase rapidly with the complexity of the requirement.

[0055] To address the aforementioned issues and improve the efficiency of constructing multidimensional data reports, the following two methods exist in the prior art:

[0056] Method 1: Indicator calculation method in a big data environment, including proposing the concept of separating indicator calculation scheduling code and business calculation SQL, persisting the SQL set for calculating indicators to the database, and associating table names with calculation SQL sets through indicator calculation information.

[0057] This method for calculating metrics can essentially achieve the definition of simple statistical metrics with zero code, reducing communication costs between business analysts and software developers regarding statistical requirements to some extent. However, this solution lacks optimization for describing dimension ranges in complex, multi-dimensional scenarios. It only proposes the concept of separating computation scheduling code from business computation SQL, without disclosing how to create computation code that is separated from business logic, and without optimizing the method for describing dimension ranges in complex, multi-dimensional scenarios.

[0058] Method 2: Model building method. The construction tasks of each basic indicator are executed in parallel to generate several basic indicator tables. Time series analysis is performed on the construction tasks of each summary indicator to determine the fastest construction path of each summary indicator. The construction tasks of each summary indicator are executed according to each fastest construction path to generate several summary indicator tables. A data model is built based on each basic indicator table and each summary indicator table.

[0059] While a description of a method for summarizing metrics by grouping metrics based on data sources and aggregating reports has been published, it does not achieve a true separation of business logic and coding. Furthermore, this solution requires strong coupling with business requirements for implementation. Coding development still needs to be performed by personnel lacking business background. The implementation of this tightly coupled solution introduces continuously increasing communication costs, significantly reducing the method's practicality and making it impossible to avoid risks arising from communication issues.

[0060] In summary, none of the methods can completely separate business logic from computational logic in multi-dimensional data statistical analysis scenarios. Even with non-programmed methods for determining multi-dimensional data reports, cost losses due to communication issues remain unavoidable. To address these problems, this invention provides a method and system for determining multi-dimensional data reports.

[0061] Before providing a detailed description of the present invention, the relevant concepts involved in the present invention will first be explained.

[0062] A multidimensional data model is a data organization method that organizes data into a cube-like structure around a central theme. This theme is represented by a fact table, and the facts are measured numerically. This cube allows for multidimensional data modeling and observation, defined by dimensions and facts.

[0063] A dimension is a perspective or viewpoint that an organization wants to record. Each dimension is associated with a table called a dimension table. A dimension table describes the attributes of a dimension.

[0064] A fact is a data metric, a numerical measure of the data to be examined. A fact table includes the name or metric of the fact and the key of each related dimension table.

[0065] Multidimensional data models are categorized based on the organization of dimensions and facts into: star schema, snowflake schema, and fact constellation schema.

[0066] Figure 1 This is a flowchart of the multidimensional data report determination method provided by the present invention, as follows: Figure 1 As shown, the present invention provides a method for determining multidimensional data reports, comprising:

[0067] Step S1: Obtain the target dataset input by the user; wherein, the target dataset includes: a target dimension table;

[0068] Step S2: Based on the target dimension table, concatenate and cache the instances related to the target dimension table to determine the initial data cache report;

[0069] Step S3: Based on the target dataset, determine the target query statement;

[0070] Step S4: Based on the initial data cache report, determine the target multidimensional data report according to the target query statement.

[0071] It should be noted that the above method can be implemented by computer equipment.

[0072] Optionally, an input interface can be set up on the front end. When it is determined that a multidimensional data report needs to be generated, the business statistical analyst (user) can use the front end interface to perform simple "drag and drop" operations and submit a form to input the corresponding target dataset. The target dataset includes: the target dimension table.

[0073] According to the backend computing logic, in step S1, the target dataset input by the user on the front end is obtained.

[0074] In step S2, based on the target dimension table obtained in step S1, the dimension data is preprocessed in the background according to the relationship between the preceding and following dimensions in the target dimension table. The corresponding related instances of the entire target dimension table are linked into multiple rows of records and cached through a relational database to determine the initial data cache report.

[0075] In step S3, based on the purpose of each data in the target dataset and according to the calculation rules of the background operation logic, an executable target query statement that conforms to the SQL (Structured Query Language) language specification is determined.

[0076] In step S4, based on the initial data cache report determined in step S2, the target query statement determined in step S3 is executed in the database, which can output statistical analysis results that meet the user's needs, namely the target multidimensional data report.

[0077] It is understandable that the specific list of data contained in the target dataset input by the user, as well as how to comprehensively determine the calculation logic of the target query statement and the corresponding specific SQL statement based on each data in the target dataset, can all be set according to actual needs, and this invention does not limit this.

[0078] Secondly, this invention can be widely applied to different database categories. In specific applications of this invention, different databases and corresponding generated statements can be adjusted according to the actual situation, and this invention does not limit them.

[0079] It should be noted that, in this invention, the specific screen displayed on the front-end interface and the specific input method (e.g., entering data in the input box after the corresponding data, or selecting the corresponding data in the corresponding option box, etc.) can be set according to the actual situation, and this invention does not limit them.

[0080] Secondly, it's understandable that determining the corresponding target query based on the data in the target dataset is a key step in achieving no-code implementation. In multi-dimensional analysis scenarios, the data in the target dataset is independent of specific business logic. After processing with pre-defined computational logic, it can be used to describe the dimensional range in multi-dimensional scenarios, thereby generating the target dimensional data table. This approach can be widely applied to complex relational scenarios.

[0081] The multidimensional data report determination method provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines the target query statement based on computational logic. By determining the initial data cache report and the target query statement based on data input by business analysts, multidimensional data reports can be generated without the involvement of programmers, reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis.

[0082] Furthermore, in one embodiment, according to the multidimensional data report determination method provided by the present invention, the target dataset further includes: a target fact table, a target fact field list, a target aggregation function, a target aggregation dimension, and a target time granularity;

[0083] The target fact field list includes several fact fields corresponding to the target fact table; the target aggregation function is the aggregation rule when aggregating the target fact fields; the target aggregation dimension is the dimension corresponding to the generation of the target multidimensional data report; and the target time granularity is the time granularity corresponding to the generation of the target multidimensional data report.

[0084] Optionally, the target dataset may also include: a target dimension table, a target fact table, a target fact field list, a target aggregation function, a target aggregation dimension, and a target time granularity.

[0085] In the target dataset, the target fact field list includes several fact fields corresponding to the target fact table; the target aggregation function is the aggregation rule when aggregating the target fact fields; the target aggregation dimension is the dimension corresponding to the generation of the target multidimensional data report; and the target time granularity is the time granularity corresponding to the generation of the target multidimensional data report.

[0086] The backend can generate executable target query statements that conform to SQL language specifications according to the purpose of each data in the target dataset and the preset calculation logic.

[0087] It is understandable that the calculation logic of the target query statement and the corresponding specific SQL statement can be determined based on the data in the target dataset, and can be set according to the actual situation. This invention does not limit this.

[0088] The multidimensional data report determination method provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. The target query statement is determined according to the calculation logic based on the target dimension table, target fact table, target fact field list, target aggregation function, target aggregation dimension, and target time granularity. By determining the initial data cache report and target query statement based on data input by business analysts, multidimensional data reports can be generated without the involvement of programmers, reducing the communication costs of requirement transfer between business analysts and programmers and improving the efficiency of multidimensional data analysis.

[0089] Furthermore, in one embodiment, according to the multidimensional data report determination method provided by the present invention, obtaining the target dataset input by the user specifically includes:

[0090] Obtain target dimension information input by the user; wherein, the target dimension information includes: a starting dimension and an ending dimension;

[0091] Based on the starting dimension and the ending dimension, determine the set of dimension chains;

[0092] A first interface is generated according to a preset dimensional chain arrangement rule; wherein, the first interface displays a set of dimensional chains arranged according to the preset dimensional chain arrangement rule;

[0093] Obtain the target dimension table input by the user on the first interface; wherein, the target dimension table is the target dimension chain determined by the user in the dimension chain set.

[0094] Optionally, the steps for obtaining the target dataset input by the user on the front end include:

[0095] Obtain the target dimension information input by the user. For example, list the dimensions in the multidimensional model through the front-end interface, and the user selects several dimensions from them, including: a starting dimension and an ending dimension.

[0096] Based on the user-selected start and end dimensions, the start dimension is used as the first dimension of the dimension chain, and the end dimension is used as the last dimension of the dimension chain. All dimension chains that satisfy this arrangement are determined, and all dimension chains are combined into a dimension chain set.

[0097] It should be noted that, based on the correlation between different dimensions, several dimensional chains can be determined by only identifying the starting and ending dimensions. For example, dimensions may include: hardware resource pool, data center, hardware, server, and virtual machine, with the starting dimension being the hardware resource pool and the ending dimension being the virtual machine. Dimensional chains such as hardware resource pool-hardware-virtual machine, and hardware resource pool-server-virtual machine may appear.

[0098] It is understandable that, based on the correlation of all dimensions, and after determining the starting and ending dimensions, the corresponding dimensional chain can be automatically determined by the associated path generation algorithm. The specific path generation algorithm used is not limited in this invention.

[0099] Secondly, it is understandable that the starting dimension and the ending dimension can be the same dimension. For example, if both the starting dimension and the ending dimension are inputs of virtual machines, then there will only be one dimension chain containing only virtual machines. The specific set of dimension chains is determined based on the input starting dimension, ending dimension, and the correlation between dimensions, and this invention does not impose any limitations on this.

[0100] All dimension chains in the dimension chain set are displayed in a certain order on the first interface according to the preset dimension chain arrangement rules. The first interface can also set the corresponding method for users to select dimension chains (for example, users can check one dimension chain to submit, or long press to select, or enter the corresponding dimension chain number, etc.), so that users can select one as the target dimension chain from all the dimension chains.

[0101] It should be noted that the preset dimension chain sorting rules can be set according to the actual situation. For example, they can be sorted in descending or ascending order according to the number of dimensions in the dimension chain, or sorted according to the pronunciation order of the dimension names. The specific sorting rules can be set according to actual needs, and this invention does not limit them.

[0102] Obtain the target dimension chain entered by the user on the first interface, and determine the corresponding target dimension table by the relationship between the preceding and following dimensions in the dimension chain.

[0103] The multidimensional data report determination method provided by this invention, based on a user-input target dataset, allows the user to select a specified dimension range in a coding-free manner. It determines an initial data cache report by concatenating and caching instances related to the target dimension table. The target query statement is then determined based on computational logic. Business analysts can determine the initial data cache report based on the input data using standardized methods (such as selecting configuration items from a dropdown list), and automatically generate the corresponding target query statement code. This generates multidimensional data reports without the need for programmers, significantly reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis.

[0104] Furthermore, in one embodiment, according to the multidimensional data report determination method provided by the present invention, the target dimension information further includes: at least one intermediate dimension;

[0105] The set of dimension chains that satisfy the arrangement order of the dimensions is determined based on the starting dimension and the ending dimension;

[0106] Based on the preset dimensional chain arrangement rules, the first interface is generated, which specifically includes:

[0107] According to the order of the starting dimension, the intermediate dimension, and the ending dimension, the set of dimension chains is determined based on the path generation algorithm;

[0108] Based on the set of dimensional chains, a first interface is generated according to a preset dimensional chain arrangement rule;

[0109] The first interface displays a set of dimension chains arranged according to the preset dimension chain arrangement rules. The preset dimension chain arrangement rules are as follows: the fewer dimensions a dimension chain includes, the higher its priority; and among dimension chains with the same number of dimensions, the higher the number of instances, the higher its priority.

[0110] Optionally, the target dimension information may also include: at least one intermediate dimension. Users can specify several intermediate dimensions according to their actual needs. Intermediate dimensions can reduce the number of dimension chains in the dimension chain set, reduce the difficulty for users to select the target dimension chain, and reduce the time required.

[0111] Based on the starting and ending dimensions, the steps of determining the set of dimension chains that satisfy the dimension arrangement order, and generating the first interface according to the preset dimension chain arrangement rules, specifically include:

[0112] Following the order of starting dimension, intermediate dimension, and ending dimension, with the starting dimension as the first dimension of the dimension chain, the ending dimension as the last dimension of the dimension chain, and the intermediate dimension as the middle part of the dimension chain, several dimension chains that meet the conditions are determined according to the path generation algorithm, thus determining the set of dimension chains.

[0113] Furthermore, it is understood that when a user inputs multiple intermediate dimensions, they can simultaneously determine that the dimension chain can contain only one or a preset number of intermediate dimensions. Alternatively, they can set whether multiple intermediate dimensions need to satisfy a certain order relationship. The specific input method for intermediate dimensions can be adjusted according to the actual situation, and this invention does not limit it.

[0114] It should be noted that, based on the correlation of all dimensions, and after determining the starting dimension, ending dimension, and intermediate dimension, the corresponding dimensional chain can be automatically determined by associating the path generation algorithm. The specific path generation algorithm used is not limited in this invention.

[0115] Based on the set of dimension chains, according to the preset dimension chain arrangement rules, the dimension chains are sorted in descending order of priority (or ascending order of priority) according to the order of the fewer dimensions included in the dimension chain, the higher the priority, and the higher the number of instances in the dimension chain with the same number of dimensions, the higher the priority. The dimension chains are then enumerated and listed in the first interface.

[0116] It is understandable that the fewer dimensions a dimensional chain includes and the higher the number of corresponding instances, the wider the road corresponding to the path and the higher the path matching degree.

[0117] It should be noted that when arranging dimension chains, different dimension chains may have the same priority. Further, the dimension chains can be arranged in descending order of the number of characters in their names. The arrangement method used in this special case can be adjusted according to the actual situation, and this invention does not limit it.

[0118] The multidimensional data report determination method provided by this invention, based on the target dataset input by the user, determines the starting dimension, ending dimension, and intermediate dimensions input by the user, determines a set of dimension chains, and allows the user to select the target dimension chain from the set of dimension chains. This allows the user to complete the selection of a specified dimension range in a coding-free manner. An initial data cache report is determined by chaining and caching instances related to the target dimension table. The target query statement is then determined based on the calculation logic. Business analysts can determine the initial data cache report based on the input data using standardized methods (e.g., selecting configuration items from a dropdown list), and automatically generate the corresponding target query statement code. This generates multidimensional data reports without the involvement of programmers, greatly reducing the communication costs between business analysts and programmers in conveying requirements and improving the efficiency of multidimensional data analysis. Further, in one embodiment, according to the multidimensional data report determination method provided by this invention, obtaining the target dataset input by the user specifically includes:

[0119] Obtain the target dimension table input by the user on the first interface, and then process the data according to each dimension in the target dimension table.

[0120] A second interface is generated by arranging dimensions in a preset order; wherein, the second interface displays a list of dimensions arranged according to the preset order.

[0121] Obtain the target aggregation dimension input by the user on the second interface; wherein the target aggregation dimension is the target dimension determined in the dimension list.

[0122] Optionally, the target aggregation dimension is the dimension that corresponds to the final target multidimensional data report when it is generated. The steps of determining the target aggregation dimension from the target dataset input by the user specifically include:

[0123] Obtain the target dimension table entered by the user on the first interface, sort the dimensions in the target dimension table according to a preset dimension order (e.g., sort in descending or ascending order according to the relationship between dimensions), and display the dimension list sorted in the preset dimension order on the second interface so that the user can aggregate the target dimensions according to the second interface.

[0124] Retrieve the target dimension selected by the user from the dimension list displayed on the second interface, and use the target dimension selected by the user as the target aggregation dimension when finally determining the target multidimensional data report.

[0125] The multidimensional data report determination method provided by this invention, based on a user-input target dataset, completes specified dimension range and specified summary calculations (statistical data is summarized according to program statements to generate the target data report) in a coding-free manner. It determines the initial data cache report by chaining and caching instances related to the target dimension table. The target query statement is then determined based on the calculation logic. Based on the data input by business analysts, the initial data cache report and related SQL target query statements can be determined according to the backend calculation logic, generating multidimensional data reports without the involvement of programmers. This reduces the communication costs of requirement transfer between business analysts and programmers, improving the efficiency of multidimensional data analysis. Furthermore, by inputting the dimension combination path (determining the target dimension chain) and the target aggregation dimension, it can generate query statement fragments for the dimension data table, thus accurately and unambiguously describing the dimension range, and has a wider range of applications compared to existing technologies.

[0126] Furthermore, in one embodiment, according to the multidimensional data report determination method provided by the present invention, determining the target query statement based on the target dataset specifically includes:

[0127] Based on the target dimension table, the target fact table, and the target fact field list, a query statement fragment for associating the dimension table with the fact table is determined; wherein, the query statement fragment for associating the dimension table with the fact table is used to associate the dimension table and the fact table in the initial data cache report;

[0128] Based on the target fact field list and the target aggregation function, a fact table indicator query statement fragment is determined; wherein, the fact table indicator query statement fragment is used to determine the aggregation rules corresponding to the fact fields in the initial data cache report;

[0129] Based on the target aggregation dimension and the target time granularity, an aggregation statement fragment is determined; wherein, the aggregation statement fragment is used to determine the corresponding dimension and time granularity when determining the target multidimensional data report;

[0130] Based on the query fragments relating the dimension table and the fact table, the query fragments relating the fact table metrics, and the aggregation fragments, the target query statement is determined.

[0131] Optionally, the target query statement can be divided into three statement fragments by preset rules, including: a query statement fragment for joining dimension tables and fact tables, a query statement fragment for joining dimension tables and fact tables, and a query statement fragment for fact table metrics.

[0132] Figure 2 This is a schematic diagram of the multidimensional data report generation method provided by the present invention, such as... Figure 2 As shown, the steps to determine the target query statement based on the target dataset specifically include:

[0133] Based on the target dimension table, target fact table, and target fact field list, the desired pairing field groups between the dimensions and fact tables in the initial data cache report can be determined when generating the target multidimensional data report based on the initial data cache report. After determining the pairing field groups, the calculation rule generation unit will connect the initial data cache report and the indicator table (field list) using a JOIN...ON (join query) SQL statement to determine the join query statement fragment between the dimension table and the fact table.

[0134] Based on the target fact field list and target aggregation function, the fact fields are used as the source of the indicator values ​​to be statistically analyzed. For each fact field and the corresponding target aggregation function input by the front-end interface, the fact fields are grouped by the fact table and concatenated with the aggregation function to form an indicator list. The indicator query statement fragment of the fact table is determined, that is, the statement fragment for querying indicators from the fact table: SELECT [indicator list...] FROM [fact table]. For example, AVG(IO latency) represents the "average IO latency" indicator.

[0135] It should be noted that aggregation functions include, but are not limited to, SUM (summation), COUNT (counting), DISTINCT_COUNT (duplicate counting), MAX (maximum value), MIN (minimum value), and AVG (average value). AVG is converted into two atomic aggregation functions, SUM and COUNT, for further processing. Each fact field corresponds to one aggregation function. When a user selects an aggregation function, they are specifying the aggregation method to be followed when performing folded aggregations on that fact field.

[0136] It is understandable that when a user inputs target fact fields through the front-end interface, they are selecting a list of fields from the corresponding target fact table. This selection could be achieved by the front-end listing the fields corresponding to the fact table, from which the user selects one or more fields to generate the target field list.

[0137] Users can determine the target fact fields based on the target dimension list after it has been determined. Alternatively, they can determine the target fact fields at the same time as the target dimension list. In this case, there may be situations where the fact fields selected by the user do not match the target dimension list. When generating the target multidimensional data report, mismatched fact fields will not be considered.

[0138] Based on the target aggregation dimension (the view observation target during statistical analysis) and the target time granularity, determine the dimension and time granularity that the user expects when determining the target multidimensional data report. After determining the statement corresponding to the time granularity, add GROUP BY (grouping and summarizing) to the end of the overall related query statement to determine the aggregation statement fragment.

[0139] It is understood that the time granularity can be selected from year, month, day, and hour, etc., depending on the actual situation, and this invention does not limit it.

[0140] After identifying the three statement fragments, the statement fragments are combined according to preset rules to determine the target query statement.

[0141] For example, by concatenating and merging the target query statement using the combination of [a query fragment involving the dimension table and the fact table] + [a query fragment involving the fact table metrics] + [a convergence query fragment], a executable statement conforming to SQL language specifications is obtained. Executing this statement in the database will output the statistical analysis results required by the user; the target output corresponds to the queried target multidimensional data report.

[0142] It should be noted that the specific SQL statements corresponding to the dimension table and fact table association query statement fragments, the dimension table and fact table association query statement fragments, and the fact table indicator query statement fragments, as well as the method of concatenating the three statement fragments, can all be adjusted according to the actual situation, and this invention does not limit them.

[0143] The multidimensional data report determination method provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines fact table association query statements, dimension table and fact table association query statement fragments, and fact table indicator query statement fragments based on computational logic. Furthermore, it determines the SQL statement formula to be directly executed in the database based on enumeration configuration input. These three statement fragments are then concatenated to obtain the target query statement, improving the convenience, ease of use, and reusability of this invention. By determining the initial data cache report and target query statement based on data input by business analysts, without the involvement of programmers, business statistical analysts can independently perform multidimensional model statistical analysis and generate multidimensional data reports. This reduces the communication costs of requirement transfer between business analysts and programmers, improving the efficiency of multidimensional data analysis. It can be widely applied to multidimensional analysis scenarios with complex business logic, such as cloud computing operations and maintenance, and consumer market user operations.

[0144] Furthermore, in one embodiment, according to the multidimensional data report determination method provided by the present invention, the target dataset further includes: a target time range;

[0145] Based on the target fact field list and the target aggregation function, determine the fact table indicator query statement fragment, specifically including:

[0146] Based on the target fact field list and the target aggregation function, determine the initial fact table indicator query statement fragment;

[0147] The fact table indicator query statement fragment is determined by limiting the start and end times of the data in the initial fact table indicator query statement fragment according to the target time range.

[0148] Optionally, the target dataset may also include: a target time range; users can select the start and end times for data statistics using date and time range selection tools provided in the front-end interface.

[0149] After determining the time range, the steps for determining the fact table metric query statement fragments based on the target fact field list and target aggregation function include:

[0150] Based on the target fact field list and the target aggregation function, determine the initial fact table indicator query statement fragment.

[0151] Based on the target time range, a WHERE qualifier is added to the end of the initial fact table metric query statement fragment to specify the start and end times of the data, thus determining the fact table metric query statement fragment. Further, the final target query statement is determined.

[0152] Understandably, when the target dataset does not include the target time range, the final target multidimensional data report includes all data that meets the filtering criteria. When the target time range is included, only data within the target time range is retained.

[0153] It should be noted that adding the WHERE qualifier to the end of the initial fact table index query statement fragment is only a specific example of a program statement to illustrate the present invention. In addition, other statements can be used to specify the start and end time of the data, and the present invention does not limit this.

[0154] Taking the procedure of a specific multidimensional data report determination method as an example, the multidimensional data report determination method provided by this invention will be explained and illustrated:

[0155]

[0156]

[0157] The user input target timeline is: data center - server - virtual machine. The initial data cache report (TEMP-OC-PATH-PRO-KXFT-2739) is determined, with days as the time granularity and data center as the target aggregation dimension. If the fact table includes data from 10 data centers, each data center includes 100 servers, and each server corresponds to 10 virtual machines.

[0158] Based on this statement, the average peak CPU usage of virtual machines in each of the 10 data centers can be determined within the period from 00:00:00 on March 1, 2021 to 00:00:00 on March 2, 2021.

[0159] The multidimensional data report determination method provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines the target query statement based on computational logic. By determining the initial data cache report and the target query statement based on data input by business analysts and limiting the start and end times of data in the target multidimensional data report, this method generates multidimensional data reports without the involvement of programmers, reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis.

[0160] Furthermore, it is understandable that after generating the target multidimensional data report, users can also make changes to any one or more items in the target dataset.

[0161] Because in this invention, the step of determining the initial data cache report based on the target dimension table,

[0162] The steps for determining the query fragments that associate dimension tables and fact tables based on the target dimension table and the target fact field list, the steps for determining the query fragments of fact table metrics based on the target fact field list and / or target aggregation functions, and the steps for determining the aggregation statement fragments based on the corresponding updates of target aggregation dimensions and target time granularity are independent of each other. When it is determined that only some input data has been changed, only the steps corresponding to the updated data need to be completed before the target query is re-determined, without having to re-execute all the steps.

[0163] It is understandable that the steps for updating and generating the initial data cache report, updating and determining the query fragments for the dimension table and the fact table, updating and determining the query fragments for the fact table metrics, and updating and determining the aggregation statement fragments are the same as the steps for generating the corresponding information in this solution, and will not be elaborated on here.

[0164] For example, if you initially wanted to check the overall data center metrics, but later discovered an anomaly in the data of a specific data center and wanted to check the status at the device level, you only need to re-execute the step of determining the aggregation statement fragment, changing the aggregation dimension from "data center" to "device" as the input, and then redetermine the target query statement to obtain the result data corresponding to the new requirements.

[0165] Understandably, given the input target time range, if the analysis time interval can be modified, then it is only necessary to redetermine the start and end time limits corresponding to the initial fact table indicator query statement fragment according to the latest requirements, and redetermine the target query statement to obtain the result data corresponding to the new requirements.

[0166] The multidimensional data report determination method provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines fact table association query statements, dimension table and fact table association query statement fragments, and fact table indicator query statement fragments based on computational logic, and concatenates these three statement fragments to obtain the target query statement. This improves the convenience, ease of use, and reusability of the invention. Determining the initial data cache report and target query statement based on data input by business analysts eliminates the need for programmers. Business statistical analysts can independently perform multidimensional model statistical analysis and generate multidimensional data reports, reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis. This method can be widely applied to multidimensional analysis scenarios with complex business logic, such as cloud computing operations and maintenance, and consumer market user operations. Furthermore, it enables rapid analysis of statistical data and is suitable for scenarios where analysis needs iterate rapidly with business requirements, such as financial market analysis and public opinion analysis.

[0167] Taking the generation process of a specific target multidimensional data report as an example, the method for determining multidimensional data reports provided by this invention will be explained and illustrated:

[0168] The user defines the target dimension chain as: Country-Province-City-District, and defines fact tables for temperature change, precipitation change, and air quality indicators (the data in the fact tables changes over time, and the spatial granularity of the fact tables is district). The target fact field list includes: temperature and precipitation. The aggregation function for temperature is to find the maximum value, and the aggregation function for precipitation is to find the minimum value. The target aggregation dimension is city, the target time granularity is day, and the target time range is from January 1, 2020 to January 1, 2021.

[0169] Finally, based on the generated target query, a multidimensional data report can be generated for each city during the period from January 1, 2020 to January 1, 2021, showing the maximum temperature and minimum precipitation for each day.

[0170] It should be noted that the above scheme is only used as an example to illustrate the present invention. Specific data and values ​​can be adjusted according to actual conditions, and the present invention does not limit them.

[0171] Figure 3 This is a schematic diagram of the structure of the multidimensional data report generation system provided by the present invention, as shown below. Figure 3 As shown, the present invention also provides a multi-dimensional data report determination system, including: an input data acquisition unit 310, a cached report determination unit 320, a query statement determination unit 330, and a target report determination unit 340;

[0172] The input data acquisition unit 310 is used to acquire the target dataset input by the user; wherein, the target dataset includes: a target dimension table;

[0173] The cache report determination unit 320 is used to determine the initial data cache report by concatenating and caching instances related to the target dimension table based on the target dimension table.

[0174] The query statement determination unit 330 is used to determine the target query statement based on the target dataset;

[0175] The target report determination unit 340 is used to determine the target multidimensional data report based on the initial data cache report and the target query statement.

[0176] Optionally, an input interface can be set up on the front end. When it is determined that a multidimensional data report needs to be generated, the business statistical analyst (user) can use the front end interface to perform simple "drag and drop" operations and submit a form to input the corresponding target dataset. The target dataset includes: the target dimension table.

[0177] According to the backend calculation logic, the input data acquisition unit 310 is used to acquire the target dataset input by the user on the front end.

[0178] The cache report determination unit 320 is used in the background to preprocess the dimension data based on the target dimension table obtained in the input data acquisition unit 310 and the relationship between the preceding and following dimensions in the target dimension table, to string together the corresponding related instances of the entire target dimension table into multiple rows of records, and to cache them through a relational database, thereby determining the initial data cache report.

[0179] The query statement determination unit 430 is used to determine an executable target query statement that conforms to the SQL (Structured Query Language) language specification based on the purpose of each data in the target dataset and the calculation rules of the background operation logic.

[0180] The target report determination unit 340 is used to cache reports based on the initial data determined in the cache report determination unit 320, and execute the target query statement determined in the query statement determination unit 330 in the database, so as to output statistical analysis results that meet the user's needs, i.e., the target multidimensional data report.

[0181] It is understandable that the specific list of data contained in the target dataset input by the user, as well as how to comprehensively determine the calculation logic of the target query statement and the corresponding specific SQL statement based on each data in the target dataset, can all be set according to actual needs, and this invention does not limit this.

[0182] Secondly, this invention can be widely applied to different database categories. In specific applications of this invention, different databases and corresponding generated statements can be adjusted according to the actual situation, and this invention does not limit them.

[0183] It should be noted that, in this invention, the specific screen displayed on the front-end interface and the specific input method (e.g., entering data in the input box after the corresponding data, or selecting the corresponding data in the corresponding option box, etc.) can be set according to the actual situation, and this invention does not limit them.

[0184] Secondly, it's understandable that determining the corresponding target query based on the data in the target dataset is a key step in achieving no-code implementation. In multi-dimensional analysis scenarios, the data in the target dataset is independent of specific business logic. After processing with pre-defined computational logic, it can be used to describe the dimensional range in multi-dimensional scenarios, thereby generating the target dimensional data table. This approach can be widely applied to complex relational scenarios.

[0185] The multidimensional data report determination system provided by this invention determines an initial data cache report based on the target dataset input by the user and by concatenating and caching instances related to the target dimension table. It then determines the target query statement based on computational logic. By determining the initial data cache report and the target query statement based on data input by business analysts, multidimensional data reports can be generated without the involvement of programmers, reducing communication costs between business analysts and programmers and improving the efficiency of multidimensional data analysis.

[0186] It should be noted that the multidimensional data report determination system provided by the present invention is used to execute the above-mentioned multidimensional data report determination method, and its specific implementation method is the same as that of the method implementation method, and will not be repeated here.

[0187] Figure 4 This is a schematic diagram of the physical structure of an electronic device provided by the present invention, such as... Figure 4As shown, the electronic device may include a processor 410, a communication interface 411, a memory 412, and a bus 413, wherein the processor 410, the communication interface 411, and the memory 412 communicate with each other via the bus 413. The processor 410 can call logical instructions in the memory 412 to execute the following methods: acquiring a target dataset input by the user; wherein the target dataset includes a target dimension table; based on the target dimension table, concatenating and caching instances related to the target dimension table to determine an initial data cache report; based on the target dataset, determining a target query statement; and based on the initial data cache report, determining a target multidimensional data report according to the target query statement.

[0188] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer power supply (which may be a personal computer, server, or network power supply, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0189] Furthermore, this invention discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the multidimensional data report determination method provided in the above-described method embodiments, for example including: obtaining a target dataset input by a user; wherein the target dataset includes a target dimension table; based on the target dimension table, concatenating and caching instances related to the target dimension table to determine an initial data cache report; based on the target dataset, determining a target query statement; and based on the initial data cache report, determining a target multidimensional data report according to the target query statement.

[0190] On the other hand, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the multidimensional data report determination method provided in the above embodiments, for example including: acquiring a target dataset input by a user; wherein the target dataset includes: a target dimension table; based on the target dimension table, concatenating and caching instances related to the target dimension table to determine an initial data cache report; based on the target dataset, determining a target query statement; and based on the initial data cache report, determining a target multidimensional data report according to the target query statement.

[0191] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0192] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer power supply (which may be a personal computer, server, or network power supply, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0193] 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 of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining multidimensional data reports, characterized in that, include: Obtain the target dataset input by the user; wherein, the target dataset includes: a target dimension table, a target fact table, a target fact field list, a target aggregation function, a target aggregation dimension, and a target time granularity; Based on the target dimension table, the instances related to the target dimension table are chained together and cached to determine the initial data cache report; Based on the target dimension table, the target fact table, and the target fact field list, a query statement fragment for associating the dimension table with the fact table is determined; wherein, the query statement fragment for associating the dimension table with the fact table is used to associate the dimension table and the fact table in the initial data cache report; Based on the target fact field list and the target aggregation function, a fact table indicator query statement fragment is determined; wherein, the fact table indicator query statement fragment is used to determine the aggregation rules corresponding to the fact fields in the initial data cache report; Based on the target aggregation dimension and the target time granularity, an aggregation statement fragment is determined; wherein, the aggregation statement fragment is used to determine the corresponding dimension and time granularity when determining the target multidimensional data report; Based on the query fragments relating the dimension table and the fact table, the query fragments of the fact table metrics, and the aggregation fragments, the target query statement is determined. Based on the initial data cache report, the target multidimensional data report is determined according to the target query statement.

2. The method for determining multidimensional data reports according to claim 1, characterized in that, The target fact field list includes several fact fields corresponding to the target fact table; the target aggregation function is the aggregation rule when aggregating the target fact fields; the target aggregation dimension is the dimension corresponding to the generation of the target multidimensional data report; and the target time granularity is the time granularity corresponding to the generation of the target multidimensional data report.

3. The method for determining multidimensional data reports according to claim 2, characterized in that, The target dataset for obtaining user input specifically includes: Obtain target dimension information input by the user; wherein, the target dimension information includes: a starting dimension and an ending dimension; Based on the starting dimension and the ending dimension, determine the set of dimension chains; A first interface is generated according to a preset dimensional chain arrangement rule; wherein, the first interface displays a set of dimensional chains arranged according to the preset dimensional chain arrangement rule; Obtain the target dimension table input by the user on the first interface; wherein, the target dimension table is the target dimension chain determined by the user in the dimension chain set.

4. The method for determining multidimensional data reports according to claim 3, characterized in that, The target dimension information also includes: at least one intermediate dimension; The process of determining a set of dimension chains based on the starting dimension and the ending dimension, and generating a first interface according to a preset dimension chain arrangement rule, specifically includes: According to the order of the starting dimension, the intermediate dimension, and the ending dimension, the set of dimension chains is determined based on the path generation algorithm; Based on the set of dimensional chains, a first interface is generated according to a preset dimensional chain arrangement rule; The first interface displays a set of dimension chains arranged according to the preset dimension chain arrangement rules. The preset dimension chain arrangement rules are as follows: the fewer dimensions a dimension chain includes, the higher its priority; and among dimension chains with the same number of dimensions, the higher the number of instances, the higher its priority.

5. The method for determining multidimensional data reports according to claim 2, characterized in that, The target dataset for obtaining user input specifically includes: Retrieve the target dimension table entered by the user on the first interface; Based on each dimension in the target dimension table, a second interface is generated according to a preset dimension arrangement order; wherein, the second interface displays a list of dimensions arranged according to the preset dimension arrangement order; Obtain the target aggregation dimension input by the user on the second interface; wherein the target aggregation dimension is the target dimension determined in the dimension list.

6. The method for determining multidimensional data reports according to claim 1, characterized in that, The target dataset also includes: a target time range; Based on the target fact field list and the target aggregation function, determine the fact table indicator query statement fragment, specifically including: Based on the target fact field list and the target aggregation function, determine the initial fact table indicator query statement fragment; The fact table indicator query statement fragment is determined by limiting the start and end times of the data in the initial fact table indicator query statement fragment according to the target time range.

7. A multidimensional data report determination system, characterized in that, include: The system includes an input data acquisition unit, a cached report determination unit, a query statement determination unit, and a target report determination unit. The input data acquisition unit is used to acquire the target dataset input by the user; wherein, the target dataset includes: a target dimension table, a target fact table, a target fact field list, a target aggregation function, a target aggregation dimension, and a target time granularity; The cache report determination unit is used to determine the initial data cache report by concatenating and caching instances related to the target dimension table based on the target dimension table. The query statement determination unit is used to determine, based on the target dimension table, the target fact table, and the target fact field list, a query statement fragment relating the dimension table and the fact table; wherein the query statement fragment relating the dimension table and the fact table is used to associate the dimension table and the fact table in the initial data cache report; a query statement fragment relating the fact table to the target fact field list and the target aggregation function is used to determine the fact table indicator query statement fragment; wherein the fact table indicator query statement fragment is used to determine the aggregation rules corresponding to the fact fields in the initial data cache report; an aggregation statement fragment is determined based on the target aggregation dimension and the target time granularity; wherein the aggregation statement fragment is used to determine the dimension and time granularity corresponding to the target multidimensional data report; and a target query statement is determined based on the query statement fragment relating the dimension table and the fact table, the query statement fragment relating the fact table to the fact table, and the aggregation statement fragment. The target report determination unit is used to determine the target multidimensional data report based on the initial data cache report and the target query statement.

8. An electronic device, characterized in that, The system includes a memory and a processor, which communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the multidimensional data report determination method as described in any one of claims 1 to 6 by calling the program instructions.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the multidimensional data report determination method as described in any one of claims 1 to 6.

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