CN109726879B

CN109726879B - Data model evaluation method, device and equipment

Info

Publication number: CN109726879B
Application number: CN201711027595.XA
Authority: CN
Inventors: 段伟希; 魏丽红; 孙金霞; 葛澍; 孔松; 梁双春; 崔俊交; 马庆
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Suzhou Software Technology Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Suzhou Software Technology Co Ltd
Priority date: 2017-10-27
Filing date: 2017-10-27
Publication date: 2020-12-01
Anticipated expiration: 2037-10-27
Also published as: CN109726879A

Abstract

The invention discloses a method, a device and equipment for evaluating a data model, wherein the method comprises the following steps: determining an incidence relation between database tables contained in the 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; respectively determining dynamic operation indexes of each database table according to the monitored access records of each database table, and determining dynamic weight factors of each database table; and 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. According to the method provided by the invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, so that the obtained comprehensive evaluation result is more objective and is fit for practical application.

Description

Data model evaluation method, device and equipment

Technical Field

The invention relates to the technical field of IT (information technology) business support, in particular to a method, a device and equipment for evaluating a data model.

Background

The data model is the core and the foundation of the IT system, in a database system, the data model is used for describing the structure and the semantics of a database, and the design quality of the data model directly influences the operation effect of the system.

A data warehouse is a theme-oriented, integrated, relatively stable data collection that reflects historical changes used to support administrative decisions. The data warehouse stores analytical data, and is mainly used for collecting various data of enterprises and performing online analysis processing.

In order to effectively support front-end query and analysis, data warehouse modeling generally adopts a layered modeling method and is divided into a basic data layer, a summary layer, a data mart layer and an application layer. The basic data layer stores and integrates the finest granularity data, and a unified data view is constructed to meet the unpredictable requirement; the summary layer generally adopts dimension modeling, fine-grained summary index calculation is carried out on detail data from multiple dimensions, and statistical analysis application is supported; the application layer is used for further summarizing and index calculation facing report reports and thematic analysis applications and directly supporting the applications. Therefore, the data warehouse model usually has redundancy, one index data usually appears in different granularity and different dimension combinations for many times, and the reasonability of the data upward convergence path greatly affects the analysis efficiency of the system. Therefore, there is a need for a methodology that is operable to perform a scientifically efficient evaluation of data models.

In the prior art, a scheme for evaluating a data model is mainly defined in international standards and thesis, and is focused on an abstract information model level without involving the landing realization of an IT system. For example, the quality of an information model for network management is defined from the object-oriented perspective, and an index for quality evaluation in a quality model for object classes is disclosed. But has the following disadvantages: (1) the objective evaluation capability is insufficient, the model evaluation is mainly oriented to the information model of the logic level, certain disjunction is realized with the actual system, and only evaluation can be carried out from the theoretical design level; in addition, the model evaluation relates to subjective indexes, relevant experts are required to participate in the evaluation process, the evaluation scores are derived from subjective scoring of the experts, and the model evaluation does not have the objective evaluation capability of automatic analysis and automatic scoring in practical application. (2) Model evaluation is carried out based on high-level design of a model, the evaluation result cannot reflect the actual application effect of the model in an IT system, and the problem that the model with better design effect is not necessarily suitable exists. (3) And the method is lack of quantitative scoring rules of evaluation indexes, so that the quality of the model is difficult to visually reflect, and the operability is low in practical application.

In summary, how to objectively evaluate the data model, reflect the actual application effect of the data model, and avoid participation of subjective evaluation is one of the technical problems to be solved urgently.

Disclosure of Invention

The embodiment of the invention provides an evaluation method, device and equipment of a data model, which are used for avoiding participation of subjective evaluation and objectively evaluating the data model by combining with an actual application scene.

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

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;

and 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.

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

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;

and 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 the static weight factor of each database table and the dynamic operation index and the dynamic weight 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 in the memory and executable on the processor; the processor implements the method of evaluating a data model according to any one of the aspects of the present invention when executing the program.

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

The invention has the beneficial effects that:

according to the method, the device and the equipment for evaluating the data model, provided by the embodiment of the invention, the incidence relation among all database tables contained in the data model to be evaluated is determined based on the 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; 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; and 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. By adopting the method provided by the invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, so that each data model to be evaluated can be objectively evaluated, the participation of subjective evaluation is greatly reduced, and the design rationality and the practicability of the data model are ensured.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:

fig. 1 is a schematic flow chart of a method for evaluating a data model according to an embodiment of the present invention;

fig. 2a is a schematic flow chart illustrating a process of determining the design reasonableness of the database table included in the to-be-evaluated data model according to an embodiment of the present invention;

FIG. 2b is a schematic flowchart of determining redundancy of the database table according to an embodiment of the present invention;

fig. 2c is a schematic flow chart of quantizing and converting the value of any evaluation index into a score according to a first embodiment of the present invention;

FIG. 2d is a schematic flowchart of determining the complexity of the database table according to an embodiment of the present invention;

fig. 2e is a schematic flow chart of determining a static weighting factor of any database table according to a first embodiment of the present invention;

fig. 3a is a schematic flowchart of determining the dynamic operation quality of the database table included in the data model to be evaluated according to an embodiment of the present invention;

FIG. 3b is a flowchart illustrating a process of determining the frequency of the database table according to an embodiment of the present invention;

fig. 3c is a schematic flow chart of determining the time delay of the database table according to an embodiment of the present invention;

FIG. 3d is a schematic flowchart of determining the stability of the database table according to an embodiment of the present invention;

fig. 3e is a schematic flow chart of determining a dynamic weighting factor of any database table according to a first embodiment of the present invention;

fig. 4 is a schematic flow chart for determining a comprehensive evaluation result of the data model to be evaluated according to the first embodiment of the present invention;

fig. 5 is a schematic structural diagram of an evaluation apparatus of a data model according to a second embodiment of the present invention.

Detailed Description

The evaluation method and device for the data model and the electronic equipment provided by the embodiment of the invention are used for avoiding participation of subjective evaluation and can objectively evaluate the data model by combining with an actual application scene.

The preferred embodiments of the present invention will be described below with reference to the accompanying drawings of the specification, it being understood that the preferred embodiments described herein are merely for illustrating and explaining the present invention, and are not intended to limit the present invention, and that the embodiments and features of the embodiments in the present invention may be combined with each other without conflict.

Example one

As shown in fig. 1, a schematic flow chart of an evaluation method of a data model according to an embodiment of the present invention may include the following steps:

s11, determining the association relation among the database tables contained in the data model to be evaluated based on the metadata structure of the data model to be evaluated.

In particular, the data model generally includes a database table, an association relationship between tables, and a table field. The data model describes content consisting of three parts, namely a data structure, a data operation and a data constraint. The data structure is used for describing static characteristics of the system, including types, contents, properties, relations among data and the like; the data operation is used for describing dynamic characteristics of the system, including insertion, modification, deletion, query and the like of data; the data constraint is actually a set of integrity rules. Integrity rules refer to the constraints and storage rules that data and its links in a given data model have to define the database and its state changes that conform to the data model to ensure the correctness, validity and compatibility of the data.

Based on the above, when the data model to be evaluated is evaluated, the association relationship between the database tables in the data model to be evaluated can be determined based on the metadata structure of the data model to be evaluated, the association and the constraint between the data.

And S12, 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.

In specific implementation, after the incidence relation among the database tables is determined, the theoretical design index of each database table can be determined on the basis because the data structure in the data model describes the static characteristics of the system.

The theoretical design index related to the embodiment of the invention is a static index of the data model to be evaluated, and can be used for describing the reasonability, redundancy, complexity and the like of the data model to be evaluated in a design level.

Preferably, the theoretical design index includes a design reasonableness of a database table included in the data model to be evaluated; and for any database table, determining the design reasonableness of the database table contained in the data model to be evaluated according to the method shown in fig. 2 a:

and S21, determining the redundancy and complexity of the database table.

In a specific implementation, the redundancy of the database table may be determined according to the method shown in fig. 2b, which includes the following steps:

and S31, determining 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, etc.

Specifically, when the redundancy evaluation index is the redundancy field ratio and the redundancy relevance ratio, the steps S321 to S325 may be performed for the two evaluation indexes to obtain the score converted from the redundancy field ratio and the score converted from the redundancy relevance ratio, respectively.

S32, the redundancy evaluation index values are quantized and converted into score values.

In a specific implementation, the step of quantizing the value of any evaluation index and converting the value into a score may include the steps shown in fig. 2 c:

s321, dividing at least one numerical range according to the value of the evaluation index in each database table.

The redundancy field ratio is taken as an example for explanation, because each database table in the data model to be evaluated can calculate to obtain the redundancy field ratio, if m database tables exist, m redundancy field ratios can be obtained, based on the m redundancy field ratios, N value ranges can be divided according to a clustering algorithm, namely N clusters are corresponding, and no intersection point exists between the N value ranges.

After the N value ranges are determined, the following process is performed for any database table:

s322, aiming at the value of the evaluation index in any database table, determining the numerical range of the value of the evaluation index.

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

S323, the absolute value of the difference between the central value in the numerical range and the value of the evaluation index is determined.

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

And S324, determining the score corresponding to the central value in the numerical range according to the score range corresponding to the numerical range.

In specific implementation, after the N value ranges are determined, an efficacy coefficient method is adopted, and the N value ranges are correspondingly obtained based on the N value ranges. For example, the maximum value in the score range may be 100. Specifically, 100 points can be divided into N points equally, and the value space of each point is the same, for example, when N is 5, 100 points can be divided into 5 point ranges of 1 to 20, 21 to 40, 41 to 60,61 to 80, and 81 to 100. These five score ranges correspond to five numerical ranges, respectively. In addition, if there is a central value in each of the numerical ranges, the central value in any of the numerical ranges corresponds to a score in the score range corresponding thereto.

Therefore, after determining the numerical range corresponding to the redundant field ratio of the database table, the score range corresponding to the numerical range can be determined. And further determining that the central value in the numerical range corresponds to a score in the corresponding score range.

And S325, 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.

In specific implementation, any evaluation index has a certain attribute, and the attribute is used for measuring whether the evaluation index is a forward index or a reverse index. The larger the value of the so-called forward indicator, the better the design that characterizes the database table. The larger the value of the so-called inverse index, the worse the database table design.

Therefore, in order to objectively obtain the evaluation result of the data model to be evaluated, when the evaluation index is converted, the score of the evaluation index needs to be obtained by using a corresponding formula according to the attribute of the evaluation index.

When the attribute of the evaluation index is positive, determining the score converted by the evaluation index according to the formula (1):

when the attribute of the evaluation index is reverse, determining the score converted by the evaluation index according to the formula (2):

in the formula (1) and the formula (2), QL3 represents a score converted from any evaluation index in any database table; the MID represents the corresponding score of the central point of the numerical range in which any evaluation index in any database table is located in the score range corresponding to the numerical range; DIS represents the absolute value of the difference between the central value of the numerical range in which the value of any evaluation index in any database table is located and the value of the evaluation index; DIS_maxThe maximum value of the numerical range in which the value of any evaluation index in any database table is located is represented; n represents the number of numerical ranges divided for any evaluation index based on the value of that evaluation index in the respective database tables.

In 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 reverse. Then the redundant fields in the database table need to be determined according to equation (2) when determining the scaled scores. After the value obtained by converting the redundant field in the database table is determined, the value obtained by converting the redundant field in each database table can be determined based on the same method.

When determining the score converted from the redundant association ratio in each database table, the score converted from the redundant association ratio in each database table may be determined in accordance with the steps of steps S321 to S325. It should be noted that, since the attribute of the redundant relevance ratio is in the reverse direction, when step S325 is executed, the score converted from the redundant relevance ratio in each database table needs to be determined according to formula (2) in the step.

And S33, carrying out weighted summation processing on the scores of the redundancy evaluation indexes to determine the redundancy of the database table.

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

in equation (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_jThe value of the value converted by the jth redundancy evaluation index is represented; w_jAnd determining the weight value corresponding to the jth redundancy evaluation index according to the actual situation.

In specific implementation, because the redundancy evaluation index includes a redundancy field ratio and a redundancy association ratio, when determining the redundancy of the database table by using the formula (3), M in the formula (3) is 2, and the redundancy of the database table is: QL2 ═ W₁*QL3₁+W₂*QL3₂。

In addition, when the redundancy evaluation index of each database table includes a redundancy field ratio and a redundancy association ratio, the redundancy of each database table can be expressed as: QL2_i＝W_i1*QL3_i1+W_i2*QL3_i2Where i represents the ith database table.

Preferably, the complexity of the database table can be determined according to the flow shown in fig. 2d, which includes the following steps:

and S41, determining the complexity evaluation index of the database table.

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

S42, the values of the complexity evaluation indexes are quantized and converted into scores.

In 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 the redundant field ratio conversion, and is not repeated here. It should be noted that, in the embodiment of the present invention, the attributes of the upper layer dependent database table number and the associated dimensionality number are both forward, and when step S325 is executed, the score obtained by converting the upper layer dependent database table number and the score obtained by converting the associated dimensionality number may be respectively determined according to formula (1); if the attribute of the lower-layer depended database table number related to the embodiment of the present invention is the inverse, the score obtained by converting the lower-layer depended database table number can be determined according to the formula (2) when step S325 is executed.

And S43, performing weighted summation processing on the scores obtained by conversion of the complexity evaluation indexes, and determining the complexity of the database table.

In particular implementation, when determining the complexity of the database table, the complexity can be determined according to formula (3). It should be noted that, when the complexity of the database table is determined by using formula (3), the meaning of each parameter in formula (3) is different from the physical meaning of each parameter in formula (3) when the redundancy of the database table is determined. For example, in determining the complexity, QL2 represents the complexity of the database table; m represents the number of complexity evaluation indexes; j represents the jth complexity evaluation index; QL3_jThe j-th complexity evaluation index is converted to obtain a score; w_jAnd representing a weight corresponding to the jth complexity evaluation index, wherein 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 may be the same or different, and are specifically determined according to the actual situation.

Specifically, when the complexity evaluation index of the database table in the embodiment of the present invention includes an upper-level depended database table number, a lower-level depended database table number, and an association dimension number, when the complexity of the database table is determined by using formula (3), M in formula (3) is 3, and the complexity of the database table is expressed as: QL2 ═ W₁*QL3₁+W₂*QL3₂+W₃*QL3₃。

On this basis, when the complexity evaluation index of each database table includes the number of upper-layer depended database tables, the number of lower-layer depended database tables, and the number of associated dimensions, then when determining the complexity of each database table, the complexity of each database table can be expressed as: QL2_i＝W_i1*QL3_i1+W_i2*QL3_i2+W_i3*QL3_i3Where i represents the ith database table.

And S22, 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.

In specific implementation, when the redundancy and the complexity of the database table are determined, the design reasonableness of the database table contained in the data model to be evaluated can be determined according to a formula (4):

in the formula (4), QL1 represents the design reasonableness of the database table included in the data model to be evaluated; QL2_kRepresenting the redundancy or complexity of the database table; k is 2; w is a_kA weight corresponding to the redundancy of the database table or a weight corresponding to the complexity of the database.

Specifically, the design reasonableness of the database table included in the data model to be evaluated may be represented as: QL1 ═ w₁*QL2₁+w₂*QL2₂。

On this basis, the design reasonableness of each database table included in the data model to be evaluated can be expressed as: QL1_i＝w_i1*QL2_i1+w_i2*QL2_i2Where i represents the ith database table.

When the design reasonableness of each database table included in the to-be-evaluated data model is determined, a static weighting factor of each database table needs to be determined, and when the method is specifically implemented, the method can be implemented according to the flow shown in fig. 2e for any database table, and includes the following steps:

and S51, determining the depth values of all the aggregation paths of the database table.

In particular, the aggregated path of the database table may be obtained based on a data model metadata structure.

Specifically, the tables in the database have the most original tables, and there are tables newly generated by aggregation based on the most original tables, so that there is a concept of an aggregation path. The convergence path refers to a path of a certain database table in the data model, which is generated from the most original table through first-level convergence; the depth of the convergence path is also the number of convergence. For example, a- > B- > C- > D- > E, it is assumed that the database table a is the most original database table, the database B is an hour database table obtained by aggregation for a certain hour based on the most original database table, and the database table C is based on a day database table obtained by aggregation for the day of the database table B; the database table D is a monthly database table or the like obtained by converging in the month based on the database table C, the depth value of the converging path of the database table D is 3, and similarly, the depth value of the database table B is 1.

And S52, determining the average value of the depth values of all the convergent paths as the static weight factor of the database table.

In specific implementation, after the depth values of all the converging paths of the database table are determined, the static weight factor of the database table may be determined according to formula (5):

in formula (5), FS represents a static weighting factor for the database table; l represents the number of the convergence paths contained in the database table; depth_lThe depth value of the ith aggregation path in the database table is represented.

The static weighting factor of the database table can be obtained based on the formula (5), and the static weighting factor FS of each database table can be obtained in the same way_iWhere i represents the ith database table.

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

In specific implementation, in order to ensure the objectivity of the data model to be evaluated, the embodiment of the invention determines the dynamic operation index of each database table based on the accessed condition of each database table in the data model to be evaluated, so that the data model to be evaluated is evaluated by combining the dynamic factors of the data model to be evaluated, and the data model to be evaluated has higher practicability.

Preferably, the dynamic operation index includes the dynamic operation quality of a database table included in the data model to be evaluated; and for any database table, determining the dynamic operation quality of the database table contained in the data model to be evaluated according to the method shown in fig. 3 a:

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

In specific implementation, the frequency of determining the database table according to the method shown in fig. 3b includes the following steps:

s71, determining the frequency evaluation index of the database table.

Preferably, the frequency evaluation index may include, but is not limited to: daily average access times of the database table, daily average access times and sub-average access field ratio of the upper database table and the like.

S72, the values of the frequency evaluation indexes are quantized and converted into score values.

In 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 the redundant field ratio conversion, and is not repeated here. It should be noted that, in the embodiment of the present invention, the attributes of the daily average access times of the database table, the daily average access times of the upper database table, and the sub-average access field ratio are all positive directions, and then, when step S325 is executed, the score obtained by converting the daily average access times of the database table, the score obtained by converting the daily average access times of the upper database table, and the score obtained by converting the sub-average access field ratio may be determined according to formula (1).

For convenience of description, the invention distinguishes the notation of the score obtained by converting the frequency evaluation index, the time delay evaluation index and the stability evaluation index which need to be determined under the dynamic operation quality from the notation of the score obtained by converting the redundancy evaluation index and the complexity evaluation index which need to be determined under the design reasonableness based on the database tables, and the score obtained by conversion is expressed by RL3 aiming at any database table, and the conversion is similar to the formulas (2) and (3), except that the QL2 in the formulas (2) and (3) is changed into RL3, and the other components are not changed. The corresponding determined frequency, delay and stability are used for RL2 representation.

S73, the scores converted by the frequency evaluation indexes are subjected to weighted summation processing to determine the frequency of the database table.

In specific implementation, when determining the frequency of the database table, the frequency may be determined according to formula (6):

in equation (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_jA score value obtained by converting the jth frequency evaluation index; q_jAnd determining the weight value corresponding to the jth frequency evaluation index 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 number of the database table, the daily average access number of the upper database table, and the secondary average access field ratio, when the frequency of the database table is determined by using formula (3), M in formula (3) is 3, and the frequency of the database table is represented as: RL2 ═ Q₁*RL3₁+Q₂*RL3₂+Q₃*RL3₃。

On the basis, the frequency evaluation indexes of each database table comprise daily average access times of the database table and an upper database tableWhen determining the frequency of each database table, the frequency of each database table may be represented as: RL2_i＝Q_i1*RL3_i1+Q_i2*RL3_i2+Q_i3*RL3_i3Where i represents the ith database table.

Specifically, the determining the time delay of the database table according to the flow shown in fig. 3c includes the following steps:

and S81, determining the time delay evaluation index of the database table.

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

And S82, quantizing the values of the delay evaluation indexes and converting the values into scores.

In 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 the redundant field ratio conversion, and is not repeated here. It should be noted that, in the embodiment of the present invention, the attributes of the sub-average access delay and the sub-average data generation delay are both inverse, and when step S325 is executed, the score obtained by converting the sub-average access delay and the score obtained by converting the sub-average data generation delay may be determined according to formula (2).

And S83, carrying out weighted summation processing on the scores obtained by conversion of the delay evaluation indexes, and determining the delay of the database table.

In particular, when determining the time delay of the database table, the time delay can be determined according to the formula (6). When the time delay of the database table is determined by using equation (6), the meaning of each parameter in equation (6) is different from the physical meaning of each parameter in equation (6) when the frequency of the database table is determined. For example, when determining latency, RL2 represents the latency of the database table; m represents the number of time delay evaluation indexes; j represents the jth time delay evaluation index; RL3_jThe value obtained by converting the jth time delay evaluation index is represented; q_jThe weight value corresponding to the jth time delay evaluation index is represented, and the weight value can be according to the actual situationAnd determining that the weight corresponding to the jth delay evaluation index may be the same as or different from the weight corresponding to the jth frequency evaluation index, the weight corresponding to the jth complexity evaluation index, and the weight corresponding to the jth redundancy evaluation index, which is specifically determined according to the practice.

Specifically, when the delay evaluation index of the database table in the embodiment of the present invention includes a sub-average access delay and a sub-average data generation delay, when the delay of the database table is determined by using formula (6), M in formula (6) is 2, and then the delay of the database table is expressed as: RL2 ═ Q₁*RL3₁+Q₂*RL3₂。

On this basis, when 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 database table can be expressed as: RL2_i＝Q_i1*RL3_i1+Q_i2*RL3_i2Where i represents the ith database table.

Preferably, the stability of the database table can also be determined according to the flow shown in fig. 3d, which includes the following steps:

and S91, determining the stability evaluation index of the database table.

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

And S92, carrying out quantification processing on the value of the stability evaluation index and converting the value into a score.

In 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 the redundant field ratio conversion, and is not repeated here. It should be noted that, if the attribute of the number of changes of the database table in the embodiment of the present invention is in the reverse direction, the score converted from the number of changes of the database table may be determined according to equation (2) when step S325 is executed.

And S93, performing weighted summation processing on the scores obtained by conversion of the stability evaluation indexes, and determining the stability of the database table.

If the stability evaluation index only includes the number of changes in the database table, step S93 specifically includes: and determining the score converted from the stability evaluation index as the stability of the database table.

When the stability evaluation index only contains the change times of the database table, the stability of the database table can be represented as: RL2 ═ RL3, where RL3 represents the score converted from the number of changes in the database table and RL2 represents the stability of the database table.

On this basis, when the stability evaluation index of each database table only contains the number of changes of the database table, the stability of each database table can be expressed as: RL2_i＝RL3_iWhere i represents the ith database table.

When the stability evaluation index comprises at least two evaluation indexes, the stability of the database table needs to be determined by using formula (3).

And S62, 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.

In specific implementation, when determining the dynamic operation quality of the database table included in the data model to be evaluated, the determination may be performed according to the formula (7):

in formula (7), RL1 represents the dynamic operation quality of the database table included in the data model to be evaluated; RL2_kRepresenting the frequency, time delay or stability of the database table; k is 3; q. q.s_kThe weight value corresponding to the frequency of the database table, the weight value corresponding to the time delay of the database or the weight value corresponding to the stability of the database table.

Specifically, the dynamic operation quality of the database table included in the data model to be evaluated may be represented as: RL1 ═ q₁*RL2₁+q₂*RL2₂+q₃*RL2₃。

On this basis, the dynamic operation quality of each database table included in the data model to be evaluated can be represented as follows: RL1_i＝q_i1*RL2_i1+q_i2*RL2_i2+q_i3*RL2_i3Where i represents the ith database table.

When determining the dynamic operation quality of each database table included in the to-be-evaluated data model, it is further necessary to determine a dynamic weight factor of each database table, and when specifically implementing, for any database table, the implementation may be performed according to the flow shown in fig. 3e, including the following steps:

and S101, determining the daily data increment of the database table and the sum of the daily data increments of all the database tables.

And S102, 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.

In specific implementation, after determining the sum of the daily increment data volume of the database table and the daily increment data volume of the database table, the dynamic weighting factor of the database table may be determined according to formula (8):

in formula (8), FR_iRepresenting the dynamic weight factor of the ith database table; n represents the number of database tables contained in the data model to be evaluated; size_iRepresenting the amount of daily growth data for the ith database table.

In specific implementation, the size of the storage space occupied by each database table can be determined, so that the daily data increment of each database table can be determined.

And S14, determining the 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.

In a specific implementation, when step S14 is executed, the process shown in fig. 4 may include the following steps:

s141, 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.

In specific implementation, a first product corresponding to the ith database table may be determined according to formula (9):

TQS_i＝FS_i*QL1_i (9)

TQS in the formula (9)_iA first product representing an ith database table; FS (file system)_iStatic weighting factors representing the ith database table; QL1_iAnd representing the design reasonableness of the ith database table contained in the data model to be evaluated.

Similarly, a second product corresponding to the ith database table may be determined according to equation (10):

TQR_i＝FR_i*RL1_i (10)

in equation (10), TQR_iA second product representing an ith database table; FR_iRepresenting the dynamic weight factor of the ith database table; RL1_iAnd representing the dynamic operation quality of the ith database table contained in the data model to be evaluated.

And S142, determining the evaluation result of the database table according to the first product and the second product.

In specific implementation, the evaluation result of the ith database table may be determined according to formula (11):

TQ_i＝TQS_i*TQR_i (11)

in formula (11), TQ_iShowing the evaluation result of the ith database table.

And S143, determining a comprehensive evaluation result of the data model to be evaluated based on the evaluation results of all the database tables.

After determining the evaluation results of the database tables included in the data model to be evaluated, determining a comprehensive evaluation result of the data model to be evaluated according to a formula (12):

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

The comprehensive evaluation result of the data model to be evaluated can be determined, and specifically, the larger the value of MQ is, the better the overall design and operation quality of the data model to be evaluated is; and comparing the data model to be evaluated in the transverse direction and the longitudinal direction, and clearly judging the quality and the degradation trend of the data model to be evaluated. In addition, on the basis of obtaining the comprehensive evaluation result of the data model to be evaluated, the indexes of each database table contained in the data model to be evaluated can be determined according to the TQ value of each database table, the ratio of TQs/TQR, the scores of QL1, QL2, QL3, RL1, RL2 and RL3, and then the data model to be evaluated can be subjected to targeted optimization. For example, for database tables with lower TQ values in the data model to be evaluated and indexes with lower values in the database tables, the database tables and the indexes in the database tables can be subjected to targeted problem analysis, and the data model to be evaluated is optimized on the basis, so that the finally obtained data model has higher use value.

Furthermore, the evaluation indexes in the embodiment of the invention can be statistically obtained from the system, the numerical value is clear, the standard is unified, and manual judgment is not needed. That is to say, the evaluation index in the embodiment of the invention is objectively measurable, and subjective judgment is reduced.

Furthermore, when the data model to be evaluated is comprehensively evaluated, in the embodiment of the present invention, 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 rationality of the static design of the data model to be evaluated is considered, but also the dynamic operation index for measuring the dynamic operation effect of the data model is introduced, and the quality of the data model in the actual application is taken as an important judgment basis, so that the process of comprehensively evaluating the data model to be evaluated is hooked with the visual angle operation condition of the data model to be evaluated, and the comprehensive evaluation method is more suitable for the actual application.

The evaluation method of the data model provided by the embodiment of the invention determines the incidence relation among database tables contained in the data model to be evaluated based on the 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; 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; and 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. By adopting the method provided by the invention, the theoretical design index and the dynamic operation index are combined to evaluate the data model to be evaluated, so that each data model to be evaluated can be objectively evaluated, the participation of subjective evaluation is greatly reduced, and the design rationality and the practicability of the data model are ensured.

Example two

Based on the same inventive concept, the embodiment of the invention also provides an evaluation device of the data model, and as the principle of solving the problems of the device is similar to the evaluation method of the data model, the implementation of the device can refer to the implementation of the method, and repeated parts are not described again.

As shown in fig. 5, a schematic structural diagram of an evaluation apparatus for a data model according to a second embodiment of the present invention includes a first determining unit 51, a second determining unit 52, a third determining unit 53, and a fourth determining unit 54, where:

a first determining unit 51, configured to determine, based on a metadata structure of a data model to be evaluated, an association relationship between database tables included in the data model to be evaluated;

the second determining unit 52 is configured to determine theoretical design indexes of the database tables and static weight factors of the database tables according to the association relationships between the database tables;

a third determining unit 53, configured to determine, according to the monitored access records of each database table, a dynamic operation index of each database table, and a dynamic weight factor of each database table respectively;

and a fourth determining unit 54, configured to determine 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.

Preferably, the second determining unit 52 is specifically configured to execute the following process for any database table: determining the depth values of all the gathering paths of the database table; and determining the average value of the depth values of all the convergent paths as a static weight factor of the database table.

Preferably, the third determining unit 53 is specifically configured to execute the following process 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.

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

the second determining unit 52 is specifically configured to determine, for any database table, 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.

Preferably, the second determining unit 52 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.

Preferably, the redundancy evaluation index at least includes one of the following items: 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.

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

the third determining unit 53 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.

Preferably, the third determining unit 53 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.

Preferably, 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.

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 aiming at the value of the evaluation index in any database table, determining the numerical range of the value of the evaluation index; and determining the absolute value of the difference between the central value within the range of values and the 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.

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 aiming at the value of the evaluation index in any database table, determining the numerical range of the value of the evaluation index; and determining the absolute value of the difference between the central value within the range of values and the 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.

Preferably, the fourth determining unit 54 is specifically configured to determine, for any database table, a first product of the design rationality of the database table and a static weighting factor, and determine a second product of the dynamic operation quality of the database table and a dynamic weighting 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.

For convenience of description, the above parts are separately described as modules (or units) according to functional division. Of course, the functionality of the various modules (or units) may be implemented in the same or in multiple pieces of software or hardware in practicing the invention.

EXAMPLE III

The third embodiment of the invention provides communication equipment, which comprises a memory, a processor and a computer program, wherein the computer program is stored on the memory and can run on the processor; the processor performs the steps as follows:

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;

Example four

A fourth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the method for evaluating a data model according to any one of the first embodiment of the present invention.

The evaluation device of the data model provided by the embodiment of the application can be realized by a computer program. It should be understood by those skilled in the art that the above-mentioned module division is only one of many module division, and if the module division is divided into other modules or not, it should be within the scope of the present application as long as the evaluation device of the data model has the above-mentioned functions.

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, and the like) 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 flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such 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 such modifications and variations.

Claims

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

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 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.