CN109726879B - A data model evaluation method, device and equipment - Google Patents

Abstract

The invention discloses a data model evaluation method, device and equipment. The method includes: determining the association relationship between each database table included in the to-be-evaluated data model based on the metadata structure of the to-be-evaluated data model; The relationship between the database tables is to determine the theoretical design index of each database table and the static weight factor of each database table. The dynamic weight factor of the table; according to the theoretical design index, static weight factor of each database table, dynamic operation index and dynamic weight factor of each database table, determine the comprehensive evaluation result of the data model to be evaluated. The method provided by the invention combines the theoretical design index and the dynamic operation index to evaluate the data model to be evaluated, so that the obtained comprehensive evaluation result is more objective and suitable for practical application.

Description

A data model evaluation method, device and equipment

technical field

The invention relates to the technical field of IT business support, and in particular, to a data model evaluation method, device and equipment.

Background technique

The data model is the core and foundation of the IT system. In the database system, the data model is used to describe the structure and semantics of the database. The quality of the data model design directly affects the operation effect of the system.

A data warehouse is a subject-oriented, integrated, relatively stable collection of data that reflects historical changes and is used to support management decisions. A data warehouse stores analytical data and is mainly used to aggregate various data of an enterprise and conduct online analysis and processing.

In order to effectively support front-end query and analysis, data warehouse modeling usually adopts a hierarchical modeling method, which is divided into basic data layer, summary layer, data mart layer and application layer. The basic data layer stores and integrates the most fine-grained data to build a unified data view to meet unforeseen needs; the aggregation layer usually adopts dimensional modeling to perform fine-grained summarization and index calculation of detailed data from multiple dimensions, supporting statistical analysis applications; The application layer is used to further summarize and calculate indicators for report reports and thematic analysis applications, and directly support the applications. Therefore, the data warehouse model is usually redundant. One indicator data usually appears multiple times in different granularity and different dimension combinations, and the rationality of the upward data aggregation path will greatly affect the analysis efficiency of the system. Therefore, it is necessary to have a set of operational methods for scientific and effective evaluation of data models.

In the prior art, the schemes for evaluating data models are mainly defined in international standards and papers, focusing on the abstract information model level, and do not involve the implementation of IT systems. For example, from an object-oriented perspective, the quality of the information model of network management is defined, and the quality evaluation indicators in the quality model of the object class are disclosed. Form a comprehensive evaluation result. However, there are the following shortcomings: (1) The objective evaluation ability is insufficient, and the model evaluation is mainly oriented to the information model at the logical level, which is out of touch with the actual system implementation, and can only be evaluated from the theoretical design level; in addition, the model evaluation involves subjective indicators, and the evaluation process requires Relevant experts participate, and the evaluation scores come from the subjective scoring of experts. In practical applications, it does not have the objective evaluation ability of automatic analysis and automatic scoring. (2) Model evaluation based on model-based high-level design, the evaluation results cannot reflect the actual application effect of the model in the IT system, and there is a problem that the model with better design effect is not necessarily applicable. (3) There is a lack of quantitative scoring rules for evaluation indicators, it is difficult to intuitively reflect the quality of the model, and the operability is low in practical applications.

To sum up, how to objectively evaluate the data model, reflect the actual application effect of the data model, and avoid the participation of subjective judgment is one of the technical problems to be solved urgently.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a data model evaluation method, device, and device, so as to avoid the participation of subjective judgments, and to evaluate the data model more objectively in combination with actual application scenarios.

In a first aspect, an embodiment of the present invention provides a method for evaluating a data model, including:

Based on the metadata structure of the data model to be evaluated, determine the association relationship between each database table included in the data model to be evaluated;

According to the relationship between each database table, the theoretical design index of each database table and the static weight factor of each database table are determined respectively; and

According to the monitored access records of each database table, determine the dynamic operation index of each database table, and determine the dynamic weight factor of each database table;

The comprehensive evaluation result of the data model to be evaluated is determined according to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table.

In a second aspect, an embodiment of the present invention provides an apparatus for evaluating a data model, including:

a first determining unit, configured to determine the association relationship between each database table included in the data model to be evaluated based on the metadata structure of the data model to be evaluated;

The second determining unit is used for respectively determining the theoretical design index of each database table and the static weight factor of each database table according to the association relationship between each database table;

The third determination unit is used to determine the dynamic operation index of each database table and determine the dynamic weight factor of each database table according to the monitored access records of each database table;

The fourth determining unit is used for determining the comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and static weighting factor of each database table, and the dynamic running index and dynamic weighting factor of each database table.

In a third aspect, an embodiment of the present invention provides a communication device, including a memory, a processor, and a computer program stored on the memory and executable on the processor; when the processor executes the program, the The evaluation method of any one of the data models provided by the present invention.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements any one of the methods for evaluating a data model provided by the present invention. step.

Beneficial effects of the present invention:

The data model evaluation method, device and device provided by the embodiments of the present invention determine the association relationship between each database table included in the to-be-evaluated data model based on the metadata structure of the to-be-evaluated data model; The relationship between each database table is determined, and the theoretical design index of each database table and the static weight factor of each database table are respectively determined; and according to the monitored access records of each database table, the dynamic operation indicators of each database table are determined respectively, and each database table is determined. According to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table, the comprehensive evaluation result of the data model to be evaluated is determined. By adopting the method provided by the present invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, which can not only objectively evaluate each data model to be evaluated, but also greatly reduces the participation of subjective evaluation, and at the same time ensures the integrity of the data model. Design rationality and practicality.

Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description, claims, and drawings.

Description of drawings

The accompanying drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the attached image:

1 is a schematic flowchart of an evaluation method for a data model provided in Embodiment 1 of the present invention;

2a is a schematic flowchart of determining the design rationality of the database table included in the data model to be evaluated according to Embodiment 1 of the present invention;

2b is a schematic flowchart of determining the redundancy of the database table according to Embodiment 1 of the present invention;

2c is a schematic flowchart of performing quantization processing on the value of the evaluation index and converting the component value for any evaluation index provided in Embodiment 1 of the present invention;

2d is a schematic flowchart of determining the complexity of the database table according to Embodiment 1 of the present invention;

2e is a schematic flowchart of determining the static weight factor of the database table for any database table according to Embodiment 1 of the present invention;

3a is a schematic flowchart of determining the dynamic running quality of the database table included in the data model to be evaluated according to Embodiment 1 of the present invention;

3b is a schematic flowchart of determining the frequency of the database table according to Embodiment 1 of the present invention;

3c is a schematic flowchart of determining the time delay of the database table according to Embodiment 1 of the present invention;

3d is a schematic flowchart of determining the stability of the database table according to Embodiment 1 of the present invention;

3e is a schematic flowchart of determining the dynamic weight factor of the database table for any database table according to Embodiment 1 of the present invention;

4 is a schematic flowchart of determining a comprehensive evaluation result of the data model to be evaluated according to Embodiment 1 of the present invention;

FIG. 5 is a schematic structural diagram of an apparatus for evaluating a data model according to Embodiment 2 of the present invention.

Detailed ways

The data model evaluation method, device, and electronic device provided by the embodiments of the present invention are used to avoid the participation of subjective judgments, and can evaluate the data model more objectively in combination with actual application scenarios.

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, but not to limit the present invention, and in the case of no conflict, the present invention The embodiments in and features in the embodiments can be combined with each other.

Example 1

As shown in FIG. 1, it is a schematic flowchart of an evaluation method for a data model provided in Embodiment 1 of the present invention, which may include the following steps:

S11. Based on the metadata structure of the data model to be evaluated, determine the association relationship between each database table included in the data model to be evaluated.

During specific implementation, the data model generally includes database tables, associations between tables, and table fields. The content described by the data model consists of three parts, namely data structure, data operation and data constraint. Data structure is used to describe the static characteristics of the system, including the type, content, nature of data and the connection between data, etc.; data operation is used to describe the dynamic characteristics of the system, including data insertion, modification, deletion and query; data constraints It is actually a collection of integrity rules. Integrity rules refer to the constraints and storage rules of the data and its connections in a given data model, which are used to limit the changes in the database and its state that conform to the data model to ensure the correctness, validity and compatibility of the data. .

Based on this, when evaluating the data model to be evaluated, based on the metadata structure of the data model to be evaluated, the relationship and constraints between the data, the association relationship between each database table in the data model to be evaluated can be determined. .

S12 , according to the association relationship between each database table, respectively determine the theoretical design index of each database table and the static weight factor of each database table.

During specific implementation, after the association relationship between each database table is determined, since the data structure in the data model describes the static characteristics of the system, the theoretical design index of each database table can be determined on this basis.

The theoretical design index involved in the embodiment of the present invention is a static index of the data model to be evaluated, and can be used to describe the rationality, redundancy, and complexity of the data model to be evaluated at the design level.

Preferably, the theoretical design index includes the design rationality of the database table included in the data model to be evaluated; and for any database table, it can be determined according to the method shown in FIG. The design rationality of the database table:

S21. Determine the redundancy and complexity of the database table.

During specific implementation, the redundancy of the database table can be determined according to the method shown in FIG. 2b, including the following steps:

S31. Determine the redundancy evaluation index of the database table.

Preferably, the redundancy evaluation index may include, but is not limited to, redundant field ratio and redundant association ratio, and the like.

Specifically, when the redundancy evaluation index is the redundancy field ratio and the redundancy association ratio, the steps of steps S321 to S325 may be performed respectively for these two evaluation indicators, so as to obtain the score obtained by converting the redundancy field ratio, And the score obtained by converting the redundancy correlation ratio.

S32: Quantize the value of each redundancy evaluation index and convert the component value.

During specific implementation, for any evaluation index, quantizing the value of the evaluation index and converting the component values may include the steps shown in Figure 2c:

S321. Divide at least one numerical range according to the value of the evaluation index in each database table.

Taking the redundancy field ratio as an example to illustrate, since each database table in the data model to be evaluated can calculate the redundancy field ratio, assuming there are m database tables, m redundancy field ratios can be obtained, based on The m redundancy field ratios can be divided into N numerical ranges according to the clustering algorithm, that is, corresponding to N clusters, and there is no intersection between the N numerical ranges.

After N numerical ranges are determined, for any database table, perform the following process:

S322. For the value of the evaluation index in any database table, determine the numerical range in which the value of the evaluation index is located.

After N numerical ranges are divided in step S321, for any database table of the data model to be evaluated, the processes of steps S322 to S325 are performed to determine the numerical range corresponding to the redundant field ratio obtained based on the database table.

S323: Determine the absolute value of the difference between the central value in the numerical range and the value of the evaluation index.

After the numerical range corresponding to the redundant field ratio is determined, the central value of the numerical range can be determined, and then the absolute value of the difference between the redundant field ratio and the central value can be determined.

S324. According to the score range corresponding to the numerical range, determine the score corresponding to the central value within the numerical range.

In a specific implementation, after N numerical ranges are determined, the efficacy coefficient method is used, and N score ranges are correspondingly obtained based on the N numerical ranges. For example, the maximum value in this score range can be 100. Specifically, 100 points can be divided into N grades on average, and the value space of each grade is the same. For example, when N is 5, 100 points can be divided into 5 score ranges, which are 1-20, 21-40 , 41～60, 61～80, 81～100. These five score ranges correspond to five numerical ranges respectively. In addition, each numerical range has a central value, correspondingly, the central value in any numerical range corresponds to a score in its corresponding score range.

Therefore, after the numerical range corresponding to the redundant field ratio of the database table is determined, the score range corresponding to the numerical range can be determined. Further, it is determined that the central value in the numerical range corresponds to a score in its corresponding score range.

S325. According to the attribute of the evaluation index, the score corresponding to the central value in the numerical range, the absolute value of the difference between the central value in the numerical range and the value of the evaluation index, the maximum value of the numerical range and the The number of divided numerical ranges is converted to obtain the score of the evaluation index.

During specific implementation, any evaluation index has a certain attribute, and the attribute is used to measure whether the evaluation index is a positive index or a reverse index. The so-called positive index, the larger the value, the better the design of the database table. The so-called reverse index, the larger the value, the worse the design of the database table.

Therefore, in order to objectively obtain the evaluation result of the data model to be evaluated, when converting the evaluation index, it is necessary to use a corresponding formula to obtain the score of the evaluation index according to the attribute of the evaluation index.

When the attribute of the evaluation index is positive, then according to formula (1), determine the score obtained by converting the evaluation index:

When the attribute of the evaluation index is inverse, then according to formula (2), determine the score obtained by converting the evaluation index:

In formula (1) and formula (2), QL3 represents the score obtained by conversion of any evaluation index in any database table; MID represents that the center point of the numerical range where any evaluation index in any database table is located is in the numerical value. The corresponding score in the score range corresponding to the range; DIS represents the absolute value of the difference between the central value of the numerical range where the value of any evaluation index in any database table is located and the value of the evaluation index; DIS _max represents The maximum value of the numerical range in which the value of any evaluation index in any database table is located; N represents the number of numerical ranges divided for any evaluation index based on the value of the evaluation index in each database table.

During specific implementation, in the redundancy evaluation index in the embodiment of the present invention, the attributes of the redundant field ratio and the redundant association ratio are both reversed. Then, it needs to be determined according to formula (2) when determining the redundant field ratio in the database table. After determining the scores obtained by the ratio of redundant fields in the database table, the scores obtained by the ratio of redundant fields in each database table can be determined based on the same method.

When determining the scores obtained by converting the redundant association ratios in each database table, the scores obtained by converting the redundant association ratios in each database table may be determined according to steps S321 to S325. It should be noted that, since the attribute of the redundant correlation ratio is inverse, when step S325 is executed, it is necessary to determine the score obtained by converting the redundant correlation ratio in each database table according to formula (2) according to the steps.

S33: Perform weighted summation processing on the scores of each redundancy evaluation index to determine the redundancy of the database table.

During specific implementation, the redundancy of the database table can be determined according to formula (3):

In formula (3), QL2 represents the redundancy of the database table; M represents the number of redundancy evaluation indexes; j represents the jth redundancy evaluation index; QL3 _j represents the jth redundancy evaluation index converted to ; the weight corresponding to the jth redundancy evaluation index of W _j , and the weight can be determined according to the actual situation.

During specific implementation, since the redundancy evaluation index includes the redundant field ratio and the redundant association ratio, when using formula (3) to determine the redundancy of the database table, M in formula (3) is 2, then the data The redundancy of the table is: QL2=W ₁ *QL3 ₁ +W ₂ *QL3 ₂ .

In addition, when the redundancy evaluation index of each database table includes redundant field ratio and redundant association ratio, the redundancy of each database table can be expressed as: QL2 _i =W _i1 *QL3 _i1 +W _i2 *QL3 _i2 , where i represents the ith database table.

Preferably, the complexity of the database table can be determined according to the process shown in Figure 2d, including the following steps:

S41. Determine the complexity evaluation index of the database table.

Preferably, the complexity evaluation index may include, but is not limited to, the number of dependent database tables in the upper layer, the number of dependent database tables and the number of associated dimensions in the lower layer, and the like.

S42: Quantize the value of each complexity evaluation index and convert the component value.

During specific implementation, the determination may be performed according to the steps of steps S321 to S325, and the determination method is similar to the method for determining the score obtained by conversion of the redundant field ratio, and details are not repeated here. It should be noted that the attributes of the number of upper-layer dependent database tables and the number of associated dimensions involved in the embodiment of the present invention are both positive, then when step S325 is executed, the upper-layer dependent database tables can be determined respectively according to formula (1). The score obtained by digital conversion and the score obtained by the conversion of the associated dimension number; the attribute of the number of tables in the lower-level dependent database table involved in the embodiment of the present invention is reversed, then when step S325 is performed, it can be determined according to formula (2). Depends on the score converted from the number of database tables.

S43. Perform a weighted summation process on the scores obtained by converting each complexity evaluation index to determine the complexity of the database table.

During specific implementation, when determining the complexity of the database table, it can be determined according to formula (3). It should be noted that, when using formula (3) to determine the complexity of the database table, the meaning of each parameter in formula (3) is different from the physical meaning of each parameter in formula (3) when determining the redundancy of the database table. same. For example, when determining the complexity, QL2 represents the complexity of the database table; M represents the number of complexity evaluation indicators; j represents the jth complexity evaluation indicator; QL3 _j represents the score obtained by converting the jth complexity evaluation indicator value; W _j represents the weight corresponding to the jth complexity evaluation index, the weight can be determined according to the actual situation, and the weight corresponding to the jth complexity evaluation index and the weight corresponding to the jth redundancy evaluation index The values may be the same or different, depending on the actual situation.

Specifically, when the complexity evaluation index of the database table in the embodiment of the present invention includes the number of upper-level dependent database tables, the number of lower-level dependent database tables, and the number of associated dimensions, then formula (3) is used to determine the complexity of the database table. When M is 3 in formula (3), the complexity of the database table is expressed as: QL2=W ₁ *QL3 ₁ +W ₂ *QL3 ₂ +W ₃ *QL3 ₃ .

On this basis, when the complexity evaluation indicators of each database table include the number of upper-level dependent database tables, the number of lower-level dependent database tables, and the number of associated dimensions, then when determining the complexity of each database table, the complexity of each data table The degree can be expressed as: QL2 _i =W _i1 *QL3 _i1 +W _i2 *QL3 _i2 +W _i3 *QL3 _i3 , where i represents the ith database table.

S22. Perform weighted summation processing on the redundancy and complexity to determine the design rationality of the database table included in the data model to be evaluated.

During specific implementation, when determining the redundancy and complexity of the database table, the design rationality of the database table included in the data model to be evaluated can be determined according to formula (4):

In formula (4), QL1 represents the design rationality of the database table included in the data model to be evaluated; QL2 _k represents the redundancy or complexity of the database table; K is 2; _wk the redundancy of the database table. The weight corresponding to the redundancy or the weight corresponding to the complexity of the database.

Specifically, the design rationality of the database table included in the data model to be evaluated can be expressed as: QL1=w ₁ *QL2 ₁ +w ₂ *QL2 ₂ .

On this basis, the design rationality of each database table included in the data model to be evaluated can be expressed as: QL1 _i =wi1 *QL2 _i1 + _wi2 *QL2 _i2 , where _i represents the ith database table.

When determining the design rationality of each database table included in the data model to be evaluated, it is also necessary to determine the static weighting factor of each database table. In specific implementation, for any database table, the process shown in FIG. 2e can be followed. Implementation, including the following steps:

S51. Determine the depth values of all aggregation paths in the database table.

During specific implementation, the aggregation path of the database table may be obtained based on the metadata structure of the data model.

Specifically, the tables in the database include the most primitive table, and there are also newly generated tables by aggregating based on the most primitive table, so there is the concept of an aggregation path. The aggregation path refers to the path through which a database table in the data model is generated from the most primitive table through level-by-level aggregation; the depth of the aggregation path is also the number of aggregations. For example, A->B->C->D->E, assuming that database table A is the most original database table, database B is an hourly database table based on the most original database table aggregated in a certain hour, and database table C is based on is the daily database table obtained by the aggregation of database table B on the same day; database table D is the monthly database table obtained based on the aggregation of database table C in the current month, etc., the depth value of the aggregation path of database table D is 3, and the depth value of database table B is the same. is 1.

S52. Determine the average value of the depth values of all the aggregation paths as the static weight factor of the database table.

During specific implementation, after determining the depth values of all the aggregation paths of the database table, the static weight factor of the database table can be determined according to formula (5):

In formula (5), FS represents the static weight factor of the database table; L represents the number of aggregation paths included in the database table; Depth _l represents the depth value of the lth aggregation path in the database table.

Based on formula (5), the static weight factor of the database table can be obtained, and similarly, the static weight factor FS _i of each database table can be obtained, wherein i represents the ith database table.

S13 , according to the monitored access records of each database table, respectively determine the dynamic operation index of each database table, and determine the dynamic weight factor of each database table.

During specific implementation, in order to ensure the objectivity of the data model to be evaluated, the embodiment of the present invention determines the dynamic operation index of each database table based on the access conditions of each database table in the data model to be evaluated, thereby combining the The dynamic factors of the data model to be evaluated are used to evaluate the data model to be evaluated, so that the data model to be evaluated is more practical.

Preferably, the dynamic operation index includes the dynamic operation quality of the database table included in the data model to be evaluated; and for any database table, it can be determined according to the method shown in FIG. The dynamic running quality of this database table:

S61. Determine the frequency, time delay and stability of the database table respectively.

During specific implementation, the frequency of the database table can be determined according to the method shown in FIG. 3b, including the following steps:

S71. Determine the frequency evaluation index of the database table.

Preferably, the frequency evaluation index may include, but is not limited to, the daily average access times of database tables, the daily average access times of upper-level database tables, and the ratio of secondary average access fields.

S72: Quantize the value of each frequency evaluation index and convert the component value.

During specific implementation, the determination may be performed according to the steps of steps S321 to S325, and the determination method is similar to the method for determining the score obtained by conversion of the redundant field ratio, and details are not repeated here. It should be noted that the attributes of the daily average access times of the database table, the daily average access times of the upper-level database table, and the sub-average access field ratio involved in the embodiment of the present invention are all positive. Formula (1) is used to determine the points converted from the daily average access times of the database table, the points converted from the daily average access times of the upper database table and the points converted from the sub-average access field ratio.

For the convenience of description, the present invention will determine the frequency evaluation index, delay evaluation index and stability evaluation index to be determined under the dynamic running quality and the symbol of the score obtained by conversion and the redundant code to be determined under the design rationality based on the database table. The marks obtained by the conversion of the redundancy evaluation index and the complexity evaluation index are distinguished. For any database table, RL3 is used to represent the converted score. The conversion is similar to formulas (2) and (3), except that the QL2 in formulas (2) and (3) is changed to RL3, and others remain unchanged. The corresponding determined frequency, delay and stability are used for RL2 representation.

S73: Perform a weighted summation process on the scores obtained by converting each frequency evaluation index to determine the frequency of the database table.

During specific implementation, when determining the frequency of the database table, it can be determined according to formula (6):

In formula (6), RL2 represents the frequency of the database table; M represents the number of frequency evaluation indexes; j represents the jth frequency evaluation index; RL3 _j represents the score converted from the jth frequency evaluation index; The weight corresponding to the jth frequency evaluation index of Q _j , the weight can be determined according to the actual situation.

Specifically, when the frequency evaluation index of the database table in the embodiment of the present invention includes the daily average access times of the database table, the daily average access times of the upper-level database table, and the sub-average access field ratio, then the formula (3) is used to determine When the frequency of the database table, M in formula (3) is 3, then the frequency of the database table is expressed as: RL2=Q ₁ *RL3 ₁ +Q ₂ *RL3 ₂ +Q ₃ *RL3 ₃ .

On this basis, when the frequency evaluation index of each database table includes the daily average number of accesses to the database table, the daily average number of accesses to the upper-level database table, and the ratio of secondary average access fields, then when determining the frequency of each database table, The frequency of each data table can be expressed as: RL2 _i =Q _i1 *RL3 _i1 +Q _i2 *RL3 _i2 +Q _i3 *RL3 _i3 , where i represents the ith database table.

Specifically, the time delay of the database table can be determined according to the process shown in Figure 3c, including the following steps:

S81. Determine the time delay evaluation index of the database table.

Preferably, the delay evaluation index may include, but is not limited to, the sub-average access delay, the sub-average data generation delay, and the like.

S82: Quantize the value of each time delay evaluation index and convert the component value.

During specific implementation, the determination may be performed according to the steps of steps S321 to S325, and the determination method is similar to the method for determining the score obtained by conversion of the redundant field ratio, and details are not repeated here. It should be noted that the attributes of the sub-average access delay and the sub-average data generation delay involved in this embodiment of the present invention are both inverse, so when step S325 is executed, the sub-average access time can be determined respectively according to formula (2). Delay-converted scores and sub-average data generation delay-converted scores.

S83. Perform weighted summation processing on the scores obtained by converting each time delay evaluation index to determine the time delay of the database table.

During specific implementation, the time delay for determining the database table may be determined according to formula (6). It should be noted that when using formula (6) to determine the time delay of the database table, the meaning of each parameter in formula (6) is different from the physical meaning of each parameter in formula (6) when determining the frequency of the database table. . For example, in determining the delay, RL2 represents the delay of the database table; M represents the number of delay evaluation indicators; j represents the jth delay evaluation index; RL3 _j represents the score obtained by converting the jth delay evaluation index Q _j represents the weight corresponding to the jth delay evaluation index, the weight can be determined according to the actual situation, and the weight corresponding to the jth delay evaluation index is the same as the weight corresponding to the jth frequency evaluation index above. The value, the weight corresponding to the jth complexity evaluation index, and the weight corresponding to the jth redundancy evaluation index may be the same or different, which are determined according to the actual situation.

Specifically, when the time delay evaluation index of the database table in the embodiment of the present invention includes the sub-average access delay and the sub-average data generation time delay, then the time delay of the database table is determined by using formula (6), and the formula ( M in 6) is 2, then the time delay of the database table is expressed as: RL2=Q ₁ *RL3 ₁ +Q ₂ *RL3 ₂ .

On this basis, the delay evaluation index of each database table includes the sub-average access delay and the sub-average data generation delay. When determining the frequency of each database table, the delay of each data table can be expressed as: RL2 _i =Q _i1 *RL3 _i1 +Q _i2 *RL3 _i2 , where i represents the ith database table.

Preferably, the stability of the database table can also be determined according to the process shown in Figure 3d, including the following steps:

S91. Determine the stability evaluation index of the database table.

Preferably, the stability evaluation index includes the number of changes of the database table and the like.

S92, quantizing the value of the stability evaluation index and converting the component value.

During specific implementation, the determination may be performed according to the steps of steps S321 to S325, and the determination method is similar to the method for determining the score obtained by conversion of the redundant field ratio, and details are not repeated here. It should be noted that, the attribute of the number of changes of the database table involved in the embodiment of the present invention is inverse, and when step S325 is executed, the score converted from the number of changes of the database table can be determined according to formula (2).

S93: Perform a weighted summation process on the scores converted from each stability evaluation index to determine the stability of the database table.

If the stability evaluation index only includes the number of changes of the database table, step S93 specifically includes: determining the score obtained by converting the stability evaluation index as the stability of the database table.

When the stability evaluation index only includes the number of changes of the database table, the stability of the database table can be expressed as: RL2=RL3, where RL3 represents the score obtained by converting the number of changes of the database table, RL2 Indicates the stability of the database table.

On this basis, when the stability evaluation index of each database table only includes the number of changes of the database table, when determining the stability of each database table, the stability of each database table can be expressed as: RL2 _i =RL3 _i , where i represents the ith database table.

When the stability evaluation index includes at least two evaluation indexes, formula (3) needs to be used to determine the stability of the database table.

S62. Perform weighted summation on the frequency, time delay and stability to determine the dynamic running quality of the database table included in the data model to be evaluated.

During specific implementation, when determining the dynamic running quality of the database table included in the data model to be evaluated, it can be determined according to formula (7):

In formula (7), RL1 represents the dynamic running quality of the database table included in the data model to be evaluated; RL2 _k represents the frequency, delay or stability of the database table; K is 3; q _k the database table The weight corresponding to the frequency, the weight corresponding to the delay of the database, or the weight corresponding to the stability of the database table.

Specifically, the dynamic running quality of the database table included in the data model to be evaluated can be expressed as: RL1=q ₁ *RL2 ₁ +q ₂ *RL2 ₂ +q ₃ *RL2 ₃ .

On this basis, the dynamic running quality of each database table included in the data model to be evaluated can be expressed as: RL1 _i =q _i1 *RL2 _i1 +q _i2 *RL2 _i2 +q _i3 *RL2 _i3 , where i represents the first i database tables.

When determining the dynamic running quality of each database table included in the data model to be evaluated, it is also necessary to determine the dynamic weight factor of each database table. In specific implementation, for any database table, the process shown in FIG. 3e can be followed. Implementation, including the following steps:

S101. Determine the daily incremental data volume of the database table and the sum of the daily incremental data volumes of all database tables.

S102. Determine the ratio of the daily incremental data volume of the database table to the sum as the dynamic weight factor of the database table.

During specific implementation, after determining the sum of the daily incremental data volume of the database table and the daily incremental data volume of the database table, the dynamic weight factor of the database table can be determined according to formula (8):

In formula (8), FR _i represents the dynamic weight factor of the ith database table; n represents the number of database tables included in the data model to be evaluated; Size _i represents the daily incremental data volume of the ith database table.

During specific implementation, the size of the storage space occupied by each database table can be determined, and thus the daily incremental data volume of each database table can be determined.

S14. Determine the comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table.

During specific implementation, when step S14 is performed, the flow shown in FIG. 4 may be followed, including the following steps:

S141. For any database table, determine the first product of the design rationality of the database table and the static weighting factor, and determine the second product of the dynamic running quality of the database table and the dynamic weighting factor.

During specific implementation, the first product corresponding to the i-th database table can be determined according to formula (9):

TQS _i =FS _i *QL1 _i (9)

In formula (9), TQS _i represents the first product of the ith database table; FS _i represents the static weight factor of the ith database table; QL1 _i represents the ith database table included in the data model to be evaluated. Design reasonableness.

Similarly, the second product corresponding to the ith database table can be determined according to formula (10):

TQR _i =FR _i *RL1 _i (10)

In formula (10), TQR _i represents the second product of the ith database table; FR _i represents the dynamic weight factor of the ith database table; RL1 _i represents the ith database table included in the data model to be evaluated. Dynamic running quality.

S142. Determine the evaluation result of the database table according to the first product and the second product.

During specific implementation, the evaluation result of the i-th database table can be determined according to formula (11):

TQ _i =TQS _i *TQR _i (11)

In formula (11), TQ _i represents the evaluation result of the ith database table.

S143. Based on the evaluation results of all database tables, determine the comprehensive evaluation result of the data model to be evaluated.

After determining the evaluation results of each database table included in the data model to be evaluated, the comprehensive evaluation result of the data model to be evaluated can be determined according to formula (12):

In formula (12), MQ represents the comprehensive evaluation result of the data model to be evaluated.

So far, the comprehensive evaluation result of the data model to be evaluated can be determined. Specifically, the larger the value of MQ, the better the overall design and operation quality of the data model to be evaluated. Determine the quality and deterioration trend of the data model to be evaluated. In addition, on the basis of obtaining the comprehensive evaluation results of the data model to be evaluated, the TQ value of each database table, the ratio of TQS/TQR, and the scores of QL1, QL2, QL3, RL1, RL2 and RL3 can be determined. The index of each database table included in the data model to be evaluated is good or bad, and then targeted optimization can be performed on the data model to be evaluated. For example, for database tables with low TQ values in the data model to be evaluated, and indicators with low scores in the database tables, targeted problem analysis can be performed on these database tables and the indicators in these database tables. The data model to be evaluated is optimized as described above, so that the finally obtained data model has more use value.

Further, the evaluation index in the embodiment of the present invention can be obtained statistically from the system, the numerical value is clear, the standard is unified, and no manual judgment is required. That is to say, the evaluation index in the embodiment of the present invention is objectively measurable, which reduces subjective judgment.

Further, when the embodiment of the present invention performs comprehensive evaluation on the data model to be evaluated, since the comprehensive evaluation result is determined based on the monitored access records of each database table and the theoretical design index of the data model to be evaluated, not only the The rationality of the static design of the data model to be evaluated is also introduced, and the dynamic operation index used to measure the dynamic operation effect of the data model is also introduced. The process of comprehensive evaluation of the data model to be evaluated is linked to the perspective operation status of the data model to be evaluated, which is more suitable for practical applications.

The data model evaluation method provided by the embodiment of the present invention determines the association relationship between each database table included in the to-be-evaluated data model based on the metadata structure of the to-be-evaluated data model; according to the association relationship between the various database tables , respectively determine the theoretical design index of each database table and the static weight factor of each database table; and according to the monitored access records of each database table, respectively determine the dynamic operation index of each database table, and determine the dynamic weight factor of each database table ; According to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table, determine the comprehensive evaluation result of the data model to be evaluated. By adopting the method provided by the present invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, which can not only objectively evaluate each data model to be evaluated, but also greatly reduces the participation of subjective evaluation, and at the same time ensures the integrity of the data model. Design rationality and practicality.

Embodiment 2

Based on the same inventive concept, the embodiment of the present invention also provides a data model evaluation device. Since the above-mentioned device solves the problem in a similar way to the data model evaluation method, the implementation of the above-mentioned device can refer to the implementation of the method. No longer.

As shown in FIG. 5, it is a schematic structural diagram of an evaluation device for a data model provided in Embodiment 2 of the present invention, including a first determination unit 51, a second determination unit 52, a third determination unit 53, and a fourth determination unit 54, wherein:

The first determining unit 51 is configured to determine the association relationship between each database table included in the data model to be evaluated based on the metadata structure of the data model to be evaluated;

The second determination unit 52 is configured to respectively determine the theoretical design index of each database table and the static weight factor of each database table according to the association relationship between each database table;

The third determining unit 53 is used to determine the dynamic operation index of each database table according to the monitored access records of each database table, and determine the dynamic weight factor of each database table;

The fourth determining unit 54 is configured to determine the comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table.

Preferably, the second determining unit 52 is specifically configured to perform the following process for any database table: determine the depth values of all the aggregation paths in the database table; and determine that the average value of the depth values of all the aggregation paths is: The static weighting factor for this database table.

Preferably, the third determining unit 53 is specifically configured to perform the following process for any database table: determine the daily incremental data volume of the database table and the sum of the daily incremental data volume of all database tables; The ratio of the daily incremental data volume of the table to the sum is determined as the dynamic weight factor of the database table.

Preferably, the theoretical design index includes the design rationality of the database table included in the data model to be evaluated; and

The second determination unit 52 is specifically configured to determine the design rationality of the database table included in the data model to be evaluated according to the following method for any database table: determine the redundancy and complexity of the database table ; and perform weighted summation processing on the redundancy and complexity to determine the design rationality of the database table included in the data model to be evaluated.

Preferably, the second determining unit 52 is specifically configured to determine the redundancy evaluation index of the database table; quantify the value of each redundancy evaluation index and convert the component value; The scores are weighted and summed to determine the redundancy of the database table; and determine the complexity evaluation index of the database table; quantify the value of each complexity evaluation index and convert the component value; evaluate each complexity The scores obtained from the index conversion are subjected to weighted summation processing to determine the complexity of the database table.

Preferably, the redundancy evaluation index includes at least one of the following: redundant field ratio and redundant association ratio; and the complexity evaluation index includes at least one of the following: the number of database tables that the upper layer depends on, and the number of database tables that the lower layer depends on. The number of database tables and associated dimensions.

Preferably, the dynamic operation index includes the dynamic operation quality of the database table included in the data model to be evaluated; and

The third determining unit 53 is specifically configured to, for any database table, determine the dynamic running quality of the database table included in the data model to be evaluated according to the following method: determine the frequency and time delay of the database table respectively. and stability; and weighted summation of the frequency, time delay and stability to determine the dynamic running quality of the database table included in the data model to be evaluated.

Preferably, the third determining unit 53 is specifically used to determine the frequency evaluation index of the database table; quantify the value of each frequency evaluation index and convert the component value; The scores are weighted and summed to determine the frequency of the database table; and the delay evaluation index of the database table is determined; the value of each delay evaluation index is quantified and the component value is converted; each delay evaluation index is converted The obtained scores are processed by weighted summation to determine the time delay of the database table; and determine the number of changes in the database table; quantify the number of changes and convert the component values; The scores are weighted and summed to determine the stability of the database table.

Preferably, the frequency evaluation index includes at least one of the following: the daily average number of visits to the database table, the daily average number of visits to the upper-level database table, and the ratio of second-average access fields; and the delay evaluation index includes at least one of the following : sub-average access delay and sub-average data generation delay; and the stability evaluation index includes the number of changes of the database table.

Preferably, the second determining unit 52 is specifically configured to divide at least one numerical range according to the value of the evaluation index in each database table; and determine the evaluation index for the value of the evaluation index in any database table. and determine the absolute value of the difference between the central value within the numerical range and the value of the evaluation index; determine the central value within the numerical range according to the score range corresponding to the numerical range The corresponding score; according to the attribute of the evaluation index, the score corresponding to the central value in the numerical range, the absolute value of the difference between the central value in the numerical range and the value of the evaluation index, the The maximum value and the number of divided numerical ranges are converted to obtain the score of the evaluation index.

Preferably, the third determining unit 53 is specifically configured to divide at least one numerical range according to the value of the evaluation index in each database table; and determine the evaluation index for the value of the evaluation index in any database table. and determine the absolute value of the difference between the central value within the numerical range and the value of the evaluation index; determine the central value within the numerical range according to the score range corresponding to the numerical range The corresponding score; according to the attribute of the evaluation index, the score corresponding to the central value in the numerical range, the absolute value of the difference between the central value in the numerical range and the value of the evaluation index, the The maximum value and the number of divided numerical ranges are converted to obtain the score of the evaluation index.

Preferably, the fourth determining unit 54 is specifically configured to, for any database table, determine the first product of the design rationality of the database table and the static weighting factor, and determine the dynamic running quality and dynamic weighting factor of the database table the second product; determining the evaluation result of the database table according to the first product and the second product; determining the comprehensive evaluation result of the data model to be evaluated based on the evaluation results of all database tables.

For the convenience of description, the above parts are divided into modules (or units) according to their functions and described respectively. Of course, when implementing the present invention, the functions of each module (or unit) may be implemented in one or more software or hardware.

Embodiment 3

Embodiment 3 of the present invention provides a communication device, including a memory, a processor, and a computer program stored on the memory and running on the processor; the processor performs the following steps:

Based on the metadata structure of the data model to be evaluated, determine the association relationship between each database table included in the data model to be evaluated;

According to the relationship between each database table, the theoretical design index of each database table and the static weight factor of each database table are determined respectively;

According to the monitored access records of each database table, determine the dynamic operation index of each database table, and determine the dynamic weight factor of each database table;

The comprehensive evaluation result of the data model to be evaluated is determined according to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table.

Embodiment 4

The fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, implements any one of the data model evaluation methods provided in the first embodiment of the present invention. step.

The data model evaluation method, device and device provided by the embodiments of the present invention determine the association relationship between each database table included in the to-be-evaluated data model based on the metadata structure of the to-be-evaluated data model; The relationship between each database table is determined, and the theoretical design index of each database table and the static weight factor of each database table are respectively determined; and according to the monitored access records of each database table, the dynamic operation indicators of each database table are determined respectively, and each database table is determined. According to the theoretical design index and static weight factor of each database table, and the dynamic operation index and dynamic weight factor of each database table, the comprehensive evaluation result of the data model to be evaluated is determined. By adopting the method provided by the present invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, which can not only objectively evaluate each data model to be evaluated, but also greatly reduces the participation of subjective evaluation, and at the same time ensures the integrity of the data model. Design rationality and practicality.

Further, the evaluation index in the embodiment of the present invention can be obtained statistically from the system, the numerical value is clear, the standard is unified, and no manual judgment is required. That is to say, the evaluation index in the embodiment of the present invention is objectively measurable, which reduces subjective judgment.

Further, when the embodiment of the present invention performs comprehensive evaluation on the data model to be evaluated, since the comprehensive evaluation result is determined based on the monitored access records of each database table and the theoretical design index of the data model to be evaluated, not only the The rationality of the static design of the data model to be evaluated is also introduced, and the dynamic operation index used to measure the dynamic operation effect of the data model is also introduced. The process of comprehensive evaluation of the data model to be evaluated is linked to the perspective operation status of the data model to be evaluated, which is more suitable for practical applications.

The apparatus for evaluating the data model provided by the embodiments of the present application may be implemented by a computer program. Those skilled in the art should be able to understand that the above-mentioned module division method is only one of many module division methods. If it is divided into other modules or not divided into modules, as long as the evaluation device of the data model has the above functions, it should be used in the application. within the scope of protection.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each process and/or block in the flowchart illustrations and/or block diagrams, and combinations of processes and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams.

Although the preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be construed to include the preferred embodiment and all changes and modifications that fall within the scope of the present invention.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (19)

1. A method for evaluating a data model, comprising:

determining an incidence relation between database tables contained in a data model to be evaluated based on a metadata structure of the data model to be evaluated;

respectively determining theoretical design indexes of the database tables and static weight factors of the database tables according to the incidence relation among the database tables; and are

Respectively determining dynamic operation indexes of each database table and determining dynamic weight factors of each database table according to the monitored access records of each database table;

determining a comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and the static weight factor of each database table and the dynamic operation index and the dynamic weight factor of each database table;

determining the static weight factors of each database table specifically comprises the following steps:

the following process is performed for any database table: determining the depth values of all the gathering paths of the database table; determining the average value of the depth values of all the convergent paths as a static weight factor of the database table;

determining dynamic weight factors of each database table, specifically comprising:

the following process is performed for any database table: determining the daily data increment of the database table and the sum of the daily data increment of all the database tables; and determining the ratio of the daily increment data volume of the database table to the sum as the dynamic weight factor of the database table.

2. The method according to claim 1, wherein the theoretical design index includes a design reasonableness of a database table included in the data model to be evaluated; and aiming at any database table, determining the design reasonableness of the database table contained in the data model to be evaluated according to the following method:

determining the redundancy and complexity of the database table; and are

And carrying out weighted summation processing on the redundancy and the complexity, and determining the design reasonableness of the database table contained in the data model to be evaluated.

3. The method of claim 2, wherein determining the redundancy of the database table specifically comprises:

determining a redundancy evaluation index of the database table;

carrying out quantization processing on the value of each redundancy evaluation index and converting the value into a score;

carrying out weighted summation processing on the scores of the redundancy evaluation indexes to determine the redundancy of the database table; and

determining the complexity of the database table specifically includes:

determining a complexity evaluation index of the database table; and are

Carrying out quantization processing on the values of all the complexity evaluation indexes and converting the values into scores;

and carrying out weighted summation processing on the scores obtained by conversion of the complexity evaluation indexes to determine the complexity of the database table.

4. The method of claim 3, wherein the redundancy evaluation index comprises at least one of: a redundant field ratio and a redundant association ratio; and the complexity evaluation index comprises at least one of: the upper level is dependent on the database table number, the lower level is dependent on the database table number and the associated dimension number.

5. The method of claim 1, wherein the dynamic operation index comprises a dynamic operation quality of a database table contained in the data model to be evaluated; and aiming at any database table, determining the dynamic operation quality of the database table contained in the data model to be evaluated according to the following method:

respectively determining the frequency, the time delay and the stability of the database table; and are

And carrying out weighted summation on the frequency, the time delay and the stability, and determining the dynamic operation quality of the database table contained in the data model to be evaluated.

6. The method of claim 5, wherein determining the frequency of the database table specifically comprises:

determining a frequency evaluation index of the database table;

carrying out quantization processing on the value of each frequency evaluation index and converting the value into a score;

carrying out weighted summation processing on the scores obtained by conversion of each frequency evaluation index to determine the frequency of the database table; and

determining the time delay of the database table specifically comprises:

determining a time delay evaluation index of the database table;

carrying out quantization processing on the values of the time delay evaluation indexes and converting the values into values;

carrying out weighted summation processing on the scores obtained by conversion of all the time delay evaluation indexes to determine the time delay of the database table; and

determining the stability of the database table specifically includes:

determining a stability evaluation index of the database table;

carrying out quantization processing on the value of the stability evaluation index and converting the value into a value; and are

And carrying out weighted summation processing on the scores obtained by conversion of the stability evaluation indexes to determine the stability of the database table.

7. The method according to claim 6, wherein the frequency evaluation index includes at least one of: daily average access times of the database table, daily average access times of the upper database table and a secondary average access field ratio; and the time delay evaluation index at least comprises one of the following items: a sub-average access delay and a sub-average data generation delay; and the stability evaluation index comprises the change times of the database table.

8. The method according to claim 3 or 6, wherein, for any evaluation index, the quantization processing is performed on the value of the evaluation index and the value is converted into a score value, and specifically comprises the following steps:

dividing at least one numerical range according to the value of the evaluation index in each database table; and are

Aiming at the value of the evaluation index in any database table, determining the numerical range of the value of the evaluation index; and are

Determining an absolute value of a difference between a central value within the range of values and a value of the evaluation index;

determining a score corresponding to a central value in the numerical range according to the score range corresponding to the numerical range;

and converting to obtain the score of the evaluation index according to the attribute of the evaluation index, the score corresponding to the central value in the numerical range, the absolute value of the difference between the central value in the numerical range and the value of the evaluation index, the maximum value of the numerical range and the number of the divided numerical ranges.

9. The method as claimed in claim 2 or 5, wherein determining the comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and the static weighting factor of each database table, and the dynamic operation index and the dynamic weighting factor of each database table specifically comprises:

aiming at any database table, determining a first product of the design reasonableness of the database table and a static weight factor, and determining a second product of the dynamic operation quality of the database table and a dynamic weight factor;

determining an evaluation result of the database table according to the first product and the second product;

and determining a comprehensive evaluation result of the data model to be evaluated based on the evaluation results of all the database tables.

10. An apparatus for evaluating a data model, comprising:

the first determining unit is used for determining the incidence relation among all database tables contained in the data model to be evaluated based on the metadata structure of the data model to be evaluated;

the second determining unit is used for respectively determining the theoretical design index of each database table and the static weight factor of each database table according to the incidence relation among the database tables;

the third determining unit is used for respectively determining the dynamic operation indexes of the database tables and determining the dynamic weight factors of the database tables according to the monitored access records of the database tables;

the fourth determination unit is used for determining a comprehensive evaluation result of the data model to be evaluated according to the theoretical design index and the static weight factor of each database table and the dynamic operation index and the dynamic weight factor of each database table;

the second determining unit is specifically configured to, for any one of the database tables, perform the following processes: determining the depth values of all the gathering paths of the database table; determining the average value of the depth values of all the convergent paths as a static weight factor of the database table;

the third determining unit is specifically configured to, for any database table, execute the following process: determining the daily data increment of the database table and the sum of the daily data increment of all the database tables; and determining the ratio of the daily increment data volume of the database table to the sum as the dynamic weight factor of the database table.

11. The apparatus of claim 10, wherein the theoretical design metric comprises a design reasonableness of a database table included in the data model to be evaluated; and

the second determining unit is specifically configured to determine, for any one of the database tables, the design reasonableness of the database table included in the data model to be evaluated according to the following method: determining the redundancy and complexity of the database table; and carrying out weighted summation processing on the redundancy and the complexity, and determining the design reasonableness of the database table contained in the data model to be evaluated.

12. The apparatus of claim 11,

the second determining unit is specifically configured to determine a redundancy evaluation index of the database table; carrying out quantization processing on the value of each redundancy evaluation index and converting the value into a score; carrying out weighted summation processing on the scores of the redundancy evaluation indexes to determine the redundancy of the database table; determining the complexity evaluation index of the database table; the values of all the complexity evaluation indexes are subjected to quantization processing and converted into values; and carrying out weighted summation processing on the scores obtained by conversion of the complexity evaluation indexes to determine the complexity of the database table.

13. The apparatus of claim 12, wherein the redundancy evaluation index comprises at least one of: a redundant field ratio and a redundant association ratio; and the complexity evaluation index comprises at least one of: the upper level is dependent on the database table number, the lower level is dependent on the database table number and the associated dimension number.

14. The apparatus of claim 10, wherein the dynamic operation metrics comprise a dynamic operation quality of a database table contained in the data model to be evaluated; and

the third determining unit is specifically configured to determine, for any database table, the dynamic operation quality of the database table included in the data model to be evaluated according to the following method: respectively determining the frequency, the time delay and the stability of the database table; and carrying out weighted summation on the frequency, the time delay and the stability to determine the dynamic operation quality of the database table contained in the data model to be evaluated.

15. The apparatus of claim 14,

the third determining unit is specifically configured to determine a frequency evaluation index of the database table; carrying out quantization processing on the value of each frequency evaluation index and converting the value into a score; carrying out weighted summation processing on the scores obtained by conversion of each frequency evaluation index to determine the frequency of the database table; determining a time delay evaluation index of the database table; carrying out quantization processing on the values of the time delay evaluation indexes and converting the values into values; carrying out weighted summation processing on the scores obtained by conversion of all the time delay evaluation indexes to determine the time delay of the database table; and determining the change times of the database table; quantizing the variation times and converting the variation times into scores; and carrying out weighted summation processing on the scores obtained by conversion of the stability evaluation indexes to determine the stability of the database table.

16. The apparatus of claim 15, wherein the frequency evaluation index comprises at least one of: daily average access times of the database table, daily average access times of the upper database table and a secondary average access field ratio; and the time delay evaluation index at least comprises one of the following items: a sub-average access delay and a sub-average data generation delay; and the stability evaluation index comprises the change times of the database table.

17. The apparatus of claim 11 or 14,

the fourth determining unit is specifically configured to determine, for any one of the database tables, a first product of the design reasonableness of the database table and the static weight factor, and determine a second product of the dynamic operation quality of the database table and the dynamic weight factor; determining an evaluation result of the database table according to the first product and the second product; and determining a comprehensive evaluation result of the data model to be evaluated based on the evaluation results of all the database tables.

18. A communication device comprising a memory, a processor and a computer program stored on the memory and executable on the processor; wherein the processor implements the method for evaluating a data model according to any one of claims 1 to 9 when executing the program.

19. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of evaluating a data model according to any one of claims 1 to 9.

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