CN112685388B - Data model table construction method and device, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses a data model table construction method, a data model table construction device, electronic equipment and a computer readable medium. One embodiment of the method comprises: acquiring a user query record information set; performing field extraction on each user query record information in the user query record information set to generate a field group to obtain a field group set; generating a popularity score value set based on the field group set; generating a popularity field group set based on the popularity scoring value set and the field group set; and constructing a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set. The above embodiment can reduce the time occupied by data query.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a data model table construction method, a data model table construction device, electronic equipment and a computer readable medium.
Background
The construction of a data model table is a technology for creating a database table. At present, the commonly used database model table is constructed in the following manner: the data are divided into a plurality of categories, each category is subdivided into a plurality of categories, and a data model table is constructed by taking the category of the data as a table name and the category of the data as a field for storing the data so as to be used for user query.
However, when the data model table is constructed in the above manner, the following technical problems often exist:
firstly, because the data are respectively stored in different data tables, the data queried by the user often relate to a plurality of data tables, and therefore, the data needed by the user needs to be queried from different data tables, so that the time for querying the data is long;
second, the user is often required to input complex associated query statements to query the required data from different data tables, resulting in inefficient query.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose data model table construction methods, apparatuses, electronic devices and computer readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a data model table building method, including: acquiring a user query record information set; performing field extraction on each user query record information in the user query record information set to generate a field group to obtain a field group set; generating a popularity score value set based on the field group set; generating a popularity field group set based on the popularity scoring value set and the field group set; and constructing a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set.
In some embodiments, said determining a heat score value for each field in said set of field groups based on said first scoring parameter, said second scoring parameter, and said set of field groups comprises:
determining a heat score value of a field based on the first scoring parameter, the second scoring parameter, and the set of field groups by:
wherein the content of the first and second substances,
a value of the heat rating of the field is represented,
the sequence number is shown to indicate that,
indicating the number of field groups included in the field group set that have the same field as the field,
the fields are represented as such, and,
indicating the number of fields in a field group in which the fields included in the field group set are identical to the fields,
represents the first in the field group set
The number of fields in a field group that include the same field as the field,
representing a number of the fields included in a field group of the field group set,
represents the first in the field group set
The number of said fields comprised in a field group,
is representative of the first scoring parameter,
represents the second scoring parameter in the form of a second score,
indicating the number of fields in each field group in which the fields included in the field group set are the same as the fields.
In a second aspect, some embodiments of the present disclosure provide an apparatus for building a data model table, the apparatus including: an acquisition unit configured to acquire a set of user query record information; the extraction unit is configured to perform field extraction on each user query record information in the user query record information set to generate a field group, so as to obtain a field group set; a first generating unit configured to generate a set of popularity score values based on the set of field groups; a second generating unit configured to generate a popularity field group set based on the popularity score value set and the field group set; and the construction unit is configured to construct a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the data model table construction method of some embodiments of the disclosure, the time occupied by data query can be reduced. Specifically, the reason why the time taken for the related data query is large is that: since the data are stored in different data tables, the data queried by the user often involves multiple data tables, and therefore, the data needed by the user needs to be queried from different data tables. Based on this, the data model table construction method of some embodiments of the present disclosure not only calculates the heat score value of each field by querying the record information by the user, but also introduces the heat field. During the construction process of the data model table, the heat field can be selected from the field group set through the heat scoring value. And because of the participation of the heat scoring value, the fields in the field group set can be effectively selected. Therefore, the data model table generated according to the selected heat field groups can reduce the number of data tables involved when the user inquires data. Furthermore, the time occupied by data query is reduced.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of an application scenario of a data model table construction method according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of a data model table construction method according to the present disclosure;
FIG. 3 is a flow diagram of further embodiments of a data model table construction method according to the present disclosure;
FIG. 4 is a schematic block diagram of some embodiments of a data model table building apparatus according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 is a schematic diagram of one application scenario of a data model table construction method of some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may obtain a user query log information set 102. Second, the computing device 101 may perform field extraction on each user query record information in the user query record information set 102 to generate a field group, resulting in a field group set 103. In turn, the computing device 101 may generate a set of popularity score values 104 based on the set of field sets 103 described above. Computing device 101 may then generate a set of hot field groups 105 based on the set of hot scoring values 104 and the set of field groups 103. Finally, the computing device 101 may perform data model table construction on each of the heat field sets 105 to generate a data model table, resulting in a data model table set 106.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to FIG. 2, a flow 200 of some embodiments of a data model table construction method according to the present disclosure is shown. The data model table construction method comprises the following steps:
In some embodiments, an executing entity (such as the computing device 101 shown in fig. 1) of the data model table building method may obtain the user query record information set through a wired connection or a wireless connection. The user query record information may be a query record generated when a user queries data in the data table. The user query record information may include a library name, a table field, and the like, which are involved in the user query of the data table.
As an example, the user query record information may be: { [ db-name1, tb-name1, field: [ a, b, c ] ], [ db-name1, tb-name2, field: [ a, c, d ] ], [ db-name2, tb-name1, field: [ a, b, d ] }.
In some embodiments, the execution subject may perform field extraction on each user query record information in the user query record information set to generate a field group, so as to obtain a field group set. Wherein, the field extraction may be to extract a field name included in the user query record information. In addition, each field group in the field group set may include all fields extracted from one user query record information.
As an example, the field group may be "field: [ a, b, c ] ".
In some embodiments, the executing entity generates the set of popularity score values based on the set of field groups, and may include the following steps:
in the first step, the weight of each field in the field group set can be determined through a TF-IDF (term frequency-inverse document frequency) algorithm.
Second, the difference between 1 and the product of the word frequency and the weight of each field in the field group is determined as the heat rating value of the field.
And step 204, generating a popularity field group set based on the popularity score value set and the field group set.
In some embodiments, the execution agent may generate a set of popularity field groups based on the set of popularity score values and the set of field groups. For each field group in the field group set, the field in the field group corresponding to the heat score value greater than the heat threshold in the heat score value set may be determined as a heat field, so as to obtain a heat field group. Thus, a set of hot field groups may be generated. In practice, the hot field may be a field with a large number of searches by the user. The heat score value may be used to characterize how repeatedly a field is searched by a user.
As an example, the above-mentioned heat threshold may be: 0.8.
in some optional implementations of some embodiments, the generating, by the execution subject, a set of popularity field groups based on the set of popularity score values and the set of field groups may include:
the method comprises the following steps of firstly, determining the heat score value which is greater than a preset heat score threshold value in the heat score value set as a first heat score value, and obtaining the first heat score value set. Wherein the predetermined hotness score threshold may be 0.9.
And secondly, generating a popularity field group set based on the first popularity score value set and the field group set. Each first hot score value in the first hot score value set and each corresponding field in each field group in the field group set can be used as a hot field group. Thus, a set of hot field groups is generated.
In some optional implementations of some embodiments, the executing body generating a popularity field group set based on the first popularity score value set and the field group set may include:
and step one, selecting fields corresponding to each first hot scoring value in the first hot scoring value set from the field group set as fields to be constructed, and obtaining a field set to be constructed. The first hot score value and the hot score value are in one-to-one correspondence, and the hot score value and the field are also in one-to-one correspondence.
And secondly, carrying out relevance analysis on each first heat score value in the first heat score value set to obtain a relevance analysis value set. The relevance analysis may be to analyze the first set of scores by an analysis algorithm (e.g., an analysis of variance algorithm, a significance check algorithm, etc.) to obtain a relevance analysis value of each first score.
In some optional implementations of some embodiments, the executing body generates a popularity field group set based on the first popularity score value set and the field group set, and may further include the following steps:
and classifying the field sets to be constructed based on the correlation analysis value sets to obtain a heat field set. Wherein, the correlation analysis values in the correlation analysis value set can be classified by using preset segmentation threshold values (e.g., [0.1, 3), [0.3, 0.6), [0.6, 0.9 ]). And then, grouping the fields to be constructed in the field set to be constructed according to the corresponding correlation analysis values to obtain a field set to be constructed, wherein the field set to be constructed is used as a hot field set.
In some embodiments, the execution subject may perform data model table construction on each of the heat field groups in the heat field group set to generate a data model table, so as to obtain a data model table set. Wherein, building the data model table may be adding each field in each hot field group to a statement (e.g., a structured query language or an unstructured query language) of a building database table (e.g., a data table of a relational database management system or a data table of a non-relational database management system) to build the data model table.
Optionally, the executing body may further execute the following steps:
firstly, performing performance test on each data model table in the data model table set to generate a performance test result, and obtaining a performance test result set. The performance test may be to perform data query on the data model table through a preset data query statement, and record a duration of the data query. This duration may be taken as a performance test result.
And secondly, sending the data model table corresponding to the performance test result meeting the preset conditions in the performance test result set to a server. The predetermined condition may be that the performance test result is less than a preset time threshold (e.g., 0.1 second). And sending the data model table to a service for inputting data and inquiring by a user.
The above embodiments of the present disclosure have the following advantages: by the data model table construction method of some embodiments of the disclosure, the time occupied by data query can be reduced. Specifically, the reason why the time taken for the related data query is large is that: since the data are stored in different data tables, the data queried by the user often involves multiple data tables, and therefore, the data needed by the user needs to be queried from different data tables. Based on this, the data model table construction method of some embodiments of the present disclosure not only calculates the heat score value of each field by querying the record information by the user, but also introduces the heat field. During the construction process of the data model table, the heat field can be selected from the field group set through the heat scoring value. And because of the participation of the heat scoring value, the fields in the field group set can be effectively selected. Therefore, the data model table generated according to the selected heat field groups can reduce the number of data tables involved when the user inquires data. Furthermore, the time occupied by data query is reduced.
With further reference to FIG. 3, a flow 300 of further embodiments of a data model table construction method is illustrated. The process 300 of the data model table construction method includes the following steps:
In some embodiments, the specific implementation manner and technical effects of the steps 301 and 302 can refer to the steps 201 and 202 in the embodiments corresponding to fig. 2, which are not described herein again.
In some embodiments, the execution body may determine a total number of fields in the field group set as the first scoring parameter.
In some embodiments, the execution body may determine the number of field groups in the field group set as the second scoring parameter.
In some embodiments, the execution body may determine a popularity score of each field in the field group set based on the first scoring parameter, the second scoring parameter, and the field group set to generate popularity scores, resulting in a popularity score set. Wherein, the ratio of each field in the field group to the product of the first scoring parameter and the second scoring parameter can be determined as the hot scoring value.
In some optional implementations of some embodiments, the determining, by the execution main body, a popularity score value of each field in the field group set to generate the popularity score value based on the first scoring parameter, the second scoring parameter, and the field group set may include:
and step one, determining the product of the sum of the ratios of the fields and the field groups corresponding to the fields and the number of the field groups which are the same as the fields and the fields in the field group set as a third grading parameter.
And secondly, determining the number of the fields in each field group in the field group set as a first number parameter.
And thirdly, determining the number of fields in each field group, which are the same as the fields in the field group set, as a second number parameter.
And a fourth step of generating the heat rating value according to the first rating parameter, the second rating parameter, the third rating parameter, the first quantity parameter, and the second quantity parameter.
In some optional implementations of some embodiments, the determining, by the execution main body, a popularity score value of each field in the field group set to generate the popularity score value based on the first scoring parameter, the second scoring parameter, and the field group set may include:
based on the first scoring parameter, the second scoring parameter and the field group set, determining the hot scoring value of the field (the calculation result can keep three decimal places) by the following formula:
wherein the content of the first and second substances,
indicating the heat rating value of the field.
Indicating a serial number.
Indicating the number of field groups included in the field group set, which are the same as the fields described above.
The above fields are indicated.
Indicating the number of fields in a field group that includes the same field as the field.
Indicating the first in the above field group set
The number of fields in the same field group as the above-mentioned fields.
Indicating the number of the fields included in the field group set.
Indicating the first in the above field group set
The number of the above fields included in the individual field group.
Representing the first scoring parameter.
Representing the second scoring parameter.
Indicating the number of fields in each field group in which the fields included in the field group set are the same as the fields.
The above formula and its related content are used as an inventive point of the embodiments of the present disclosure, and a second technical problem mentioned in the background art is solved, that is, the query efficiency is low because a user often needs to input a complex associated query statement to query the required data from different data tables. Factors that lead to query inefficiency tend to be as follows: the user is required to input a complex associated query statement to query the required data from different data tables. If the above factors are solved, the query efficiency can be improved. To achieve this, first, since the hot field can be a field with a high user query degree, the hot field is used for characterizing the potential query habits of most users. Therefore, the popularity score value is introduced, and the popularity score value can be used for selecting the field with higher query repetition rate from the query record information of the user as the popularity field. Then, the influence factors of the generated heat score values, such as the proportion of each field in each field group, the proportion of each field in all field groups, and the proportion of the number of fields in each field group in all field groups, are considered. Since the hot field is a field used to characterize the query habits of most users. Therefore, the generated popularity score value needs to consider the occupation ratio of each field in different field groups and the number of field groups in which each field is located (wherein, the larger the number of field groups in which a field is located, the more the field conforms to the query habits of different users). In addition, since the ratio of the number of the fields in all the field groups is the influence factor of the heat score value (i.e., the more times all the users search for a field, the more the field conforms to the query habits of the users). Thus, the above formula also introduces a specific gravity between the number of fields and all the fields. In addition, the number of the field group in which the introduced field is located and the number of the field in all the field groups can have a positive correlation with the hot score value, so that the hot score value can be further adjusted. Finally, the generated heat grading value can be more accurate because the formula introduces a plurality of influence factors of the generated heat grading value. Therefore, the popularity field screened out by the popularity score value can better accord with the query habits of most users. Therefore, the data model table constructed by the hot field can meet the query conditions of the user to a certain extent, and the user can be prevented from inputting complex associated query statements to query required data from a plurality of data tables to a certain extent. Furthermore, the efficiency of data query is improved.
And 307, constructing a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set.
In some embodiments, the specific implementation manner and technical effects of steps 306-307 can refer to steps 204-205 in those embodiments corresponding to fig. 2, which are not described herein again.
As can be seen from fig. 3, compared with the description of some embodiments corresponding to fig. 2, the flow 300 of the data model table building method in some embodiments corresponding to fig. 3 embodies the step of generating the set of hotness score values. First, the field of the degree of popularity is considered to be a field with a high degree of user query, so that the field of popularity is used for characterizing the potential query habits of most users. Therefore, the popularity score value is introduced, and the popularity score value can be used for selecting the field with higher query repetition rate from the query record information of the user as the popularity field. Then, the influence factors of the generated heat score values, such as the proportion of each field in each field group, the proportion of each field in all field groups, and the proportion of the number of fields in each field group in all field groups, are considered. Since the hot field is a field used to characterize the query habits of most users. Therefore, the generated popularity score value needs to consider the occupation ratio of each field in different field groups and the number of field groups in which each field is located (wherein, the larger the number of field groups in which a field is located, the more the field conforms to the query habits of different users). In addition, since the ratio of the number of the fields in all the field groups is the influence factor of the heat score value (i.e., the more times all the users search for a field, the more the field conforms to the query habits of the users). Thus, the above formula also introduces a specific gravity between the number of fields and all the fields. In addition, the number of the field group in which the introduced field is located and the number of the field in all the field groups can have a positive correlation with the hot score value, so that the hot score value can be further adjusted. Finally, the generated heat grading value can be more accurate because the formula introduces a plurality of influence factors of the generated heat grading value. Therefore, the popularity field screened out by the popularity score value can better accord with the query habits of most users. Therefore, the data model table constructed by the hot field can meet the query conditions of the user to a certain extent, and the user can be prevented from inputting complex associated query statements to query required data from a plurality of data tables to a certain extent. Furthermore, the efficiency of data query is improved.
With further reference to fig. 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a data model table construction apparatus, which correspond to those of the method embodiments shown in fig. 2, and which may be applied in various electronic devices.
As shown in fig. 4, the inventory-related information display device 400 of some embodiments includes: an acquisition unit 401, an extraction unit 402, a first generation unit 403, a second generation unit 404, and a construction unit 405. The obtaining unit 401 is configured to obtain a user query record information set; an extracting unit 402, configured to perform field extraction on each user query record information in the user query record information set to generate a field group, so as to obtain a field group set; a first generating unit 403 configured to generate a set of popularity score values based on the set of field groups; a second generating unit 404 configured to generate a popularity field set based on the popularity score value set and the field set; the constructing unit 405 is configured to perform data model table construction on each hot field group in the hot field group set to generate a data model table, so as to obtain a data model table set.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a user query record information set; performing field extraction on each user query record information in the user query record information set to generate a field group to obtain a field group set; generating a popularity score value set based on the field group set; generating a popularity field group set based on the popularity scoring value set and the field group set; and constructing a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, an extraction unit, a first generation unit, a second generation unit, and a construction unit. Where the names of these units do not in some cases constitute a limitation on the units themselves, for example, the acquisition unit may also be described as a "unit that acquires a user query record information set".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (9)
1. A data model table construction method comprises the following steps:
acquiring a user query record information set;
performing field extraction on each user query record information in the user query record information set to generate a field group, so as to obtain a field group set;
generating a set of popularity score values based on the set of field groups;
generating a popularity field group set based on the popularity score value set and the field group set;
constructing a data model table for each heat field group in the heat field group set to generate a data model table, so as to obtain a data model table set;
wherein generating a set of popularity score values based on the set of field groups comprises:
determining the sum of the number of fields included in each field group in the field group set as a first scoring parameter;
determining the number of field groups in the field group set as a second scoring parameter;
determining the heat score value of each field in the field group set based on the first scoring parameter, the second scoring parameter and the field group set to obtain a heat score value set;
wherein determining the heat score value of each field in the field group set comprises:
the ratio of the occupation ratio of each field in the field group to the product of the first scoring parameter and the second scoring parameter is determined as the heat scoring value.
2. The method of claim 1, wherein the method further comprises:
performing performance test on each data model table in the data model table set to generate a performance test result, and obtaining a performance test result set;
and sending the data model table corresponding to the performance test result meeting the preset condition in the performance test result set to a server.
3. The method of claim 1, wherein generating a set of popularity field groups based on the set of popularity score values and the set of field groups comprises:
determining the heat score value which is greater than a preset heat score threshold value in the heat score value set as a first heat score value to obtain a first heat score value set;
generating a set of popularity field sets based on the first set of popularity score values and the set of field sets.
4. The method of claim 3, wherein generating a set of popularity field groups based on the first set of popularity score values and the set of field groups comprises:
selecting fields corresponding to each first heat score value in the first heat score value set from the field group set as heat fields to obtain a heat field set;
and performing relevance analysis on each first heat score value in the first heat score value set to generate a relevance analysis value, so as to obtain a relevance analysis value set.
5. The method of claim 4, wherein the generating a set of popularity field sets based on the first set of popularity score values and the set of field sets, further comprises:
and classifying the heat field set based on the correlation analysis value set to obtain a heat field set.
6. The method of claim 1, wherein said determining a heat score value for each field in the set of field groups based on the first scoring parameter, the second scoring parameter, and the set of field groups comprises:
determining a product of the sum of the occupation ratios of the fields in the corresponding field groups and the number of the field groups including the fields in the field group set as a third scoring parameter;
determining the sum of the number of the fields in each field group in the field group set as a first number parameter;
determining the sum of the number of fields in each field group including the field in the field group set as a second number parameter;
and generating the heat scoring value according to the first scoring parameter, the second scoring parameter, the third scoring parameter, the first quantity parameter and the second quantity parameter.
7. A data model table building apparatus, comprising:
an acquisition unit configured to acquire a set of user query record information;
the extracting unit is configured to perform field extraction on each user query record information in the user query record information set to generate a field group, so as to obtain a field group set;
a first generating unit configured to generate a set of popularity score values based on the set of field groups;
a second generating unit configured to generate a popularity field group set based on the popularity score value set and the field group set;
the construction unit is configured to construct a data model table for each heat field group in the heat field group set to generate a data model table, so that a data model table set is obtained;
wherein generating a set of popularity score values based on the set of field groups comprises:
determining the sum of the number of fields included in each field group in the field group set as a first scoring parameter;
determining the number of field groups in the field group set as a second scoring parameter;
determining the heat score value of each field in the field group set based on the first scoring parameter, the second scoring parameter and the field group set to obtain a heat score value set;
wherein determining the heat score value of each field in the field group set comprises:
the ratio of the occupation ratio of each field in the field group to the product of the first scoring parameter and the second scoring parameter is determined as the heat scoring value.
8. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.
9. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-6.
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