An index analyzer implementation method and system based on a business three-dimensional analysis model

Abstract

The application discloses a kind of based on service three-dimensional analysis model's index analyzer implementation method and system, the method includes: determining analysis model and index dataset, for service analysis theme;The index data of analysis model and index dataset are analyzed, determine atomic index data and composite index data, composite index data is decomposed, and finally composite index data is decomposed to atomic index data;Determine the business process where atomic index data is located;Identify the business object corresponding to atomic index data in business process;Determine business scenario, determine logical entity, atomic index data is obtained from object logical entity, and different logical entities exist under different business scenarios for the same business object;With index data three-dimensional structure method determines the attribute contrast in logical entity, the system includes index data analyzer, and the application can greatly improve the flexibility of service analysis.

Description

Technical Field

[0001] This invention relates to the fields of business definition, data model, business intelligence, database, and computer system, and in particular to a method and system for implementing an indicator analyzer based on a three-dimensional business analysis model. Background Technology

[0002] Indicator data forms the foundation of business management and analysis. Indicator data are statistical values ​​that reflect a company's operating status and business activities. Indicator data generally consists of two parts: the indicator name and the indicator value.

[0003] The construction of indicator data systems and the processing and calculation of indicator data are important functions of many current enterprise management software and business intelligence systems. However, current methods and technologies have the following problems:

[0004] I. The definition of indicator data lacks a systematic approach. Generally, the indicator data is designed by suppliers (consulting service providers, software developers) and customers based on current business needs and expert experience. This results in limited coverage of indicator data and frequent changes in indicator data as business development and management needs change.

[0005] Second, the calculation of indicator data lacks systematic logic. Existing technologies are mostly based on data hierarchy and fixed data models for indicator data processing and calculation. Moreover, the calculation and processing logic is solidified in the program. When the calculation method and scope of indicator data change, a lot of resources are required to modify it.

[0006] Third, existing indicator data analysis methods are insufficient to achieve the perception of the effectiveness of business activities and the improvement of business process efficiency. There is a measurement gap between business analysis at the management level and business activities. For example, the "revenue" indicator data is analyzed based on financial standards (amount). Revenue recognition is in the business activity of "delivery and receipt". The measurement of delivery and receipt is "the number of signed items". At this time, when analyzing revenue, it is impossible to link it with the business activity of delivery and receipt, so it is impossible to perceive the actual business situation in real time, and it is also impossible to guide the improvement of business activities. Summary of the Invention

[0007] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for implementing an indicator analyzer based on a three-dimensional business analysis model.

[0008] The technical solution adopted in this invention is a method for implementing an indicator analyzer based on a three-dimensional business analysis model. The indicator analyzer starts from the analysis data model, progressively decomposes the indicators into atomic indicators, and maps the atomic indicators to the business data model through a three-dimensional structure. The specific steps of this method are as follows:

[0009] Step S1: Determine the analysis model and indicator dataset for the business analysis topic;

[0010] Step S2: Analyze the indicator data of the analysis model and indicator dataset to determine the atomic indicator data and composite indicator data. Decompose the composite indicator data, and finally decompose all the composite indicator data into atomic indicator data.

[0011] Step S3: Determine the business process in which the atomic indicator data resides;

[0012] Step S4: Identify the business objects corresponding to atomic indicator data in the business process;

[0013] Step S5: Determine the business scenario and the logical entity. Atomic indicator data is obtained from the object's logical entity. The same business object may have different logical entities in different business scenarios.

[0014] Step S6: Use the three-dimensional structure method of indicator data to determine the attribute mapping in the logical entity, including business primary key attributes (y-axis elements), master data attributes (x-axis elements), basic data attributes (x-axis elements), and measurement attributes (z-axis elements). Finally, map the atomic indicator data definition to the logical entity attributes.

[0015] Furthermore, the indicator data is calculated by starting with atomic indicator data. Atomic indicator data is calculated based on the master data, basic data, and metric attributes of the three-dimensional structure and transaction logic entity according to the mapping and calculation relationship. Composite indicator data is calculated based on the atomic indicator data through the atomic indicator data calculation relationship in the indicator data definition. The analysis model data is calculated based on the atomic indicator data and composite indicator data.

[0016] Furthermore, when the composite indicator data and data model are used to calculate the lower-level indicator data, the lower-level indicator data performs dimensional degradation and dimensional-level convolution calculations on the dimensions and levels of the composite indicator data and data model.

[0017] Furthermore, the dimensions of atomic indicator data are the master data and basic data of the object logical entity. When atomic indicator data is used to calculate composite indicator data, the dimensions of the composite indicator data are converged, and the hierarchical calculation of each dimension of the composite indicator data is summarized. The dimension combination of composite indicator data should include the dimension intersection of the lower-level indicator data.

[0018] Furthermore, in the calculation process of the analysis model data, the indicator data in the model includes atomic indicator data and composite indicator data. The analysis model is subjected to dimensional convergence and dimensional hierarchy calculation. The common dimension of the analysis model should be the intersection of the dimensions of the lower-level indicator data.

[0019] A system for implementing an indicator analyzer based on a three-dimensional business analysis model, the system comprising: an indicator data analyzer,

[0020] The indicator data analyzer starts from business analysis and decomposes the analysis data model step by step to the atomic indicator data that measures the business process. The atomic indicator data, through a three-dimensional structure, identifies the business objects (O), logical entities (E), and business relationships (R) of the business activities, and classifies the business data into main data (M), basic data (B), and metrics (m).

[0021] Furthermore, the analytical data model is a set of indicator data used to perform multi-dimensional analysis on business themes or business objects. The expression is: analytical model = [C(indicator data), ∩(M), ∩(B)];

[0022] Wherein, C is the set of indicator data, that is, the list of indicator data under the analysis topic, which includes composite indicator data and atomic indicator data; ∩(M) and ∩(B) are the intersection of the master data and basic data of all atomic indicator data / composite indicator data in the analysis model, that is, the indicator data under the analysis model are analyzed from the same dimensional perspective.

[0023] Furthermore, the analysis model can automatically generate physical data model table building scripts through expressions, which are then stored in the physical database. Through the calculation logic of various indicator data, the data is calculated from transaction data and stored in the analysis model's physical table, so as to support business analysis applications to directly access the data table for analysis and display.

[0024] Beneficial effects:

[0025] 1) The core of enterprise operation analysis is operational indicator data. This data, based on business facts, reflects the enterprise's operational efficiency and effectiveness. Therefore, it is necessary to establish a logical relationship between analytical indicator data and business fact data, realizing the connection between transaction data (TP) and analytical data (AP). This invention can achieve the relationship between analytical indicator data and transaction data through a three-dimensional structure, constructing SQL operators for atomic indicator data to enable real-time calculation of indicator data based on transaction data.

[0026] 2) Business management analysis requires the dynamic combination of dimensional and indicator data. Therefore, it is necessary to determine the calculation logic of each indicator data to form a calculation link between business indicator data and analysis models. This method can start from business fact data, determine the operator logic of atomic indicator data, and dynamically assemble composite indicator data and analysis models based on atomic indicator data, changing the traditional data storage method based on data hierarchy and fixed analysis model structure.

[0027] 3) The 3D-structure-based indicator data analyzer can drive the construction of analytical data computation and physical storage models based on business metadata. Atomic indicator data is associated with the original business data through 3D structural semantics to construct data computation logic operators. Composite indicator data is assembled with semantics from atomic indicator data to construct data computation logic operators. The analytical model determines the indicator dataset and data storage structure through the semantic assembly of dimensions and indicator data. Indicator data computation driven by business metadata enables on-demand (real-time, batch) computation and storage (memory, storage) of indicator data, greatly improving the flexibility of business analysis. The analytical model construction driven by business metadata can also quickly and automatically generate a physical storage model based on the analytical dimensions and indicator dataset, realizing data persistence. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the implementation of the index analyzer of the present invention.

[0029] Figure 2 This is a diagram showing the index decomposition structure of the present invention;

[0030] Figure 3 This is a flowchart illustrating the calculation process for the indicator data of this invention. Detailed Implementation

[0031] It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. The following describes the application in further detail with reference to the accompanying drawings and specific embodiments.

[0032] like Figure 1 As shown, a method for implementing an indicator analyzer based on a three-dimensional business analysis model is presented.

[0033] The indicator data analyzer starts from the analytical data model, gradually breaking down the indicator data into atomic indicator data, and then mapping the atomic indicator data to the business data model through a three-dimensional structure. The specific method is as follows:

[0034] Step S1: Determine the analysis model and indicator dataset. For the business analysis topic, such as revenue target analysis, determine the dimensions and indicator dataset to be analyzed.

[0035] Step S2: Analyze the indicator data in the analysis model indicator dataset to determine the atomic indicator data and composite indicator data. The composite indicator data is decomposed, and ultimately all composite indicator data is decomposed into atomic indicator data. The indicator decomposition structure is shown in Figure 2.

[0036] Step S3: Determine the business process in which the atomic indicator data resides, such as the customer ordering process or the shipping process.

[0037] Step S4: Identify the business objects corresponding to atomic indicator data in the business process. For example, the business object corresponding to the atomic indicator data "order quantity" is "order", and the business object corresponding to the atomic indicator data "order quantity of a certain product" is also "order".

[0038] Step S5: Determine the business scenario and the logical entity. Atomic indicator data is obtained from the object's logical entity. The same business object may have different logical entities in different business scenarios. For example, a delivery note object may have different business scenarios such as B2B delivery, B2C delivery, and internal delivery. These scenarios correspond to different delivery note models. Therefore, it is necessary to determine the logical entity corresponding to the atomic indicator data.

[0039] Step S6: Use the three-dimensional structure method of indicator data to determine the attribute mapping in the logical entity, including the business primary key attribute y-axis element, the master data attribute x-axis element, the basic data attribute x-axis element, and the measurement attribute z-axis element, and finally map the atomic indicator data definition to the logical entity attributes.

[0040] Indicator data calculation begins with atomic indicator data, which is calculated based on the master data, basic data, and metric attributes of the three-dimensional structure and transactional logical entities according to mapping and calculation relationships. Composite indicator data is calculated based on the atomic indicator data through the calculation relationships of the atomic indicator data defined in the indicator data definition. Analytical model data is then calculated based on both atomic and composite indicator data.

[0041] When calculating lower-level indicator data from composite indicator data and data model data, dimensional alignment may be required. This may necessitate dimensional degradation and hierarchical convolution calculations of the lower-level indicator data and data model to align with the dimensions and levels of the composite indicator data and data model. The indicator data calculation process is as follows: Figure 3 As shown.

[0042] The dimensions of atomic indicator data are the master data and basic data of the object logical entity. When atomic indicator data is used to calculate composite indicator data, dimensional convergence (i.e., atomic indicator data GROUP BY composite indicator data dimension combination) is required, and the hierarchical calculation of each dimension of the composite indicator data is summarized. The dimensional combination of composite indicator data should include the intersection of the dimensions of the lower-level indicator data.

[0043] Similarly, during the calculation of data in the analytical model, the indicator data (including atomic and composite indicator data) should undergo dimensional convergence and hierarchical calculation according to the needs of the analytical model. The common dimension of the analytical model should be the intersection of the dimensions of the lower-level indicator data to ensure that the indicator data is analyzed from the same business analysis perspective.

[0044] A method and system for implementing an indicator analyzer based on a three-dimensional business analysis model is disclosed. This system includes an indicator data analyzer, which starts with business analysis and, targeting the analysis data model and indicator dataset, progressively decomposes them to atomic indicator data that measure business processes. The atomic indicator data, through a three-dimensional structure, identifies the business objects (O), logical entities (E), and business relationships (R) of business activities, and classifies the business data into master data (M), basic data (B), and metrics (m).

[0045] Atomic index data expression and uses: The three-dimensional structure of atomic index data is expressed as: Atomic index data = [K(O), Cn m(M, B), f(m)], explained in detail below:

[0046] K is the primary key of the business object and is required. If the metric data only measures a single business object, then K is the primary key of that business object. For example, in the metric data "Quantity ordered by a certain customer this month", the business object is "Order" and the primary key is "Order Number".

[0047] M represents the master data attribute in an object entity. Master data is an object that supports different business operations within an enterprise, such as "customer," "organization," and "product." Business activities are oriented towards master data objects; for example, shipping is done for the "customer" master data object.

[0048] B represents the basic data attribute in the object entity. Basic data, also called reference data, is used to classify other data, such as "customer level" and "order type".

[0049] The master data and basic data correspond to the analytical dimensions of the indicator data, supporting the analysis of business from different perspectives and levels.

[0050] m is a metric attribute in the object entity, used to measure business volume, such as "product order quantity".

[0051] Atomic indicator data is expressed through a three-dimensional structure and can be directly associated with the attributes of object entities in the business process, automatically constructing data retrieval logic scripts, such as SQL scripts.

[0052] Composite indicator data expressions and uses: Composite indicator data is generated by superimposing and calculating multiple atomic indicator data or composite indicator data. Composite indicator data can be decomposed layer by layer, ultimately breaking it down into atomic indicator data.

[0053] Composite index data is expressed as: Composite index data = [K(O), ∩(M), ∩(B), f(index data)]

[0054] K is the primary key of the business object. It is optional in composite indicator data, depending on whether the analysis is performed on a business object. When composite indicator data involves multiple business objects, the main business object, i.e. the business object to be analyzed, needs to be determined. For example, the indicator data "Quantity of a certain product ordered but not shipped in a certain order" involves two business activities: ordering and shipping. These correspond to two business objects: "Order Order" and "Shipping Order". The main business object is the Order Order, and the business primary key is "Order Order Number".

[0055] ∩(M) and ∩(B) are the intersection of the primary and basic data of the atomic index data / composite index data under the composite index data. That is, the composite index data is calculated by associating the lower-level index data (atomic index data or lower-level composite index data) with the same dimension.

[0056] f(indicator data) refers to the calculation relationship of the lower-level indicator data in the composite indicator data.

[0057] Composite indicator data can be automatically converted into indicator data calculation scripts, such as SQL scripts, through expressions.

[0058] The analytical model is a set of indicator data used to conduct multi-dimensional analysis of business themes or business objects. The expression is: Analytical model = [C(indicator data), ∩(M), ∩(B)].

[0059] C represents the set of indicator data, which is a list of indicator data under the analysis topic. This set of indicator data includes composite indicator data and atomic indicator data.

[0060] ∩(M) and ∩(B) are the intersection of the master data and basic data of all atomic index data / composite index data in the analysis model, that is, the index data under the analysis model are analyzed from the same dimensional perspective.

[0061] The analysis model can automatically generate physical data model table building scripts through expressions, which are then stored in the physical database. Through the calculation logic of various indicator data, the data is calculated from transaction data and stored in the analysis model's physical tables, so that business analysis applications can directly access the data tables for analysis and display.

[0062] The core of enterprise operation analysis lies in operational indicator data. This data, based on business facts, reflects the enterprise's operational efficiency and effectiveness. Therefore, it is necessary to establish a logical relationship between analytical indicator data and business fact data, linking transaction data (TP) and analytical data (AP). This invention utilizes a three-dimensional structure to achieve the relationship between analytical indicator data and transaction data, constructing SQL operators for atomic indicator data to enable real-time indicator data calculation based on transaction data.

[0063] Business management analysis requires the dynamic combination of dimensional and indicator data. Therefore, it is necessary to determine the calculation logic of each indicator data to form a calculation link between business indicator data and analysis models. This method can start from business fact data, determine the operator logic of atomic indicator data, and dynamically assemble composite indicator data and analysis models based on atomic indicator data, changing the traditional data storage method based on data hierarchy and fixed analysis model structure.

[0064] A three-dimensional structure-based indicator data analyzer can drive the construction of analytical data computation and physical storage models based on business metadata. Atomic indicator data is associated with the original business data through three-dimensional structural semantics to construct data computation logic operators. Composite indicator data is assembled with semantics from atomic indicator data to construct data computation logic operators. The analytical model determines the indicator dataset and data storage structure through the semantic assembly of dimensions and indicator data. Indicator data computation driven by business metadata enables on-demand (real-time, batch) computation and storage (memory, storage) of indicator data, greatly improving the flexibility of business analysis. The analytical model construction driven by business metadata can also quickly and automatically generate a physical storage model based on the analytical dimensions and indicator dataset, realizing data persistence.

[0065] In the description of this invention, it should be noted that, unless otherwise specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0066] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for implementing an indicator analyzer based on a three-dimensional business analysis model, characterized in that... The indicator analyzer starts by analyzing the data model, breaking down indicators into atomic indicators step by step, and then mapping the atomic indicators to the business data model through a three-dimensional structure. The specific steps of this method are as follows: Step S1: Determine the analysis model and indicator dataset for the business analysis topic; Step S2: Analyze the indicator data of the analysis model and indicator dataset to determine the atomic indicator data and composite indicator data. Decompose the composite indicator data, and finally decompose all the composite indicator data into atomic indicator data. Step S3: Determine the business process in which the atomic indicator data resides; Step S4: Identify the business objects corresponding to atomic indicator data in the business process; Step S5: Determine the business scenario and the logical entity. Atomic indicator data is obtained from the object's logical entity. The same business object may have different logical entities in different business scenarios. Step S6: Use the three-dimensional structure method of indicator data to determine the attribute mapping in the logical entity, including business primary key attributes (y-axis elements), master data attributes (x-axis elements), basic data attributes (x-axis elements), and measurement attributes (z-axis elements). Finally, map the atomic indicator data definition to the logical entity attributes.

2. The method for implementing an indicator analyzer based on a three-dimensional business analysis model as described in claim 1, characterized in that, The calculation method for the indicator data is as follows: the indicator data calculation starts from the atomic indicator data. The atomic indicator data is calculated based on the master data, basic data and measurement attributes of the three-dimensional structure and transaction logic entity according to the mapping and calculation relationship. The composite indicator data is calculated based on the atomic indicator data through the atomic indicator data calculation relationship in the indicator data definition. The analysis model data is calculated based on the atomic indicator data and the composite indicator data.

3. The method for implementing an indicator analyzer based on a three-dimensional business analysis model as described in claim 1, characterized in that, When the composite indicator data and data model data are used to calculate the lower-level indicator data, the lower-level indicator data is subjected to dimensional degradation and dimensional-level convolution calculations according to the dimensions and levels of the composite indicator data and data model.

4. The method for implementing an indicator analyzer based on a three-dimensional business analysis model as described in claim 1, characterized in that, The dimensions of atomic indicator data are the master data and basic data of the object logical entity. When atomic indicator data is used to calculate composite indicator data, the dimensions of the composite indicator data are converged, and the hierarchical calculation of each dimension of the composite indicator data is summarized. The dimension combination of composite indicator data should include the intersection of the dimensions of the lower-level indicator data.

5. The method for implementing an indicator analyzer based on a three-dimensional business analysis model as described in claim 2, characterized in that, In the process of calculating the data in the analysis model, the indicator data in the model includes atomic indicator data and composite indicator data. The analysis model is subjected to dimensional convergence and dimensional hierarchy calculation. The common dimension of the analysis model should be the intersection of the dimensions of the lower-level indicator data.

6. A system for implementing an index analyzer based on a three-dimensional business analysis model, characterized in that, This system is applied to the indicator analyzer implementation method based on a business three-dimensional analysis model as described in claim 1. The system includes: an indicator data analyzer. The indicator data analyzer starts from business analysis and decomposes the analysis data model step by step to the atomic indicator data that measure the business process. The atomic indicator data, through a three-dimensional structure, identifies the business objects (O), logical entities (E), and business relationships (R) of the business activities, and classifies the business data into main data (M), basic data (B), and metrics (m).

7. The indicator analyzer implementation system based on a business three-dimensional analysis model as described in claim 6, characterized in that, The analytical data model is a set of indicator data used to perform multi-dimensional analysis of business themes or business objects. The expression is: analytical model = [C (indicator data), ∩(M), ∩(B)]; Wherein, C is the set of indicator data, that is, the list of indicator data under the analysis topic, which includes composite indicator data and atomic indicator data; ∩(M) and ∩(B) are the intersection of the master data and basic data of all atomic indicator data and composite indicator data in the analysis model, that is, the indicator data under the analysis model are analyzed from the same dimensional perspective.

8. The indicator analyzer implementation system based on a business three-dimensional analysis model as described in claim 7, characterized in that, The analysis model can automatically generate physical data model table building scripts through expressions, which are then stored in the physical database. Through the calculation logic of various indicator data, the data is calculated from transaction data and stored in the analysis model's physical tables, so that business analysis applications can directly access the data tables for analysis and display.

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