Data table grouping methods and apparatus, readable storage media, electronic devices

Abstract

This disclosure belongs to the field of computer technology and relates to a data table grouping method and apparatus, a readable storage medium, and an electronic device. The method includes: acquiring multiple business data tables to be grouped corresponding to a target business database, and acquiring target data fields and corresponding target field types from the multiple business data tables to be grouped; grouping the target data fields according to a grouping dimension to obtain field grouping results, and generating clustering input data based on the field grouping results; wherein the grouping dimension is determined by the target data fields and target field types; performing clustering processing on the clustering input data to obtain clustering results, and determining the grouping results of the multiple business data tables to be grouped based on the clustering results. In this disclosure, the clustering input data is generated based on the field grouping results, thereby realizing the grouping of the business data tables to be grouped, avoiding reliance on manual intervention in the grouping process, and improving the efficiency and accuracy of obtaining grouping results.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a data table grouping method and device, a computer-readable storage medium, and an electronic device. Background Technology

[0002] In recent years, with the development of computer technology, a large amount of data is generated in the process of implementing a business. Based on this, grouping the business data tables in the business database will facilitate the understanding of the business by the personnel maintaining the business, thereby improving the business processing efficiency.

[0003] In related technologies, the process of grouping business data tables in a business database is usually done by personnel who are very familiar with the business. This increases the reliance on manual labor, makes it impossible to generate grouping results for business data tables in real time, and reduces the efficiency of business data table grouping. At the same time, some unavoidable errors will occur in the manual process, which reduces the accuracy of business data table grouping.

[0004] Therefore, there is an urgent need in the field to develop a new method and apparatus for grouping data tables.

[0005] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this disclosure is to provide a data table grouping method, a data table grouping device, a computer-readable storage medium, and an electronic device, thereby overcoming, at least to some extent, the problem of low efficiency in obtaining data table grouping results caused by related technologies.

[0007] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0008] According to a first aspect of the present invention, a data table grouping method is provided. The method includes: acquiring a plurality of business data tables to be grouped corresponding to a target business database, and acquiring target data fields and target field types corresponding to the target data fields in the plurality of business data tables to be grouped; grouping the target data fields according to a grouping dimension to obtain field grouping results, and generating clustering input data based on the field grouping results; wherein the grouping dimension is determined by the target data fields and the target field types; performing clustering processing on the clustering input data to obtain clustering results, so as to determine the grouping results of the plurality of business data tables to be grouped based on the clustering results.

[0009] In an exemplary embodiment of the present invention, the target data field includes non-relational fields; the step of grouping the target data field according to the grouping dimension to obtain field grouping results includes: filtering the non-relational fields present in the target data field to obtain relational fields, and grouping the relational fields according to the grouping dimension to obtain relational field grouping results; performing statistics on the relational field grouping results in the business data table to be grouped to obtain grouping statistics results, and determining the field grouping results corresponding to the business data table to be grouped based on the grouping statistics results.

[0010] In an exemplary embodiment of the present invention, generating clustering input data based on the grouping results of the field includes: calculating the number of grouping results of the same relation field in multiple business data tables to be grouped to obtain a count calculation result; sorting the count calculation result in descending order to obtain a count sorting result; determining a preset number of high-count grouping results in the relation field grouping results based on the count sorting result; and generating clustering input data based on the high-count grouping results.

[0011] In an exemplary embodiment of the present invention, generating clustering input data based on the high number of grouping results includes: generating multiple field row vectors corresponding to multiple business data tables to be grouped based on the high number of grouping results, and generating a clustering input matrix based on the multiple field row vectors.

[0012] In an exemplary embodiment of the present invention, generating multiple field row vectors corresponding to multiple business data tables to be grouped based on the high-number grouping results includes: generating an initial field row vector corresponding to the high-number grouping results, and sequentially judging the relational field grouping results and the high-number grouping results in the business data tables to be grouped; wherein, the elements in the initial row vector correspond one-to-one with the high-number grouping results; if there is a relational field grouping that is consistent with the high-number grouping results, in the initial field row vector, a target element corresponding to the high-number grouping results is determined, and the value of the target element is replaced with a preset element value to generate a field row vector corresponding to the business data tables to be grouped.

[0013] In an exemplary embodiment of the present invention, generating a clustering input matrix based on the plurality of field row vectors includes: generating an initial clustering input matrix based on the plurality of field row vectors, and performing dimensionality reduction processing on the initial clustering input matrix to obtain the clustering input matrix.

[0014] In an exemplary embodiment of the present invention, the method further includes: determining, based on the grouping result, the business data tables to be grouped belonging to the same group; if there are multiple business data tables to be grouped belonging to the same group, determining the field creation time of the target data field in the multiple business data tables to be grouped; if there are multiple field creation times corresponding to the business data tables, sorting the multiple field creation times in ascending order to obtain a lower field creation time, and determining the lower field creation time as the business creation time of the business data tables to be grouped; sorting the business creation times of the business data tables to be grouped in the same group in ascending order to obtain an ascending order sorting result, and determining the data flow relationship between the business data tables to be grouped based on the ascending order sorting result.

[0015] According to a second aspect of the present invention, a data table grouping apparatus is provided. The apparatus includes: an acquisition module configured to acquire a plurality of business data tables to be grouped corresponding to a target business database, and to acquire target data fields and target field types corresponding to the target data fields in the plurality of business data tables to be grouped; a dimension grouping module configured to group the target data fields according to a grouping dimension to obtain a field grouping result, and to generate clustering input data based on the field grouping result; wherein the grouping dimension is determined by the target data fields and the target field types; and a data table grouping module configured to perform clustering processing on the clustering input data to obtain a clustering result, so as to determine the grouping result of the plurality of business data tables to be grouped based on the clustering result.

[0016] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores computer-readable instructions, which, when executed by the processor, implement the data table grouping method of any of the above exemplary embodiments.

[0017] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the data table grouping method in any of the above exemplary embodiments.

[0018] As can be seen from the above technical solutions, the data table grouping method, data table grouping device, computer storage medium, and electronic device in the exemplary embodiments of the present invention have at least the following advantages and positive effects:

[0019] In the methods and apparatus provided by the exemplary embodiments of this disclosure, on the one hand, the clustering input data is generated based on the field grouping results, and the field grouping results are determined based on the target data field and the target field type, thereby providing an automatic method for obtaining grouping results without relying on manual methods, avoiding dependence on manual methods and improving the efficiency of obtaining grouping results; on the other hand, the grouping results are obtained based on the clustering results, which improves the accuracy of the grouping results and avoids grouping errors caused by manual methods.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0022] Figure 1 The schematic diagram illustrates a flowchart of the data table grouping method in an embodiment of this disclosure;

[0023] Figure 2 The diagram illustrates the interface for importing a business data table to be grouped in an embodiment of this disclosure.

[0024] Figure 3 This illustration schematically shows the field grouping results in an embodiment of the present disclosure;

[0025] Figure 4 This illustration schematically shows clustering input data in an embodiment of the present disclosure;

[0026] Figure 5 This illustration shows a flowchart of the process for obtaining field grouping results in an embodiment of this disclosure.

[0027] Figure 6 This illustration schematically shows a process diagram for generating clustering input data in an embodiment of the present disclosure;

[0028] Figure 7 This schematic diagram illustrates the process of generating multiple segment row vectors in an embodiment of this disclosure;

[0029] Figure 8 The clustering input matrix in an embodiment of this disclosure is illustrated schematically;

[0030] Figure 9 This illustration shows a flowchart of determining data flow relationships in an embodiment of the present disclosure;

[0031] Figure 10 This illustration shows a flowchart of data table grouping in an application scenario.

[0032] Figure 11 This diagram illustrates the data flow relationship in an embodiment of the present disclosure.

[0033] Figure 12 This schematic diagram illustrates the structure of a data table grouping device according to an embodiment of the present disclosure;

[0034] Figure 13 An electronic device for a data table grouping method is illustrated schematically in an embodiment of this disclosure;

[0035] Figure 14 The illustration schematically shows a computer-readable storage medium for a data table grouping method according to an embodiment of the present disclosure. Detailed Implementation

[0036] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0037] The terms “a,” “an,” “the,” and “the” are used in this specification to indicate the presence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended inclusion and to mean that there may be other elements / components / etc. in addition to the listed elements / components / etc.; the terms “first” and “second” are used only as markings and are not a limitation on the number of objects.

[0038] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities.

[0039] In view of the problems existing in the related technologies, this disclosure proposes a data table grouping method. Figure 1 The flowchart illustrating the data table grouping method is shown below. Figure 1 As shown, the data table grouping method includes at least the following steps:

[0040] Step S110. Obtain multiple business data tables to be grouped corresponding to the target business database, and obtain the target data fields and target field types corresponding to the target data fields in the multiple business data tables to be grouped.

[0041] Step S120. Group the target data fields according to the grouping dimension to obtain the field grouping results, and generate clustering input data based on the field grouping results; wherein, the grouping dimension is determined by the target data fields and the target field type.

[0042] Step S130. Perform clustering processing on the clustering input data to obtain clustering results, so as to determine the grouping results of multiple business data tables to be grouped based on the clustering results.

[0043] In the methods and apparatus provided by the exemplary embodiments of this disclosure, on the one hand, the clustering input data is generated based on the field grouping results, and the field grouping results are determined based on the target data field and the target field type, thereby providing an automatic method for obtaining grouping results without relying on manual methods, avoiding dependence on manual methods and improving the efficiency of obtaining grouping results; on the other hand, the grouping results are obtained based on the clustering results, which improves the accuracy of the grouping results and avoids grouping errors caused by manual methods.

[0044] The following section provides a detailed explanation of each step in the data table grouping method.

[0045] In step S110, multiple business data tables to be grouped corresponding to the target business database are obtained, and the target data fields and target field types corresponding to the target data fields are obtained from the multiple business data tables to be grouped.

[0046] In an exemplary embodiment of this disclosure, business data is typically stored in a designated database in the form of business data tables. Based on this, the target business database refers to a database that stores multiple business data tables, and the business data tables to be grouped refer to the data tables stored in the target business database.

[0047] Each business data table to be grouped contains target data fields, such as the primary key target data field and the database account target data field. Furthermore, there is a target field type corresponding to each target data field. For example, the target field type of the primary key target data field is integer, and the target field type of the database account target data field is variable-length string.

[0048] For example, Figure 2 The interface for importing business data tables to be grouped is shown, such as... Figure 2 As shown, interface 200 is the interface for importing business data tables to be grouped, and control 201 is the control for importing business data tables to be grouped. When the user touches control 201, an information table 202 of the business data tables to be grouped will be displayed in interface 200. The information table records the information of the business data tables to be grouped, such as the target business database to which the business data tables to be grouped belong and the time information of importing the business data tables to be grouped.

[0049] Control 203 is a control for determining the grouping result of the business data table to be grouped. When control 203 is touched, the grouping result corresponding to the imported business data table to be grouped will be automatically determined and the grouping result will be displayed in interface 200.

[0050] For example, when the user touches control 201, the business data table A to be grouped and the business data table B to be grouped are imported. At this time, the target data fields include data field A1 corresponding to business data table A to be grouped and data field B1 corresponding to business data table B to be grouped. Similarly, the target field type includes the field type of data field A1 and the data type of target data field B1.

[0051] In this exemplary embodiment, automatically obtaining the target data fields and target field types of the business data tables to be grouped helps to determine whether there is a relationship between the business data tables to be grouped based on the target data fields and target field types, and thus determine the grouping result of the business data tables to be grouped. This provides a basis for determining the grouping result of the business data tables to be grouped without relying on manual methods, thereby improving the efficiency of determining the grouping result of the business data tables to be grouped, avoiding human errors, and improving the accuracy of determining the grouping result of the business data tables to be grouped.

[0052] In step S120, the target data fields are grouped according to the grouping dimension to obtain the field grouping results, and clustering input data is generated based on the field grouping results; wherein, the grouping dimension is determined by the target data fields and the target field type.

[0053] In an exemplary embodiment of this disclosure, the grouping dimension is determined by the target data field and the target field type. That is, the determination of the field grouping result must take into account the target data field and the target field type. For example, there is a target data field A1 in the business data table A to be grouped, and the target field type of A1 is integer. There is also a target data field A1 in the business data table B to be grouped, but the target field type of A1 is character. In this case, the target data field A1 in the business data table A to be grouped and the target data field A1 in the business data table B to be grouped are not target data fields with the same grouping dimension.

[0054] Based on this, the field grouping result refers to the result of grouping the target data fields in all business data tables to be grouped according to the grouping dimension, and the clustering input data can be the data input into the clustering algorithm.

[0055] For example, by grouping target data fields from multiple business data tables according to the grouping dimension, we can obtain, as follows: Figure 3 The field grouping results shown are as follows. The array `Array` stores the grouping results. The symbols `=>` store the key and value on either side, respectively. The key represents the grouping dimension, which could be a target data field named `record_no` of type 64-bit variable-length string. The value 29 indicates the number of times this grouping dimension appears in multiple business data tables to be grouped, and so on. Figure 3 It shows the number of times each grouping dimension appears in multiple business data tables to be grouped.

[0056] based on Figure 3 The grouping results of the fields can yield results such as Figure 4 The clustering input data shown is as follows, where Figure 4 The clustering input data shown is a matrix, in which each row vector corresponds to a business data table to be grouped, and each column corresponds to a grouping dimension.

[0057] Based on this Figure 4 The element with a value of 1 in the first row and first column indicates that the business data table to be grouped, corresponding to the first row vector, contains a target data field and its type corresponding to grouping dimension 1. This pattern continues until we can deduce... Figure 4 The meaning of other elements that are 1.

[0058] The element with a value of 0 in the first row and second column indicates that the target data field and its type corresponding to grouping dimension 2 do not exist in the business data table to be grouped corresponding to the first row vector. This can be deduced by analogy. Figure 4 The meaning of other elements that are 0.

[0059] In an optional embodiment, Figure 5 The diagram illustrates the process of obtaining field grouping results in a data table grouping method, as follows: Figure 5 As shown, the target data field includes non-relational fields, and the method includes at least the following steps: In step S510, the non-relational fields existing in the target data field are filtered to obtain relational fields, and the relational fields are grouped according to the grouping dimension to obtain the relational field grouping result.

[0060] Among them, non-relational fields refer to target data fields that cannot be used to determine the relationship between business data tables to be grouped. For example, it can be the primary key id that exists in every business data table to be grouped, or the data table creation time creat_time that exists in many business data tables to be grouped. This exemplary implementation does not make any special limitations on this.

[0061] It is worth noting that which target data fields are non-relational fields are usually determined from the pre-configured configuration information.

[0062] Based on this, a relational field refers to a target data field that can be used to determine the relationship between business data tables to be grouped. The relational field grouping result is the result obtained by grouping the relational fields according to the grouping dimension.

[0063] For example, given three business data tables to be grouped, filtering the non-relational fields in these tables will yield relational fields, such as relational field A, relational field B, relational field C, and relational field D. Grouping relational fields A, B, C, and D according to the grouping dimension will produce the corresponding field grouping results.

[0064] In step S520, the grouping results of the relational fields in the business data table to be grouped are statistically analyzed to obtain the grouping statistical results, and based on the grouping statistical results, the field grouping results corresponding to the business data table to be grouped are determined.

[0065] Among them, the grouping statistics result is to count how many different relation field grouping results exist in different business data tables to be grouped, and then obtain the field grouping result based on the grouping statistics result.

[0066] For example, there are four relation field grouping results in different business data tables to be grouped: relation field grouping result 1, relation field grouping result 2, relation field grouping result 3, and relation field grouping result 4. Then, it is determined whether there are target data fields in the different business data tables to be grouped that correspond to relation field grouping result 1, relation field grouping result 2, relation field grouping result 3, and relation field grouping result 4, in order to obtain the field grouping results.

[0067] In this exemplary embodiment, non-relational fields in the target data fields are filtered out, avoiding the adverse effects of non-relational fields on subsequent grouping results, thereby improving the accuracy of subsequent grouping results, making it easier for developers to understand the relationships between grouped data tables, improving the developer experience, and thus increasing the speed of subsequent business processing.

[0068] In an optional embodiment, Figure 6This diagram illustrates the process of generating clustering input data in the data table grouping method, as follows: Figure 6 As shown, the method includes at least the following steps: In step S610, the number of grouping results of the same relation field in multiple business data tables to be grouped is calculated to obtain the number calculation result, and the number calculation result is sorted in descending order to obtain the number sorting result.

[0069] In this context, there may be different relation field grouping results in each business data table to be grouped, and the count calculation result refers to the number of times the relation field grouping appears in different business data tables to be grouped. Based on this, the count sorting result is the result obtained by sorting the count calculation results from high to low.

[0070] For example, given relation field grouping results A, B, and C, we can count the number of times each of these three relation field grouping results appears in different business data tables to be grouped, to obtain the count result. Specifically, the count result could be that the number of times relation field grouping result A appears in different business data tables to be grouped is 5, the number of times relation field grouping result B appears in different business data tables to be grouped is 1, and the number of times relation field grouping result C appears in different business data tables to be grouped is 3.

[0071] Based on this, the resulting sorted counts are: relation field grouping result A, relation field grouping result C, and relation field grouping result B.

[0072] In step S620, based on the number sorting results, a preset high number grouping result is determined in the relation field grouping results, and clustering input data is generated based on the high number grouping results.

[0073] Here, the preset number refers to a value used to determine the grouping results with the higher number of items.

[0074] For example, the sorted results by number are relation field grouping result A, relation field grouping result B, relation field grouping result E, relation field grouping result F, relation field grouping result D, and relation field grouping result C.

[0075] The preset number is 5. Therefore, the high-number grouping results are relation field grouping result A, relation field grouping result B, relation field grouping result E, relation field grouping result F, and relation field grouping result D. Clustering input data is generated based on these 5 high-number grouping results.

[0076] In this exemplary embodiment, the clustering input data is generated based on the high number of grouping results. Since the high number of grouping results are obtained by sorting the results of the number calculation in descending order, the clustering input data generated from the high number of grouping results can better reflect the relationship between different business data tables to be grouped, thereby improving the accuracy of the subsequent grouping results.

[0077] In an optional embodiment, generating clustering input data based on the high number of grouping results includes: generating multiple field row vectors corresponding to multiple business data tables to be grouped based on the high number of grouping results, and generating a clustering input matrix based on the multiple field row vectors.

[0078] In this clustering matrix, each field row vector corresponds to a business data table to be grouped, and the elements in the field row vector can indicate whether there is a relational field grouping result in the business data table to be grouped that corresponds to the high number of grouping results. The matrix composed of the field row vectors corresponding to different business data tables to be grouped is the clustering input matrix. The clustering input matrix indicates whether there is a relational field grouping result in each business data table to be grouped that corresponds to the high number of grouping results. Using the clustering input matrix as the clustering input data, the grouping results of different business data tables to be grouped can be obtained more accurately.

[0079] For example, there are 5 high-number grouping results, namely relation field A, relation field B, relation field C, relation field D and relation field E. In addition, there are 3 business data tables to be grouped. Based on this, there are 3 field row vectors. Specifically, the field row vector corresponding to the first business data table to be grouped, that is, the first row of the clustering input matrix is ​​[0 01], the field row vector corresponding to the second business data table to be grouped, that is, the second row of the clustering input matrix is ​​[1 0 0], and the field row vector corresponding to the third business data table to be grouped, that is, the third row of the clustering input matrix is ​​[1 1 0].

[0080] In this exemplary embodiment, the clustering input data consists of multiple field row vectors. Each field row vector represents whether there is a grouping result in the business data table to be grouped that corresponds to the highest number of score results. Based on this, the clustering input matrix is ​​used as the clustering input data, so that the clustering input data can fully reflect the relationship between different business data tables to be grouped, thereby improving the accuracy of the subsequent grouping results.

[0081] In an optional embodiment, Figure 7 The diagram illustrates the process of generating multiple sub-segment row vectors in a data table grouping method, as shown below. Figure 7As shown, the method includes at least the following steps: In step S710, an initial field row vector corresponding to the high number of grouping results is generated, and the relation field grouping results and the high number of grouping results in the business data table to be grouped are judged in turn; wherein, the elements in the initial row vector correspond one-to-one with the high number of grouping results.

[0082] The initial field row vector refers to the initial value of the field row vector. Generally, the element values ​​in the initial field row vector are all 0. Furthermore, the elements in the initial field row vector correspond one-to-one with the high number of grouping results. That is, the element values ​​in the initial field row vector represent whether there is a relational field grouping result in the special data table to be grouped that corresponds to the high number of grouping results.

[0083] For example, there are 5 high-number relation grouping results, namely relation field grouping result A, relation field grouping result B, relation field grouping result C, relation field grouping result D, and relation field grouping result E. There are also 3 business data tables to be grouped. Based on this, the initial field row vector corresponding to the first, second, and third business data tables to be grouped is a row vector with 3 elements, all of which have a value of 0.

[0084] The first business data table to be grouped contains three relation field grouping results: relation field grouping result A, relation field grouping result B, and relation field grouping result C. The second business data table to be grouped contains two relation field grouping results: relation field grouping result B and relation field grouping result E. The third business data table to be grouped contains three relation field grouping results: relation field grouping result F, relation field grouping result H, relation field grouping result B, and relation field grouping result C. Based on this, the relation field grouping results of each business data table to be grouped are compared with the highest number of grouping results.

[0085] In step S720, if there is a relational field grouping that is consistent with the high number of grouping results, the target element corresponding to the high number of grouping results is determined in the initial field row vector, and the value of the target element is replaced with a preset element value to generate a field row vector corresponding to the business data table to be grouped.

[0086] Specifically, when there is a relational field grouping result that is consistent with the high number of grouping results, in the initial field row vector, the target element that has a corresponding relationship with the high number of grouping results is determined, and the target element is replaced with a preset element value from the initial value to generate the field row vector.

[0087] For example, there are 5 high-number grouping results: relation field grouping result A, relation field grouping result B, relation field grouping result C, relation field grouping result D, and relation field grouping result E. There are also three business data tables to be grouped. The first business data table to be grouped has 3 relation field grouping results: relation field grouping result A, relation field grouping result B, and relation field grouping result C. The second business data table to be grouped has 2 relation field grouping results: relation field grouping result B and relation field grouping result E. The third business data table to be grouped has 3 relation field grouping results: relation field grouping result F, relation field grouping result H, relation field grouping result B, and relation field grouping result C.

[0088] Based on this, in the first business data table to be grouped, there are three relational field grouping results that are consistent with the high number of grouping results. Therefore, the initial field row vector corresponding to the first business data table to be grouped is replaced from [0 0 0 0 0] to [1 1 1 0 0]. In the second business data table to be grouped, there are two relational field grouping results that are consistent with the high number of grouping results. Therefore, the initial field row vector corresponding to the second business data table to be grouped is replaced from [0 0 0 0 0] to [0 1 0 0 1]. In the third business data table to be grouped, there are two relational field grouping results that are consistent with the high number of grouping results. Therefore, the initial field row vector corresponding to the third business data table to be grouped is replaced from [0 0 0 00] to [0 1 1 0 0].

[0089] In this exemplary embodiment, it is determined whether there are a high number of grouping results in the grouping results of the relation field, so as to determine the field row vector and then determine the clustering input data. This allows the clustering input data to fully reflect the relationship between different business data tables to be grouped, thereby improving the accuracy of the subsequent grouping results.

[0090] In an optional embodiment, generating a clustering input matrix based on multiple field row vectors includes: generating an initial clustering input matrix based on multiple field row vectors, and performing dimensionality reduction processing on the initial clustering input matrix to obtain the clustering input matrix.

[0091] Dimensionality reduction refers to the process of using dimensionality reduction algorithms to filter out redundant dimensions in the initial clustering input matrix, thereby obtaining a clustering input matrix without redundant dimensions.

[0092] For example, in practice, there may be hundreds or thousands of business data tables to be grouped, and there may be dozens of high-number grouping results, such as 20 high-number grouping results. In this case, the initial clustering input matrix has hundreds or thousands of rows and 20 columns. Using such an initial clustering input matrix as the clustering input data would increase the computer's overhead. Therefore, it is necessary to perform dimensionality reduction processing on the initial clustering input matrix, which can yield results like... Figure 8 The clustering input matrix shown corresponds to a business data table to be grouped, with each row in the matrix representing a different data table.

[0093] In this exemplary embodiment, by performing dimensionality reduction on the initial clustering input matrix, the performance loss of the computer can be reduced, the speed of obtaining subsequent grouping results can be improved, and the grouping results can be displayed to developers more quickly, thus improving the developer's experience.

[0094] In step S130, clustering processing is performed on the clustering input data to obtain clustering results, so as to determine the grouping results of multiple business data tables to be grouped based on the clustering results.

[0095] In the exemplary embodiments of this disclosure, the clustering result refers to the result obtained after performing clustering processing. The clustering result can be used to know the grouping status of different business data tables to be grouped, that is, to know the grouping results of multiple business data tables to be grouped.

[0096] For example, the clustering results are: [[[0.32577665, -1.26569048]], [[-0.50640004, -0.23309057], [-0.44637509, -0.18763846], [-0.56776335, -0.20842107], [-0.50009628, -0.18470169], [-0.41565208, -0.08526049]], [[-0.01619255, [0.31758307], [0.0758956, 0.43795481], [0.13392493, 0.42965911], [-0.20119517, 0.28452057], [-0.06349963, 0.32300778], [-0.07315711, 0.27786787], [-0.1268783, 0.28080463], [-0.06179994, 0.3994676], [0 [0.05898525, 0.35933669], [-0.05684983, 0.29557246], [-1.13151414, -0.17908838], [-1.02899066, -0.2440337], [-0.95896219, -0.22926587], [-1.06148567, -0.16432055], [0.29121225, 0.51871942], [0.315 [40743, 0.56874978], [0.40704733, 0.58248365], [0.32982316, 0.65342315], [[0.69899463, -0.80018415], [0.73965191, -0.77817354]]], where the clustering results are stored in the form of an array, and this array consists of multiple array elements. The content stored in each array element is the two-dimensional data that is grouped into the same group, for example in Figure 9 In the array, the first array element stores the two-dimensional data assigned to the first group [0.32577665, -1.26569048], the second array element stores the two-dimensional data assigned to the second group [-0.5064004, -0.23309057], [-0.44637509, -0.18763846] and [-0.56776335, -0.20842107], and so on, thus determining the two-dimensional data in different groups.

[0097] It is worth noting that each two-dimensional data corresponds to a business data table to be grouped. Therefore, the grouping results can be obtained based on the clustering results described above.

[0098] In an optional embodiment, Figure 9 The diagram illustrates the process of determining data flow relationships in a data table grouping method, as shown below. Figure 9 As shown, the method includes at least the following steps: In step S910, based on the grouping results, determine the business data tables to be grouped that belong to the same group; if there are multiple business data tables to be grouped that belong to the same group, determine the field creation time of the target data field in the multiple business data tables to be grouped.

[0099] When there are multiple business data tables belonging to the same group, it is necessary to determine the creation time of the target data field in the business data table to be grouped, i.e., the field creation time.

[0100] For example, based on the grouping results, it is determined that the business data tables to be grouped are divided into two groups. Group 1 contains business data tables A, B, and C, while Group 2 contains business data tables D and E.

[0101] Based on this, the creation time of each target data field in the business data tables to be grouped (A, B, C, and D) is determined.

[0102] In step S920, if there are multiple field creation times corresponding to the business data table, the multiple field creation times are sorted in ascending order to obtain the lowest field creation time, so as to determine the lowest field creation time as the business creation time of the business data table to be grouped.

[0103] When there are multiple field creation times corresponding to the business data tables to be grouped, that is, when there are multiple target data fields corresponding to the business data tables to be grouped, the creation times of the multiple fields are sorted in ascending order to obtain the lowest field creation time of the earliest created target data field, and this lowest field creation time is used as the business creation time of creating the business data tables to be grouped.

[0104] For example, in the business data table D to be grouped, there are three fields for creation time: 17:52, 17:58, and 18:00. In this case, 17:52 is determined to be the lower field creation time, and 17:52 is taken as the business creation time of the business data table D to be grouped.

[0105] In step S930, the business creation time of the business data tables to be grouped in the same group is sorted in ascending order to obtain the ascending order sorting result, so as to determine the data flow relationship between the business data tables to be grouped based on the ascending order sorting result.

[0106] The ascending sort result is obtained by sorting the business creation time from low to high. Based on this, the data flow relationship is from the business data table with the earliest business creation time to the business data table with the most recent business creation time.

[0107] For example, if there are 3 business data tables to be grouped in Group 1, namely Business Data Table A, Business Data Table B, and Business Data Table C, and the field corresponding to Business Data Table A was created at 17:52, the field corresponding to Business Data Table B was created at 20:21, and the field corresponding to Business Data Table C was created at 21:01.

[0108] Based on this, the ascending sort results are 17:52, 20:21, and 21:01. From this, we can conclude that the data flow relationship is from business data table A to business data table B, and from business data table B to business data table C.

[0109] In this exemplary embodiment, the data flow relationship can be determined based on the field creation time. Subsequently, the relationship between the business data tables to be grouped in the same group can be drawn based on the data flow relationship. This not only helps developers understand the business data tables to be grouped, but also helps developers understand the relationship between the business data tables to be grouped in the same group, thereby improving the developer experience and increasing the speed of subsequent business processing by developers.

[0110] In the methods and apparatus provided by the exemplary embodiments of this disclosure, on the one hand, the clustering input data is generated based on the field grouping results, and the field grouping results are determined based on the target data field and the target field type, thereby providing an automatic method for obtaining grouping results without relying on manual methods, avoiding dependence on manual methods and improving the efficiency of obtaining grouping results; on the other hand, the grouping results are obtained based on the clustering results, which improves the accuracy of the grouping results and avoids grouping errors caused by manual methods.

[0111] The data table grouping method in this embodiment will be described in detail below with reference to an application scenario.

[0112] Figure 10 This illustrates a flowchart of data table grouping in an application scenario, such as... Figure 10As shown, step S1010 is the process of obtaining multiple business data tables to be grouped; step S1020 is the process of initializing the Array array; step S1030 is the process of determining the field grouping results and recording the field grouping results in the data Array; step S1040 is the process of determining the initial clustering input matrix based on the field grouping results; step S1050 is the process of performing dimensionality reduction processing on the initial clustering input matrix to obtain clustering input data; step S1060 is the process of performing clustering processing on the clustering input data to obtain clustering results and determining the grouping results of the business data tables to be grouped based on the clustering results; and step S1070 is the process of determining the business data tables to be grouped that belong to the same group and determining the field creation time of the target data fields of the business data tables to be grouped, so as to determine the data flow relationship based on the field creation time.

[0113] Specifically, if the business data tables to be grouped in the same group include business data table A, business data table B, and business data table C, the data flow relationship can be determined based on the field creation time, and the data flow relationship diagram can be shown as follows: Figure 11 As shown, data first flows from business data table A to business data table C, and then from business data table C to business data table B.

[0114] In this application scenario, on the one hand, the clustering input data is generated based on the field grouping results, and the field grouping results are determined based on the target data fields and target field types. This provides an automatic way to obtain grouping results without relying on manual methods, avoiding dependence on manual processes and improving the efficiency of obtaining grouping results. On the other hand, the grouping results are obtained based on the clustering results, which improves the accuracy of the grouping results and avoids grouping errors caused by manual processes.

[0115] Furthermore, in an exemplary embodiment of this disclosure, a data table grouping device is also provided. Figure 12 A schematic diagram of the data table grouping device is shown, such as... Figure 12 As shown, the data table grouping device 1200 may include: an acquisition module 1210, a dimension grouping module 1220, and a data table grouping module 1230. Wherein:

[0116] The acquisition module 1210 is configured to acquire multiple business data tables to be grouped corresponding to the target business database, and to acquire the target data fields and target field types corresponding to the target data fields in the multiple business data tables to be grouped; the dimension grouping module 1220 is configured to group the target data fields according to the grouping dimension to obtain the field grouping result, and to generate clustering input data according to the field grouping result; wherein, the grouping dimension is determined by the target data fields and the target field types; the data table grouping module 1230 is configured to perform clustering processing on the clustering input data to obtain the clustering result, so as to determine the grouping result of the multiple business data tables to be grouped according to the clustering result.

[0117] The specific details of the data table grouping device 1200 have been described in detail in the corresponding data table grouping method, so they will not be repeated here.

[0118] It should be noted that although several modules or units of the data table grouping device 1200 are mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0119] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0120] The following reference Figure 13 To describe an electronic device 1300 according to such an embodiment of the present invention. Figure 13 The electronic device 1300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0121] like Figure 13 As shown, the electronic device 1300 is manifested in the form of a general-purpose computing device. The components of the electronic device 1300 may include, but are not limited to: at least one processing unit 1310, at least one storage unit 1320, a bus 1330 connecting different system components (including storage unit 1320 and processing unit 1310), and a display unit 1340.

[0122] The storage unit stores program code that can be executed by the processing unit 1310, causing the processing unit 1310 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention.

[0123] Storage unit 1320 may include readable media in the form of volatile storage units, such as random access memory (RAM) 1321 and / or cache memory 1322, and may further include read-only memory (ROM) 1323.

[0124] Storage unit 1320 may also include a program / utility 1324 having a set (at least one) of program modules 1325, such program modules 1325 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may contain the reality of the network environment.

[0125] Bus 1330 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0126] Electronic device 1300 can also communicate with one or more external devices 1370 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 1300, and / or with any device that enables electronic device 1300 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 1350. Furthermore, electronic device 1300 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 1360. As shown, network adapter 1360 communicates with other modules of electronic device 1300 via bus 1330. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 1300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0127] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0128] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

[0129] refer to Figure 14 As shown, a program product 1400 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0130] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0131] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0132] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0133] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0134] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

Claims

1. A data table grouping method, characterized in that, The method includes: Obtain multiple business data tables to be grouped corresponding to the target business database, and obtain the target data fields and target field types corresponding to the target data fields in the multiple business data tables to be grouped; The target data fields are grouped according to the grouping dimension to obtain the field grouping result, and clustering input data is generated based on the field grouping result; wherein, the grouping dimension is determined by the target data fields and the target field type; the field grouping result represents the number of times each of the grouping dimensions appears in multiple business data tables to be grouped; the clustering input data is a matrix, in which each row corresponds to a business data table to be grouped, each column corresponds to a grouping dimension, and the element value indicates whether the grouping dimension corresponding to the column exists in the business data table corresponding to the row; Clustering is performed on the clustering input data to obtain clustering results, and the grouping results of multiple business data tables to be grouped are determined based on the clustering results.

2. The data table grouping method according to claim 1, characterized in that, The target data fields include non-relational fields; The process of grouping the target data fields according to the grouping dimension to obtain the field grouping results includes: Filter the non-relational fields in the target data fields to obtain relational fields, and group the relational fields according to the grouping dimension to obtain relational field grouping results; The grouping results of the relation fields in the business data table to be grouped are statistically analyzed to obtain grouping statistics. Based on the grouping statistics, the field grouping results corresponding to the business data table to be grouped are determined.

3. The data table grouping method according to claim 2, characterized in that, The step of generating clustering input data based on the grouping results of the fields includes: The number of grouping results for the same relation field in multiple business data tables to be grouped is calculated to obtain the count calculation result, and the count calculation result is sorted in descending order to obtain the count sorting result; Based on the sorting results, a preset number of high-number grouping results are determined in the grouping results of the relation field, and clustering input data is generated based on the high-number grouping results.

4. The data table grouping method according to claim 3, characterized in that, The step of generating clustering input data based on the high-number grouping results includes: Based on the high number of grouping results, multiple field row vectors corresponding to the multiple business data tables to be grouped are generated, and a clustering input matrix is ​​generated based on the multiple field row vectors.

5. The data table grouping method according to claim 4, characterized in that, The step of generating multiple field row vectors corresponding to the multiple business data tables to be grouped based on the high number of grouping results includes: An initial field row vector corresponding to the high-number grouping result is generated, and the relationship field grouping result and the high-number grouping result in the business data table to be grouped are judged in turn; wherein, the elements in the initial field row vector correspond one-to-one with the high-number grouping result; If a relational field group exists that is consistent with the high number of grouping results, in the initial field row vector, a target element corresponding to the high number of grouping results is determined, and the value of the target element is replaced with a preset element value to generate a field row vector corresponding to the business data table to be grouped.

6. The data table grouping method according to claim 4, characterized in that, The step of generating a clustering input matrix based on the multiple field row vectors includes: An initial clustering input matrix is ​​generated based on the row vectors of the multiple fields, and the initial clustering input matrix is ​​then subjected to dimensionality reduction processing to obtain the final clustering input matrix.

7. The data table grouping method according to claim 1, characterized in that, The method further includes: Based on the grouping results, determine the business data tables to be grouped that belong to the same group. If there are multiple business data tables to be grouped that belong to the same group, determine the field creation time of the target data field in the multiple business data tables to be grouped. If there are multiple creation times of the field corresponding to the business data table, sort the multiple creation times of the field in ascending order to obtain the lower field creation time, and determine the lower field creation time as the business creation time of the business data table to be grouped. The business creation time of the business data tables to be grouped in the same group is sorted in ascending order to obtain an ascending sort result, so as to determine the data flow relationship between the business data tables to be grouped based on the ascending sort result.

8. A data table grouping device, characterized in that, include: The acquisition module is configured to acquire multiple business data tables to be grouped corresponding to the target business database, and to acquire target data fields and target field types corresponding to the target data fields in the multiple business data tables to be grouped. The dimension grouping module is configured to group the target data field according to the grouping dimension to obtain the field grouping result, and generate clustering input data based on the field grouping result; wherein, the grouping dimension is determined by the target data field and the target field type; the field grouping result represents the number of times each grouping dimension appears in multiple business data tables to be grouped; the clustering input data is a matrix, in which each row corresponds to a business data table to be grouped, each column corresponds to a grouping dimension, and the element value indicates whether the grouping dimension corresponding to the column exists in the business data table corresponding to the row; The data table grouping module is configured to perform clustering processing on the clustering input data to obtain clustering results, so as to determine the grouping results of multiple business data tables to be grouped based on the clustering results.

9. An electronic device, characterized in that, include: processor; Memory for storing the executable instructions of the processor; The processor is configured to execute the data table grouping method of any one of claims 1-7 by executing the executable instructions.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, When the computer program is executed by a processor, it implements the data table grouping method according to any one of claims 1-7.

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