CN115794043A - Calculation system and calculation method for table data aggregation processing of BI tool - Google Patents
Calculation system and calculation method for table data aggregation processing of BI tool Download PDFInfo
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Abstract
The invention discloses a calculation system and a calculation method for table data aggregation processing of a BI tool, wherein the calculation system for table data aggregation processing of the BI tool comprises the following steps: the system comprises a service input layer, a middle station, a data engine and a service output layer; the service input layer is used for receiving the table processing function and the parameters thereof input by the user and transmitting the table processing function and the parameters thereof to the middle station, and the parameters of the table processing function comprise: indexes, dimensions and filtering conditions; the middle station is used for checking and analyzing the information input by the service input layer and sending the analyzed specific calculation request to the data engine; the data engine receives the calculation request of the middle station and performs data calculation based on the calculation request, and returns the calculation result to the service output layer. The invention has the beneficial effects that a user can directly and independently complete a complex data processing scene in a direct, interactive and business logic rule-based data processing mode which is beneficial to the user to master on a BI front-end analysis page.
Description
Technical Field
The invention relates to table data processing of a BI tool, in particular to a calculation system and a calculation method for table data aggregation processing of the BI tool.
Background
The data visualization analysis needs to be based on a data base which can be understood by a user and has no error index, and the user needs to perform various processing on a piece of data to achieve the purpose. Such treatments are composed of many types, including: general data conversion: such as data type conversion, absolute value of data; mutual calculation between data: such as logical operation (if, or, etc.), mathematical operation (addition, subtraction, multiplication, division, etc.), text operation (string concatenation, etc.); and (3) data aggregation operation: summary, average, etc. of the data. Cross-view computation: the proportion, ordering, etc. of the data. And (3) calculating between lines: cycle synchronization, cumulative value calculation, etc.
A complete data processing flow may have the above various types of components, and such an operation is not a simple process for a user, and there are two main ways currently: the data processing only involves simple operations such as data conversion, data mutual calculation and aggregation operation, a user can obtain a result by relying on the function operation of the front end, but the data processing capability of the front end function is limited, complex operations such as cross-view calculation, line-to-line calculation and combination of the two calculations and other calculations are involved, and the function cannot achieve the data processing target expected by the user.
Depending on the data processing capability at the bottom of the product, which is separate from the front-end data analysis interface, the data processing capability at the bottom has several drawbacks: the underlying data processing capabilities are often based on code logic, are difficult for users to master, and require significant debugging costs during use. Data processing is separated from the analysis behavior of the front end, the debugging and the analysis of the data by the user cannot interact with each other, the continuity of the analysis flow of the user is poor, and the analysis efficiency is low.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a calculation system and a calculation method for the table data aggregation processing of a BI tool.
In order to achieve the above object, the present invention adopts the following technical solutions:
a computing system for table data aggregation processing for a BI tool, comprising: the system comprises a service input layer, a middle station, a data engine and a service output layer;
the service input layer is used for receiving the table processing function and the parameters thereof input by the user and transmitting the table processing function and the parameters thereof to the middle station, and the parameters of the table processing function comprise: indices, dimensions, and filtering conditions; the middle station is used for checking and analyzing the information input by the service input layer and sending the analyzed specific calculation request to the data engine; the data engine receives the calculation request of the middle station, performs data calculation based on the calculation request and returns the calculation result to the service output layer; and the service output layer is used for displaying the calculation result to the user.
Further, the verification of the information by the middle station comprises the following steps:
checking whether the format of the table processing function is correct;
checking whether the index of the table processing function is a polymerization calculation index;
checking whether the dimension of the table processing function and the filter condition can be obtained from the component of the BI tool.
Further, the table processing function comprises a base function, a first step function and a second step function;
the basic function is to calculate the table data after filtering according to the filtering condition, and the dimensionality is used as the aggregation dimensionality to execute the calculation of the index;
the first step function is to obtain the dimension of an analysis region and add the dimension of a function region as the dimension of aggregation on the basis of the basic function;
the second step function is to obtain the dimension of the analysis region and subtract the dimension of the function region as the aggregation dimension on the basis of the basic function.
Further, the index of the table processing function and the filtering condition may be output results of other table processing functions.
Further, when the table processing function is a nested function, the upper view of the outermost function is the view of the output position of the outermost function, and the upper view of the inner function is the view constructed by the sum of all dimensions of the upper functions of all levels upwards and the dimension of the output position.
Further, when the table processing function is a nested function, a new view is generated and data splicing with an upper view is completed, so that the data calculation scene under different dimensions is met.
Further, the filtering condition of the parameter of the table processing function is analyzed by the middle station, the filtering condition is converted into a specific filtering requirement and is transmitted to the data engine, wherein the priority level of the filtering condition is higher than that of index calculation and data splicing; the plurality of filter conditions perform intersection calculations.
Further, the service output layer displays the index calculated by the data engine, and the index and other fields in the BI component are displayed in the area to be analyzed of the BI component for the user to apply.
A calculation method for table data aggregation processing of a BI tool includes the following steps:
step 4, the data engine receives the calculation request of the middle station, performs data calculation based on the calculation request, and returns the calculation result to the service output layer;
and 5, the service output layer displays the calculation result to the user.
Further, the verification of the information by the middle station comprises the following steps: checking whether the format of the table processing function is correct; checking whether the index of the table processing function is a polymerization calculation index; checking whether the dimension of the table processing function and the filter condition can be obtained from the component of the BI tool.
The method has the beneficial effects that the user can directly and independently complete the complex data processing scene in a direct and interactive data processing mode which is based on the business logic rule and is beneficial to the user to master on the front-end analysis page of the BI.
A computing system that constructs sheet data processing inside a BI tool completes complex data processing scenarios in a manner that is easily understood by a user at the BI front end. The request of the front end is analyzed and sent to the data engine by the middle station, and the data engine carries out calculation. The user is not required to master the underlying code logic. The user does not relate to the underlying code logic, analysis can be continuously carried out through the BI front end, the continuity of the analysis process is good, and the analysis efficiency is high.
The user only needs to master the most basic function, does not need to have a professional code background, and does not need a large amount of learning cost.
The method has the advantages that the achievable computing power is strong, the table processing function is triggered from the most basic computing, and all basic computing powers are integrated, so that any complex computing scene is realized. When a user grasps a basic table processing function, the analysis requirement of any complex logic of data can be met, and the user has stronger data analysis capability. The user can not only complete complex data processing by analyzing the page at the front end.
Drawings
FIG. 1 is a block diagram of an execution framework of a computing system for a table data aggregation process of a BI tool of the present invention;
FIG. 2 is a schematic illustration of an input interface of a business input layer of a computing system for table data aggregation processing of a BI tool of the present invention;
FIG. 3 is a schematic diagram of an output interface of a business output layer of a computing system for table data aggregation processing of a BI tool of the present invention showing return group intra-aggregation indicators;
FIG. 4 is a schematic diagram of a functional output view of a computing system for table data aggregation processing of a BI tool of the present invention stitched with an upper view to obtain a view.
Detailed description of the preferred embodiments
The invention is described in detail below with reference to the figures and the embodiments.
As shown in fig. 1 to 4, a calculation system for a table data aggregation process of a BI tool includes: the system comprises a service input layer, a middle station, a data engine and a service output layer.
The service input layer is used for receiving the table processing function and the parameters thereof input by the user and transmitting the table processing function and the parameters thereof to the middle station, and the parameters of the table processing function comprise: indices, dimensions, and filtering conditions. The middle station is used for checking and analyzing the information input by the service input layer and sending the analyzed specific calculation request to the data engine. The data engine receives the calculation request of the middle station and performs data calculation based on the calculation request, and returns the calculation result to the service output layer. And the service output layer is used for displaying the calculation result to the user.
A calculation method for table data aggregation processing of a BI tool includes the following steps:
step 4, the data engine receives the calculation request of the middle station, performs data calculation based on the calculation request and returns the calculation result to the service output layer;
and 5, the service output layer displays the calculation result to the user.
1. Service input layer
The business input layer relates to formula rule input and business data input. Referring to fig. 2, formulas and parameters are input at an input interface of a service input layer.
1.1 formula rule input, embodied by function. The table processing function includes a base function, a first step function, and a second step function.
The basis function (DEF function) is a function that calculates table data after filtering according to a filtering condition, and calculates an index with a dimension as a polymerization dimension. Format of DEF function: DEF (index, [ dimension 1, dimension 2. ] filter condition 1, filter condition 2. ]). And (4) calculating the data after condition filtering according to the filtering conditions 1 and 2, and performing calculation of the index by taking the dimension 1 and the dimension 2 as a polymerization dimension. For example, DEF (SUM _ AGG (purchase quantity), [ product ], [ user = "a").
The first step function (DEF _ ADD function) is to obtain the analysis region dimension and ADD the function region dimension as the aggregation dimension on the basis of the base function. For example, DEF _ ADD (SUM _ AGG (purchase quantity), [ user ]), the actual calculation dimensions are [ product ] and [ user ] on the basis of the above DEF function.
The second step function (DEF _ SUB function) is to obtain the analysis region dimension and subtract the function region dimension as the aggregation dimension based on the basis of the basis function. For example, DEF _ SUB (SUM _ AGG (purchase quantity), [ user ]), the actual calculation dimension is [ product ] on the basis of the above DEF function.
The indices of the table processing functions and the filter conditions may be the output results of other table processing functions. I.e. the table processing functions can be used nested.
The user enters a data processing request based on the requirements of the service input layer. And after confirmation, the corresponding request is transferred to the middle platform to analyze the data processing request.
1.2, service data input, parameters [ indexes ] and [ dimensions ] in a table processing function [ filtering conditions ] and the like can be data fields in a product, and a user can write data into the table processing function to transfer the data to a lower layer and perform subsequent processing on the data based on an input rule.
2. Middle platform
The middlebox involves rule checksum function parsing.
2.1 rule checking, the information input by the user through the service input layer can return a correct calculation result only if the principle agreed by the table processing function is satisfied. Therefore, the central station verifies the rules input by the user and returns error information to the user for adjustment.
The verification of the information by the middle station comprises the following steps:
1) Checking whether the format of the table processing function is correct or not;
2) Whether the index of the processing function of the check table is a polymerization calculation index or not;
3) The dimensions of the check sheet processing function and the filter criteria are accessible from within the components of the BI tool.
And when all the checked contents pass, the middle station analyzes the information input by the service input layer into a specific calculation request and sends the calculation request to the data engine. And when different contents exist, returning unqualified information to the user and prompting the user to modify.
The middle station is responsible for analyzing the table processing function into a specific calculation request and sending the calculation request to the data engine. When a table processing function contains a plurality of calculation requests, the middle station can decompose the complete rule request for calculation.
And 2.2, function analysis, namely, after receiving the information input by the service input layer, the middle station analyzes the information input by the service input layer into a specific calculation request and sends the calculation request to the data engine. The specific calculation request is embodied in data calculation, data splicing, data filtering and calculation combination.
2.2.1 calculating data, identifying the parameter [ index ] in the processing function of the middle station, and sending a request of the parameter [ index ] to a data engine. [ INDEX ] is the output value of the table processing function, and the table processing function calculates according to the corresponding rule and then outputs the index as the result of the table processing function. The [ index ] may be different basic calculations and combinations thereof, the parameter requires output as calculation results of aggregation level, such as sum (purchase quantity), and the parameter calculation process supports the application of all detail calculation functions, such as sum (purchase quantity per unit price), sum (purchase quantity per sum (unit price), sum (abs (purchase quantity)), and the like, on the basis of satisfying the output as the result of aggregation level.
2.2.2 data splicing, the computing power of the data splicing is not embodied in parameters, but the application of function results is embodied. The middle station obtains the information input by the service input layer, analyzes the information into a corresponding splicing request, and delivers the information to the data engine for calculation.
The specific parsing logic of the splicing calculation is as follows: the output result of the base function (DEF function) will be integrated into the upper view of the function.
When the table processing function is a nested function, the upper view of the outermost function is the view of the output position of the outermost function, and may be the view of the BI display area or the detail table.
The upper view of the nested inner-layer functions in the functions is a view constructed by the sum of all dimensions of the upper-layer functions of all levels upwards and the dimension of an output position, namely the upper view on which the functions depend is a view of accumulated dimensions from inside to outside.
And calculating and outputting the function as an index, forming a new view by the function with the output index and dimension 1 and dimension 2, and splicing the new view with an upper view (left join) according to the function with dimension 1 and dimension 2 = dimension 1 and dimension 2 in the upper view. If the dimension in the upper view is missing compared with the dimension in the function, the missing dimension is supplemented into the upper view and then spliced with the function view (refer to fig. 4).
And the new view is generated and spliced with the upper view to complete the data splicing, so that the data calculation scene under different dimensions is met.
2.2.3 data filtering, the middle station analyzes the filtering condition of the parameter of the table processing function, converts the filtering condition into a specific filtering requirement and transfers the specific filtering requirement to the data engine, wherein the priority level of the filtering condition is higher than the index calculation and the data splicing. The [ index ] is executed based on the results of the data after [ filtering condition 1, filtering condition 2 ] screening, and intersection calculation is executed by a plurality of filtering conditions.
And the business output layer displays the indexes calculated by the data engine, and the indexes are displayed in the area to be analyzed of the BI component together with other fields in the BI component for the user to apply.
2.2.4 calculating the combination, the function will contain a plurality of calculation requests, the analysis of the combination is a sequence analysis, a large number of calculation units realize the return of different calculation results through different execution sequences, the middle station analyzes the calculation requirements in the function into basic calculation requests, and sends the calculation requests which identify the combination rules and analyze the basic calculation requests into corresponding calculation sequences to the data engine. The main combination modes are divided into two types: combining and nesting.
Combining: the [ index ] may be a combination of a plurality of basis calculation functions, and the detailed calculation and the aggregate calculation may be combined arbitrarily on the basis of satisfying that the output result is the aggregate result. Filter condition 1, filter condition 2 may be a combination of a plurality of basis calculation functions, such as AND (purchase quantity >3, purchase quantity price > 10). The function itself is a combination by which computation, filtering, and splicing are combined. Thereby enabling a combination of the underlying computing power.
Nesting: and the output of the function result is obtained by splicing the output view of the function with the upper view, and the obtained index is used as an output item. The functions are nested through their own views. The method specifically comprises the following steps: the [ index ] may be a nested function, in addition to being a combination of basis calculation functions. For example, def (avg (sum (purchase quantity), easy [ product ], easy [ user ])). The index used in combination in the filtering condition may be an index output by the nested function, for example, def (sum (available [ purchase quantity ]), easy [ product ], def (sum (available [ unit price ]), easy [ user ]) > 7.
3. Data engine
And the data engine receives the calculation request of the transfer of the middlebox, performs data calculation based on the calculation request and returns the calculation result to the service output layer.
4. Service output layer
And (3) outputting: and the data engine completes calculation based on the calculation request transferred from the middle platform and returns the result to the service layer, and the service layer output layer obtains a new calculation index which is displayed in the area to be analyzed of the assembly together with other fields in the BI assembly for subsequent analysis and use by a user. The returned calculation index is calculated as a function in combination as in fig. 3 below. (this index is calculated by aggregating the purchase data based on product dimensions and stitching the results into a master view). The application comprises the following steps: the output calculation index is used by the user for other calculations or presentations with other fields in the component.
The foregoing shows and describes the general principles, principal features and advantages of the invention. It should be understood by those skilled in the art that the above embodiments do not limit the present invention in any way, and all technical solutions obtained by using equivalent alternatives or equivalent variations fall within the scope of the present invention.
Claims (10)
1. A computing system for table data aggregation processing for a BI tool, comprising: the system comprises a service input layer, a middle station, a data engine and a service output layer;
the service input layer is used for receiving a table processing function and parameters thereof input by a user and transmitting the table processing function and the parameters thereof to the middle station, and the parameters of the table processing function comprise: indexes, dimensions and filtering conditions; the middle station is used for checking and analyzing the information input by the service input layer and sending the analyzed specific calculation request to the data engine; the data engine receives the calculation request of the middle station, performs data calculation based on the calculation request and returns the calculation result to the service output layer; and the service output layer is used for displaying the calculation result to the user.
2. The BI tool table data aggregation process calculation system as recited in claim 1,
the verification of the information by the middle station comprises the following steps:
checking whether the format of the table processing function is correct;
checking whether the index of the table processing function is a polymerization calculation index;
checking whether the dimension and the filtering condition of the table processing function can be acquired from the component of the BI tool.
3. The BI tool table data aggregation process calculation system as recited in claim 1,
the table processing function comprises a basic function, a first step function and a second step function;
the basic function is to calculate the table data after filtering according to the filtering condition, and calculate the execution index by taking the dimension as the aggregation dimension;
the first step function is to obtain the dimension of an analysis region and add the dimension of a function region as the dimension of aggregation on the basis of a basic function;
the second step function is to obtain the dimension of an analysis region and subtract the dimension of a function region as the dimension of aggregation on the basis of the basic function.
4. The BI tool table data aggregation process calculation system as recited in claim 1,
the index of the table processing function and the filter condition may be output results of other table processing functions.
5. The BI tool table data aggregation process calculation system as recited in claim 1,
when the table processing function is a nested function, the upper view of the outermost function is the view of the output position of the function, and the upper view of the inner function is the view constructed by the sum of all dimensions of the upper functions of all levels upwards and the dimension of the output position.
6. The BI tool table data aggregation processing calculation system as recited in claim 1,
when the table processing function is a nested function, the new view is generated and the data is spliced with the upper view, so that the calculation scene of the data under different dimensions is met.
7. The BI tool table data aggregation process calculation system as recited in claim 1,
the middle station analyzes the filtering condition of the parameter of the table processing function, converts the filtering condition into a specific filtering requirement and transfers the specific filtering requirement to the data engine, wherein the priority level of the filtering condition is higher than the priority level of index calculation and data splicing; the plurality of filter conditions perform intersection calculations.
8. The BI tool table data aggregation process calculation system as recited in claim 1,
and the business output layer displays the indexes calculated by the data engine, and the indexes are displayed in the area to be analyzed of the BI component together with other fields in the BI component for the user to apply.
9. A calculation method for table data aggregation processing of a BI tool, comprising the steps of:
step 1, a service input layer receives the table processing function and the parameters thereof input by a user, wherein the parameters of the table processing function comprise: indices, dimensions, and filtering conditions;
step 2, the service input layer transmits the table processing function and the parameters thereof input by the user to the middle station;
step 3, the middle station checks the information input by the service output layer, if the check is not passed, error information is returned, and if the check is passed, the middle station analyzes the information input by the service input layer into a specific calculation request and sends the calculation request to a data engine;
step 4, the data engine receives the calculation request of the middle station, performs data calculation based on the calculation request and returns the calculation result to the service output layer;
and 5, the service output layer displays the calculation result to the user.
10. The calculation method for table data aggregation processing of a BI tool as claimed in claim 9,
the verification of the information by the middle station comprises the following steps:
checking whether the format of the table processing function is correct;
checking whether the index of the table processing function is an aggregation calculation index;
checking whether the dimension and the filtering condition of the table processing function can be obtained from the component of the BI tool.
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