CN111814445A - Data table generation method, device and system - Google Patents
Data table generation method, device and system Download PDFInfo
- Publication number
- CN111814445A CN111814445A CN202010566293.5A CN202010566293A CN111814445A CN 111814445 A CN111814445 A CN 111814445A CN 202010566293 A CN202010566293 A CN 202010566293A CN 111814445 A CN111814445 A CN 111814445A
- Authority
- CN
- China
- Prior art keywords
- data
- data table
- configuration information
- target
- generating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000012545 processing Methods 0.000 claims description 27
- 238000010586 diagram Methods 0.000 claims description 23
- 238000003860 storage Methods 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 9
- 230000004044 response Effects 0.000 claims description 7
- 238000009826 distribution Methods 0.000 description 28
- 238000010801 machine learning Methods 0.000 description 16
- 238000012360 testing method Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 3
- 238000012790 confirmation Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000007667 floating Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003238 somatosensory effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
- G06F40/166—Editing, e.g. inserting or deleting
- G06F40/183—Tabulation, i.e. one-dimensional positioning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F8/00—Arrangements for software engineering
- G06F8/70—Software maintenance or management
- G06F8/71—Version control; Configuration management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Abstract
The specification provides a method, a device and a system for generating a data table, wherein the method comprises the following steps: acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field; and generating a data table according to the configuration information, wherein each data object in the data table meets the corresponding limiting condition.
Description
Technical Field
The present specification relates to the field of data processing technologies, and more particularly, to a method for generating a data table, a device for generating a data table, a system including at least one computing device and at least one storage device, and a computer-readable storage medium.
Background
With the rapid development and wide application of technologies such as artificial intelligence and big data, new technologies and application scenes are continuously emerging. These new technologies and scenarios first require experiments or tests with specific data.
In the prior art, a developer generally manually constructs a data table according with an application scenario or specific requirements, and performs corresponding experiments or tests. However, building a data table by a developer is inefficient and labor intensive, increasing testing costs.
Disclosure of Invention
An object of the present specification is to provide a new technical solution for automatically generating a data table.
According to a first aspect of the present specification, there is provided a method for generating a data table, including:
acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field;
and generating a data table according to the configuration information, wherein each data object in the data table meets a corresponding limiting condition.
Optionally, the obtaining the configuration information includes:
a configuration interface for providing configuration information;
and acquiring the configuration information through the configuration interface.
Optionally, the obtaining the configuration information includes:
providing an entry for uploading a configuration file;
acquiring a configuration file uploaded by a user through the entrance;
and analyzing the configuration file to obtain the configuration information.
Optionally, the method further includes:
detecting whether the configuration file is legal or not;
under the condition that the configuration file is legal, executing the step of analyzing the configuration file to obtain the configuration information;
and generating an error log under the condition that the configuration file is illegal.
Optionally, the format of the configuration file is any one of xlsx, csv, tsv and parquet.
Optionally, the method further includes:
providing a canvas area for creating a data processing flow diagram in an interface;
the information acquisition module is further configured to:
in response to an operation of selecting a target data source by a user, displaying the target data source selected by the user in the canvas area; wherein the target data source comprises the configuration information;
the data table generation module is further configured to:
responding to the operation of selecting a target operator by a user, and displaying the target operator selected by the user in the canvas area; the target operator is used for generating a data table according to input information;
connecting the target data source and the target operator in the layout area to construct a data processing flow chart;
and operating the data processing flow chart to obtain the data table.
Optionally, the configuration information further includes a total number of rows of a data table and a first number of execution units for generating the data table in parallel,
the generating a data table according to the configuration information comprises:
determining the number of rows of the data object generated by each execution unit as a second number according to the total number of rows and the first number of the execution units which generate the data table in parallel;
controlling each execution unit to generate a corresponding second quantity of row data objects;
and obtaining the data table according to the data object generated by each execution unit.
Optionally, the limitation condition includes at least one of: data type, data distribution type, data range, number of unique values, null rate, data gradient and data regularity.
Optionally, the constraint condition of the target field includes the number of unique values;
before generating the target data object corresponding to the target field in the data table according to the configuration information, the method further comprises: determining a plurality of numerical values corresponding to the target field according to the number of the unique values to serve as first candidate values;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the first candidate value.
Optionally, the limitation condition of the target field further includes a data gradient; the data gradient comprises a set proportion of the sum of the frequencies of a set number of values with the most frequent occurrence to the total number of target data objects corresponding to the target field;
before generating the target data object corresponding to the target field in the data table according to the configuration information, the method further includes:
determining a set number of values with the maximum occurrence frequency corresponding to the target field as second candidate values;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object of the target field according to the set proportion and the second candidate value.
Optionally, the limitation condition of the target field further includes a null rate,
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and emptying the target data object corresponding to the target field according to the empty value rate.
Optionally, the constraint condition of the target field further includes a data rule;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises:
and generating data conforming to the data rule as a target data object corresponding to the target field in the data table.
Optionally, the limitation condition of the target field further includes a data distribution type;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data distribution type, so that the value of the target data object conforms to the data distribution type.
Optionally, the limitation condition of the target field further includes a data range;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data range, so that the value of the target data object is in the data range.
Optionally, the configuration information further includes a specified data type;
the method further comprises the following steps:
and converting the target data object corresponding to the target field into the specified data type.
Optionally, the method further includes:
and responding to the operation of viewing the data table, and displaying the data table.
Optionally, the method further includes:
determining an application scene to which the data table is applicable;
searching for an application item matched with the application scene;
and inputting the data table to the application project.
Optionally, the method further includes:
acquiring a preset machine learning model;
and testing the performance of the machine learning model according to the data table.
According to a second aspect of the present specification, there is provided a data table generation apparatus including:
the information acquisition module is used for acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field;
and the data table generating module is used for generating a data table according to the configuration information, wherein each data object in the data table meets the corresponding limiting condition.
Optionally, the information obtaining module is further configured to:
a configuration interface for providing configuration information;
and acquiring the configuration information through the configuration interface.
Optionally, the information obtaining module is further configured to:
providing an entry for uploading a configuration file;
acquiring a configuration file uploaded by a user through the entrance;
and analyzing the configuration file to obtain the configuration information.
Optionally, the method further includes:
means for detecting whether the configuration file is legitimate;
a module for executing the analysis of the configuration file to obtain the configuration information under the condition that the configuration file is legal;
and generating an error log if the configuration file is illegal.
Optionally, the format of the configuration file is any one of xlsx, csv, tsv and parquet.
Optionally, the method further includes:
a module for providing a canvas area in an interface for creating a data processing flow diagram;
the information acquisition module is further configured to:
in response to an operation of selecting a target data source by a user, displaying the target data source selected by the user in the canvas area; wherein the target data source comprises the configuration information;
the data table generation is further to:
responding to the operation of selecting a target operator by a user, and displaying the target operator selected by the user in the canvas area; the target operator is used for generating a data table according to input information;
connecting the target data source and the target operator in the layout area to construct a data processing flow chart;
and operating the data processing flow chart to obtain the data table.
Optionally, the configuration information further includes a total number of rows of a data table and a first number of execution units for generating the data table in parallel,
the generating a data table according to the configuration information comprises:
determining the number of rows of the data object generated by each execution unit as a second number according to the total number of rows and the first number of the execution units which generate the data table in parallel;
controlling each execution unit to generate a corresponding second quantity of row data objects;
and obtaining the data table according to the data object generated by each execution unit.
Optionally, the limitation condition includes at least one of: data type, data distribution type, data range, number of unique values, null rate, data gradient and data regularity.
Optionally, the constraint condition of the target field includes the number of unique values;
the device further comprises: a module for determining a plurality of numerical values corresponding to the target field as first candidate values according to the number of the unique values;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the first candidate value.
Optionally, the limitation condition of the target field further includes a data gradient; the data gradient comprises a set proportion of the sum of the frequencies of a set number of values with the most frequent occurrence to the total number of target data objects corresponding to the target field;
the device further comprises:
a module for determining a set number of values corresponding to the target field with the largest frequency of occurrence as a second candidate value;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object of the target field according to the set proportion and the second candidate value.
Optionally, the limitation condition of the target field further includes a null rate,
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and emptying the target data object corresponding to the target field according to the empty value rate.
Optionally, the constraint condition of the target field further includes a data rule;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises:
and generating data conforming to the data rule as a target data object corresponding to the target field in the data table.
Optionally, the limitation condition of the target field further includes a data distribution type;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data distribution type, so that the value of the target data object conforms to the data distribution type.
Optionally, the limitation condition of the target field further includes a data range;
generating a target data object corresponding to a target field in a data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data range, so that the value of the target data object is in the data range.
Optionally, the configuration information further includes a specified data type;
the device further comprises:
and the module is used for converting the target data object corresponding to the target field into the specified data type.
Optionally, the method further includes:
means for presenting the data table in response to an operation to view the data table.
Optionally, the method further includes:
a module for determining an application scenario to which the data table applies;
a module for searching an application item matched with the application scene;
means for inputting the data table to the application project.
Optionally, the method further includes:
a module for obtaining a preset machine learning model;
a module for testing performance of the machine learning model according to the data table.
According to a third aspect of the present specification, there is provided a system comprising at least one computing device and at least one storage device, wherein the at least one storage device is for storing instructions for controlling the at least one computing device to perform the method according to the first aspect of the present specification.
According to a fourth aspect of the present description, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method according to the first aspect of the present description.
In the embodiments of the present specification, the data table is automatically generated by the configuration information. Therefore, the data table meeting the requirements of the user can be automatically generated, the construction efficiency of the data table is improved, and the construction cost of the data table is reduced.
Other features of the present description and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a block diagram of one example of a hardware configuration of an electronic device that can be used to implement embodiments of the present description.
FIG. 2 is a flow chart diagram of a method for generating a data table according to an embodiment of the present description;
FIG. 3 is a schematic diagram of an interface for obtaining configuration information according to a first embodiment of the present disclosure;
FIG. 4 is a schematic diagram of an interface for obtaining configuration information according to a second embodiment of the present disclosure;
FIG. 5 is a schematic diagram of an interface for obtaining configuration information according to a third embodiment of the present disclosure;
FIG. 6 is a block schematic diagram of an example of an apparatus for generating a data table according to an embodiment of the present description;
fig. 7 is a block schematic diagram of a system in accordance with an embodiment of the present description.
Detailed Description
Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present specification unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Various embodiments and examples according to embodiments of the present specification are described below with reference to the drawings.
< hardware configuration >
Fig. 1 is a block diagram showing a hardware configuration of an electronic apparatus 1000 that can implement an embodiment of the present specification.
The electronic device 1000 may be a laptop, desktop, cell phone, tablet, etc. As shown in fig. 1, the electronic device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. The processor 1100 may be a central processing unit CPU, a microprocessor MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a headphone interface, and the like. The communication device 1400 is capable of wired or wireless communication, for example, and may specifically include Wifi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. The display device 1500 is, for example, a liquid crystal display panel, a touch panel, or the like. The input device 1600 may include, for example, a touch screen, a keyboard, a somatosensory input, and the like. A user can input/output voice information through the speaker 1700 and the microphone 1800.
The electronic device shown in fig. 1 is merely illustrative and is in no way intended to limit the description, its application, or uses. In the embodiment of the present disclosure, the memory 1200 of the electronic device 1000 is used to store instructions for controlling the processor 1100 to operate so as to execute any one of the data table generating methods provided in the embodiments of the present disclosure. It will be appreciated by those skilled in the art that although a number of means are shown for the electronic device 1000 in fig. 1, this description may refer to only some of the means therein, e.g. the electronic device 1000 refers to only the processor 1100 and the memory means 1200. The skilled person can design the instructions according to the solution disclosed in the present specification. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
< method examples >
In the present embodiment, a method for generating a data table is provided. The method of generating the data table may be implemented by an electronic device. The electronic device may be the electronic device 1000 as shown in fig. 1.
As shown in fig. 2, the method for generating a data table according to this embodiment may include the following steps S2100 to S2200:
in step S2100, configuration information is acquired.
The configuration information at least includes information describing fields represented by each column of data objects in the data table to be generated and the defining conditions of the data objects corresponding to each field.
In one embodiment of the present specification, the configuration information may further include a total number of rows of the data table to be generated, and may further include a first number of execution units for generating the data table in parallel. The total number of rows may represent the number of data objects corresponding to each field.
In one embodiment of the present description, the defining condition may include at least one of: data type, data distribution type, data range, number of unique values, null rate, data gradient and data regularity.
Data types may include, for example, int integer, long integer, float single precision floating point, double precision floating point, string type, date type, time type.
The data rule is used for expressing the rule accorded by the data object of the corresponding field, such as an identity card number, a mobile phone number, a bank card number and the like. For example, the identification card number meeting the corresponding conditions can be generated according to the gender, the age and the attribution place of the identification card, and the information such as the gender, the age and the attribution place of the identification card can be analyzed reversely according to the identification card number. For another example, a mobile phone number meeting corresponding conditions may be generated according to an operator and a mobile phone home location, and information such as the operator and the home location of the mobile phone may be analyzed reversely according to the mobile phone number. For another example, the bank card number satisfying the condition may be generated according to the bank name and the bank card category (for example, the bank card may include a debit card, a credit card, and a quasi-credit card), and the information such as the bank name and the bank card category may be reversely analyzed according to the bank card number.
The data distribution type is used to indicate the distribution type to which the data object of the corresponding field conforms. The data distribution type may include, for example, a data distribution type of a main stream such as bernoulli distribution, binomial distribution, geometric distribution, chi-square distribution, poisson distribution, uniform distribution, normal distribution, and the like. The type of data distribution may also include sample distribution types that specify probabilistic samples, unspecific probabilistic samples, and samples from fields of other data tables.
The data range may be used to indicate the range in which the data object of the corresponding field is located. The data range in this embodiment may be a discrete data range or a continuous data range.
The discrete data range indicates the data category contained in the field to be generated, such as "large, female" indicates that the data category of the field to be generated can only be large or female. The contiguous data range may be a single or multiple span ranges, indicating that the data object of the corresponding field must be within the specified span range. For example, a single interval range such as "(20, 50 ]", may indicate that the data object value must satisfy 20< value ≦ 50. further, for example, a plurality of interval ranges such as "(20, 50], [90, 100 ]" may indicate that the data object value must satisfy 20< value ≦ 50or 90 ≦ value ≦ 100.
The unique value number indicates the number of different values that the data object to correspond to a field contains.
The null rate may represent a null rate of the data object of the corresponding field.
The data gradient may represent a proportion of a sum of frequencies of the n values at which the data objects of the corresponding field appear most frequently to the total data amount. For example, the data gradient is "10, 0.85", which indicates that the sum of the frequencies of 10 values that occur most frequently in the data objects of the corresponding fields accounts for 85% of the total number of the data objects.
In one embodiment of the present specification, the manner of acquiring the configuration information may include:
a configuration interface for providing configuration information; and acquiring configuration information through the configuration interface.
Specifically, at least one group of input boxes can be included in the configuration interface, and the group of input boxes can include a first input box for inputting a field name and a second input box for inputting a definition condition of a data object corresponding to the field. For the same set of input boxes, there is a one-to-one correspondence between qualifiers and field names.
As shown in fig. 3, the user may input the field name of the target field through the first input box, where the target field may be a field represented by any column of the data object in the data table. The user can also input the definition conditions of the data object corresponding to the target field through the second input box.
In one example, the field name of the target field may be age, the data object corresponding to the target field may be a specific numerical value, and the limitation condition of the data object corresponding to the target field may include a data range, which may be [18, 70], for example, indicating that the data object corresponding to the age is greater than or equal to 18 and less than or equal to 70.
In another embodiment of the present specification, the manner of obtaining the configuration information may include:
providing an entry for uploading a configuration file; acquiring a configuration file uploaded by a user through the entrance; and analyzing the configuration file to obtain configuration information.
As shown in fig. 4, a "click to upload" button may be provided as a portal for uploading a profile. The user clicks the button, the electronic device is triggered to provide a window for selecting the configuration file, and the user selects the configuration file according to an application scene or specific requirements. In the window, a confirmation button may be provided, and the user may trigger the electronic device to acquire the configuration file uploaded by the user by clicking the confirmation button. When the electronic device obtains the configuration file, the name of the configuration file may be displayed in an interface for a user to view.
The electronic device can obtain configuration information for describing fields represented by each line of data objects in the data table to be generated and limiting conditions of the data objects corresponding to each field by analyzing the configuration file so as to generate the data table.
In one embodiment of the present description, the format of the configuration file may be any one of xlsx, csv, tsv, and parquet.
On the basis of the embodiment, the method may further include:
detecting whether the configuration file is legal or not; under the condition that the configuration file is legal, executing a step of analyzing the configuration file to obtain configuration information; in the event that the configuration file is illegal, an error log is generated.
In this embodiment, the error log may include a configuration file illegal reason. Under the condition of generating the error log, the error log can be displayed so that a user can check the reason why the configuration file is illegal and correspondingly modify the configuration file.
In yet another embodiment of the present specification, the function of generating the data table according to the input information may be provided by a preset target operator. On this basis, the method can further comprise: a canvas area for creating a data processing flow diagram is provided in the interface. Then, obtaining the configuration information may include: and in response to the operation of selecting the target data source by the user, showing the target data source selected by the user in the canvas area. Wherein, the target data source comprises the configuration information.
Specifically, a data source list area may be provided in the interface, and is used to show icons and/or names of data sources that can be used for building the data processing flow chart, so as to be selected by the current user. The user may drag the selected target data source into the canvas area to achieve the effect of exposing the user-selected target data source in the canvas area in response to the user-selected target data source.
Step S2200 is to generate a data table according to the configuration information.
Wherein, each data object in the data table satisfies the corresponding limiting condition.
In one embodiment of the present description, a generation button may be provided in the interface, and the user may trigger the electronic device to execute the step of generating the data table according to the configuration information by clicking the generation button.
In another embodiment of the present description, the configuration information is obtained by exposing a target data source selected by a user to a canvas area. Correspondingly, generating the data table according to the configuration information may include: responding to the operation of selecting a target operator by a user, and displaying the target operator selected by the user in a canvas area; connecting a target data source and a target operator in the layout area to construct a data processing flow chart; and operating the data processing flow chart to obtain a data table. Wherein the target operator may be an operator for generating a data table from the input information.
For example, as shown in fig. 5, a user may drag the target data source and the target operator to the canvas area, and connect the target data source and the target operator in the canvas area to obtain the data processing flowchart. The interface also comprises an operation button, and a user can trigger the operation of operating the data processing flow chart by clicking the operation button so as to obtain the data sheet.
In the embodiments of the present specification, the data table is automatically generated by the configuration information. Therefore, the data table meeting the requirements of the user can be automatically generated, the construction efficiency of the data table is improved, and the construction cost of the data table is reduced.
In one embodiment of the present specification, the step of generating the data table from the configuration information may be performed by a distributed system configured by a plurality of execution units.
In this embodiment, the configuration information further includes a total number of rows of the data table and a first number of execution units for generating the data table in parallel. The total number of rows may represent the number of data objects corresponding to any field.
On this basis, generating the data table according to the configuration information may include steps S2210 to S2230 as follows:
in step S2210, the number of rows of the data object generated by each execution unit is determined as a second number based on the total number of rows in the data table and the first number of execution units used to generate the data table in parallel.
In this embodiment, the number of rows of the generated data objects may be evenly distributed to the execution units for parallel generation of the data table according to the total number of rows of the data table and the first number of execution units for parallel generation of the data table. For example, if the total number of rows in the data table is M and the first number of execution units used to generate the data table in parallel is N1, then the number of rows of data objects generated by each execution unit may be the same, i.e., the corresponding second number N2 generated by each execution unit is the same, and N2 is M/N1. If the total number of rows M of the data table is not an integer multiple of the first number N1, then execution units may be randomly selected for generation for the remaining M% N1 row data objects.
In one embodiment of the present specification, the number of rows of the data object generated by each execution unit may also be allocated according to the operation capability of each execution unit. For the execution unit with stronger computing power, the corresponding second number can be set to be larger; for the execution units with weaker operation capability, the corresponding second number can be set to be smaller. And the sum of the second numbers corresponding to all the execution units for generating the data table in parallel is the total number of rows of the data table.
Step S2220, controls each execution unit to generate a corresponding second amount of data objects.
For each execution unit, the generated second number of data objects may form a sub-data table, and for each sub-data table, the fields corresponding to the data objects in the same column are the same, and each data object satisfies the corresponding limit condition. For each row of data objects, the number of data objects is the same as the number of fields in the configuration information, and the data objects respectively correspond to the fields in the configuration information.
In step S2230, a data table is obtained according to the data object generated by each execution unit.
In an embodiment of the present specification, each data object may be filled in a corresponding column according to a field corresponding to each column, so as to obtain a data table.
In another embodiment of the present specification, the second number of row data objects generated by each execution unit may form a sub data table, and then the sub data tables corresponding to each execution unit may be spliced to obtain a final data table.
In one embodiment of the present specification, the limitation condition of the target field may include the number of unique values, and then, the specified number of unique values may be selected as the data object of the target field within the specified data range of the target field. If the operations to generate the corresponding second number of row data objects are performed by a plurality of execution units, respectively, in the distributed system, the unique values generated by each execution unit may be different, resulting in the number of unique values generated by the distributed system as a whole exceeding a specified number. For example, the number of unique values included in the constraint condition of the target field is 5, the unique values generated by one execution unit are 1, 2, 3, 4 and 5, respectively, and the unique values generated by another execution unit are 3, 4, 5, 6 and 7, respectively, so that the data object of the target field generated by the distributed system as a whole includes 7 different unique values, which exceeds the number of the specified unique values.
In order to avoid that the number of unique values generated by the distributed system exceeds a specified number, before generating a target data object corresponding to a target field in the data table according to the configuration information, the method may further include: and determining a plurality of numerical values corresponding to the target field as first candidate values according to the number of the unique values.
In this embodiment, the number of unique values may be a specified number, and then, the number of first candidate values may also be the same as the specified number, and all the first candidate values are different.
In one example, the first candidate value may be determined by randomly selecting a predetermined number of values as the first candidate value.
In another example, the constraint of the target field may further include a data range, and then the first candidate value may be determined by randomly selecting a specified number of values from the data range as the first candidate value.
On the basis of this embodiment, generating the target data object corresponding to the target field in the data table according to the configuration information may include: and generating a target data object corresponding to the target field according to the first candidate value.
For any one execution unit, the value of each target data object may be selected from the first candidate values.
In one example, generating the target data object corresponding to the target field according to the first candidate value may be: for each target data object, one of the first candidate values is randomly selected as the value of the target data object.
In another example, generating the target data object corresponding to the target field according to the first candidate value may be: the first candidate values are selected in turn as the values for each target data object, respectively.
For example, the first candidate value includes 1, 2, 3, 4, and 5, and the second number corresponding to one execution unit may be 20, then the values of the target data object generated by the execution unit may be 1, 2, 3, 4, 5, 1, 2, 3, 4, and 5, respectively.
By the embodiment, the situation that the number of the values of the target data objects generated by the distributed system exceeds the specified number can be avoided, and the data objects in the finally generated data table are ensured to meet the corresponding limiting conditions.
In one embodiment of the present description, the constraint of the target field may further include a data gradient. The data gradient includes a set proportion of a sum of frequencies of a set number of values that occur most frequently to a total number of target data objects corresponding to the target field. For example, if the data gradient is "i, j", the ratio of the sum of the frequencies of i values with the highest frequency of occurrence to the total number of target data objects corresponding to the target field may be j in the designated data range of the target field.
If the operation of generating the corresponding second number of data objects is executed by the plurality of execution units in the distributed system respectively, the values with the most frequent occurrence in the data objects generated by each execution unit are different, so that the proportion of the sum of the frequencies of the values with the most frequent occurrence generated by the distributed system as a whole to the total number of the target data objects corresponding to the target field is different from the set proportion defined in the data gradient. For example, the gradient of data included in the constraint condition of the target field is "5, 0.8", values generated by one execution unit and having the highest frequency of occurrence are 1, 2, 3, 4, 5, and the ratio of the sum of the frequencies of occurrence of these values to the second number is 0.8; the other execution unit generates the values with the most frequent occurrence, namely 3, 4, 5, 6 and 7, and the ratio of the sum of the frequent occurrence of the values to the second number is 0.8; however, the 5 values of the data objects of the target field generated by the distributed system as a whole may be 2, 3, 4, 5, and 6, and the sum of the frequency of occurrence of the 5 values accounts for 0.7 of the total number of the target data objects corresponding to the target field, which is different from the set ratio of 0.8 defined in the data gradient.
In order to avoid a ratio of a sum of frequencies of values with the highest frequency of occurrence generated by the distributed system to a total number of target data objects corresponding to the target fields, which is different from a set ratio defined in the data gradient, before generating the target data objects corresponding to the target fields in the data table according to the configuration information, the method may further include: and determining a set number of values with the maximum occurrence frequency corresponding to the target field as second candidate values.
In one example, the second candidate value may be determined by randomly selecting a predetermined number of values as the second candidate value.
In another example, the constraint of the target field may further include a data range, and then the second candidate value may be determined by randomly selecting a specified number of values from the data range as the second candidate value.
On the basis of this embodiment, generating the target data object corresponding to the target field in the data table according to the configuration information may include: and generating a target data object of the target field according to the set proportion and the second candidate value.
For any execution unit, the value with the highest occurrence frequency may be selected from the second candidate values, and the ratio between the sum of the occurrence frequencies of all the second candidate values and the second quantity is a set ratio.
By the embodiment, the condition that the proportion of the sum of the frequencies of the values with the most frequent occurrence generated by the distributed system in the total number of the target data objects corresponding to the target field is different from the set proportion defined in the data inclination can be avoided, and the data objects in the finally generated data table are ensured to meet the corresponding limiting conditions.
In one embodiment of the present specification, the constraint of the target field may further include a null rate. Then, generating the target data object corresponding to the target field in the data table according to the configuration information includes: and nulling the target data object corresponding to the target field according to the null value rate.
In one embodiment, when each execution unit generates a data object, the target data object corresponding to the target field may be nulled according to a null rate.
In another embodiment, the target data object corresponding to the target field may be nulled according to a null rate after the data table is obtained according to the data object generated by each execution unit.
In one embodiment of the present specification, the constraint of the target field may further include a data rule. Then, generating the target data object corresponding to the target field in the data table according to the configuration information includes: and generating data conforming to the data rule as a target data object corresponding to the target field in the data table.
For example, in the case that the data rule is an identity card number, the configuration information may include at least one of a gender, an age, and an identity card attribution, so that an identity card number meeting a condition may be generated according to at least one of the gender, the age, and the identity card attribution as a target data object corresponding to the target field.
For another example, when the data rule is a mobile phone number, the configuration information may include an operator and/or a mobile phone attribution, so that a mobile phone number meeting the condition may be generated according to the operator and/or the mobile phone attribution, and used as a target data object corresponding to the target field.
For another example, in the case that the data rule is a bank card number, the configuration information may include a bank name and/or a bank card category, so that a bank card number meeting the condition may be generated according to the bank name and/or the bank card category, and is used as the target data object corresponding to the target field.
In one embodiment of the present specification, the constraint of the target field may further include a data distribution type. Then, generating the target data object corresponding to the target field in the data table according to the configuration information includes: and generating a target data object corresponding to the target field according to the data distribution type, so that the value of the target data object conforms to the data distribution type.
In one embodiment of the present description, the restriction condition of the target field may further include a data range. Then, generating the target data object corresponding to the target field in the data table according to the configuration information includes: and generating a target data object corresponding to the target field according to the data range, so that the value of the target data object is in the data range.
In this embodiment, the value of each target data object may be randomly selected from the data range.
In one embodiment of the present description, the configuration information further includes a specified data type. Then, the method may further comprise: and converting the target data object corresponding to the target field into a specified data type.
In one embodiment of the present description, the method may further comprise: and responding to the operation of viewing the data table, and displaying the data table.
In one example, after the data table is obtained, a view button is provided in the interface, and the user clicks the view button to trigger an operation of viewing the data table. And the electronic equipment responds to the operation of viewing the data table, and displays the data table in an interface for a user to view.
In one embodiment of the present description, the method may further comprise:
determining an application scene to which the data table is applicable; searching an application item matched with the application scene; and inputting the data table to the application project.
In one embodiment of the present specification, the application items matching the application scenario to which the data table is applied may be training items of a machine learning model, test items of a machine learning model, or test items of a spark program.
In the training items of the machine learning model, the machine learning model may be trained using the data table as a training sample.
In the test items of the machine learning model, the performance of the machine learning model may be tested by using the data table as a test sample.
In the testing project of the spark program, the running performance of the spark program can be checked and optimized, and whether the spark program has the running efficiency problem caused by data inclination or not can be tested, so that the targeted improvement of developers can be facilitated.
In one embodiment of the present description, the method may further comprise:
acquiring a preset machine learning model; the performance of the machine learning model is tested according to the data sheet.
For example, the data table may be input to a test item of the machine learning model, and the performance of the machine learning model may be tested using the data table as a test sample.
< apparatus embodiment >
In this embodiment, a device 6000 for generating a data table is provided, as shown in fig. 6, including an information obtaining module 6100 and a data table generating module 6200. The information acquisition module is used for acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field; the data table generating module is used for generating a data table according to the configuration information, wherein each data object in the data table meets the corresponding limiting condition.
In an embodiment of the present specification, the information obtaining module 6100 may further be configured to:
a configuration interface for providing configuration information;
and acquiring configuration information through a configuration interface.
In an embodiment of the present specification, the information obtaining module 6100 may further be configured to:
providing an entry for uploading a configuration file;
acquiring a configuration file uploaded by a user through an entrance;
and analyzing the configuration file to obtain configuration information.
In an embodiment of the present specification, the data table generating device 6000 may further include:
a module for detecting whether the configuration file is legal;
a module for analyzing the configuration file to obtain configuration information under the condition that the configuration file is legal;
and the module is used for generating an error log under the condition that the configuration file is illegal.
In one embodiment of the present specification, the configuration file is in any one of xlsx, csv, tsv, and parquet.
In an embodiment of the present specification, the data table generating device 6000 may further include:
a module for providing a canvas area in an interface for creating a data processing flow diagram;
the information acquisition module 6100 may also be used to:
responding to the operation of selecting the target data source by the user, and displaying the target data source selected by the user in the canvas area; the target data source comprises configuration information;
the data table generation module 6200 may also be configured to:
responding to the operation of selecting a target operator by a user, and displaying the target operator selected by the user in a canvas area; the target operator is used for generating a data table according to input information;
connecting a target data source and a target operator in the layout area to construct a data processing flow chart;
and operating the data processing flow chart to obtain a data table.
In one embodiment of the present specification, the configuration information further includes a total number of rows of the data table and a first number of execution units to generate the data table in parallel,
generating the data table according to the configuration information includes:
determining the number of lines of the data object generated by each execution unit as a second number according to the total number of lines and the first number of execution units which generate the data table in parallel;
controlling each execution unit to generate a corresponding second quantity of row data objects;
and obtaining a data table according to the data object generated by each execution unit.
In one embodiment of the present description, the limitation includes at least one of: data type, data distribution type, data range, number of unique values, null rate, data gradient and data regularity.
In one embodiment of the present specification, the constraint of the target field includes the number of unique values;
the data table generating means 6000 may further include:
a module for determining a plurality of values corresponding to the target field as first candidate values according to the number of the unique values;
generating the target data object corresponding to the target field in the data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the first candidate value.
In one embodiment of the present specification, the constraint of the target field further comprises a data gradient; the data inclination comprises a set proportion of the sum of the frequencies of the set number of values with the most frequent occurrence to the total number of the target data objects corresponding to the target field;
the data table generating means 6000 may further include:
a module for determining a set number of values corresponding to the target field with the most frequency of occurrence as second candidate values;
generating the target data object corresponding to the target field in the data table according to the configuration information comprises: and generating a target data object of the target field according to the set proportion and the second candidate value.
In one embodiment of the present specification, the constraints of the target field further include a null rate,
generating the target data object corresponding to the target field in the data table according to the configuration information comprises: and nulling the target data object corresponding to the target field according to the null value rate.
In one embodiment of the present specification, the constraint of the target field further includes a data rule;
generating the target data object corresponding to the target field in the data table according to the configuration information comprises:
and generating data conforming to the data rule as a target data object corresponding to the target field in the data table.
In one embodiment of the present specification, the constraint of the target field further includes a data distribution type;
generating the target data object corresponding to the target field in the data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data distribution type, so that the value of the target data object conforms to the data distribution type.
In one embodiment of the present specification, the constraint of the target field further comprises a data range;
generating the target data object corresponding to the target field in the data table according to the configuration information comprises: and generating a target data object corresponding to the target field according to the data range, so that the value of the target data object is in the data range.
In one embodiment of the present description, the configuration information further includes a specified data type;
the data table generating means 6000 may further include:
and the module is used for converting the target data object corresponding to the target field into the specified data type.
In an embodiment of the present specification, the data table generating device 6000 may further include:
a module for presenting the data table in response to an operation to view the data table.
In an embodiment of the present specification, the data table generating device 6000 may further include:
a module for determining an application scenario to which the data table applies;
a module for searching an application item matched with the application scene;
a module for inputting a data table to an application project.
In an embodiment of the present specification, the data table generating device 6000 may further include:
a module for obtaining a preset machine learning model;
means for testing the performance of the machine learning model according to the data sheet.
The generating means 6000 of the data table may be implemented in various ways, as will be clear to a person skilled in the art. The data table generating means 6000 may be implemented, for example, by instructing a configuration processor. For example, the instructions may be stored in a ROM and read from the ROM into a programmable device when the apparatus is started up to implement the data table generating means 6000. For example, the data table generating means 6000 can be cured into a dedicated device (for example an ASIC). The data table generating means 6000 can be divided into separate units or they can be combined together. The data table generating means 6000 may be implemented by one of the various implementations described above, or may be implemented by a combination of two or more of the various implementations described above.
In this embodiment, the data table generating device 6000 may have various implementation forms, for example, the data table generating device 6000 may be any functional module running in a software product or an application program providing the data table generating service, or a peripheral insert, a plug-in, a patch, etc. of the software product or the application program, and may also be the software product or the application program itself.
< System embodiment >
In this embodiment, as shown in FIG. 7, a system 7000 of at least one computing device 7100 and at least one storage device 7200 is also provided. The at least one memory device 7200 is configured to store executable instructions; the instructions are for controlling at least one computing device 7100 to perform a method of generating a data table according to any embodiment of the present description.
In this embodiment, the system 7000 may be a device such as a mobile phone, a tablet computer, a palmtop computer, a desktop computer, a notebook computer, a workstation, a game console, or may be a distributed system formed by a plurality of devices.
< computer-readable storage Medium >
In the present embodiment, there is also provided a computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the method for generating a data table according to any embodiment of the present specification.
The present description may be an apparatus, method, and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the specification.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present specification may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions 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). In some embodiments, an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), can execute computer-readable program instructions to implement various aspects of the present description by utilizing state information of the computer-readable program instructions to personalize the electronic circuit.
Aspects of the present description are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the description. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
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 description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
The foregoing description of the embodiments of the present specification has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present description is defined by the appended claims.
Claims (10)
1. A method for generating a data table comprises the following steps:
acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field;
and generating a data table according to the configuration information, wherein each data object in the data table meets a corresponding limiting condition.
2. The method of claim 1, the obtaining configuration information comprising:
a configuration interface for providing configuration information;
and acquiring the configuration information through the configuration interface.
3. The method of claim 1, the obtaining configuration information comprising:
providing an entry for uploading a configuration file;
acquiring a configuration file uploaded by a user through the entrance;
and analyzing the configuration file to obtain the configuration information.
4. The method of claim 3, further comprising:
detecting whether the configuration file is legal or not;
under the condition that the configuration file is legal, executing the step of analyzing the configuration file to obtain the configuration information;
and generating an error log under the condition that the configuration file is illegal.
5. The method of claim 3, wherein the configuration file has a format selected from any one of xlsx, csv, tsv and parquet.
6. The method of claim 1, further comprising:
providing a canvas area for creating a data processing flow diagram in an interface;
the acquiring the configuration information includes:
in response to an operation of selecting a target data source by a user, displaying the target data source selected by the user in the canvas area; wherein the target data source comprises the configuration information;
the generating a data table according to the configuration information comprises:
responding to the operation of selecting a target operator by a user, and displaying the target operator selected by the user in the canvas area; the target operator is used for generating a data table according to input information;
connecting the target data source and the target operator in the layout area to construct a data processing flow chart;
and operating the data processing flow chart to obtain the data table.
7. The method of claim 1, the configuration information further comprising a total number of rows of a data table and a first number of execution units to generate the data table in parallel,
the generating a data table according to the configuration information comprises:
determining the number of rows of the data object generated by each execution unit as a second number according to the total number of rows and the first number of the execution units which generate the data table in parallel;
controlling each execution unit to generate a corresponding second quantity of row data objects;
and obtaining the data table according to the data object generated by each execution unit.
8. An apparatus for generating a data table, comprising:
the information acquisition module is used for acquiring configuration information, wherein the configuration information comprises information used for describing fields represented by each line of data objects in a data table to be generated and limiting conditions of the data objects corresponding to each field;
and the data table generating module is used for generating a data table according to the configuration information, wherein each data object in the data table meets the corresponding limiting condition.
9. A system comprising at least one computing device and at least one storage device, wherein the at least one storage device is to store instructions for controlling the at least one computing device to perform the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010566293.5A CN111814445A (en) | 2020-06-19 | 2020-06-19 | Data table generation method, device and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010566293.5A CN111814445A (en) | 2020-06-19 | 2020-06-19 | Data table generation method, device and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111814445A true CN111814445A (en) | 2020-10-23 |
Family
ID=72846318
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010566293.5A Pending CN111814445A (en) | 2020-06-19 | 2020-06-19 | Data table generation method, device and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111814445A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110145300A1 (en) * | 2009-12-10 | 2011-06-16 | Toshiba Tec Kabushiki Kaisha | Database system, server apparatus, terminal apparatus, and database updating method |
| CN107015957A (en) * | 2017-03-16 | 2017-08-04 | 深圳市沃易科技有限公司 | User's list generation method and device |
| CN108008942A (en) * | 2017-11-16 | 2018-05-08 | 第四范式（北京）技术有限公司 | The method and system handled data record |
| CN109815252A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Test data generating method, device, computer equipment and readable storage medium storing program for executing |
| CN110502519A (en) * | 2019-08-26 | 2019-11-26 | 北京启迪区块链科技发展有限公司 | A kind of method, apparatus of data aggregate, equipment and storage medium |
| CN110928879A (en) * | 2019-11-20 | 2020-03-27 | 贵州电网有限责任公司电力科学研究院 | Wide table generation method and device |
-
2020
- 2020-06-19 CN CN202010566293.5A patent/CN111814445A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110145300A1 (en) * | 2009-12-10 | 2011-06-16 | Toshiba Tec Kabushiki Kaisha | Database system, server apparatus, terminal apparatus, and database updating method |
| CN107015957A (en) * | 2017-03-16 | 2017-08-04 | 深圳市沃易科技有限公司 | User's list generation method and device |
| CN108008942A (en) * | 2017-11-16 | 2018-05-08 | 第四范式（北京）技术有限公司 | The method and system handled data record |
| CN109815252A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Test data generating method, device, computer equipment and readable storage medium storing program for executing |
| CN110502519A (en) * | 2019-08-26 | 2019-11-26 | 北京启迪区块链科技发展有限公司 | A kind of method, apparatus of data aggregate, equipment and storage medium |
| CN110928879A (en) * | 2019-11-20 | 2020-03-27 | 贵州电网有限责任公司电力科学研究院 | Wide table generation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107103036B (en) | Method and equipment for acquiring application downloading probability and programmable equipment | |
| CN104350475B (en) | Visual code is checked | |
| CN111190718A (en) | Method, device and system for realizing task scheduling | |
| US10175977B2 (en) | User profile based code review | |
| CN109388376B (en) | Software development risk assessment method, device, equipment and readable storage medium | |
| CN111045653B (en) | System generation method and device, computer readable medium and electronic equipment | |
| CN108459964B (en) | Test case selection method, device, equipment and computer readable storage medium | |
| US20170371504A1 (en) | Method and system for visual requirements and component reuse driven rapid application composition | |
| CN114154461A (en) | Text data processing method, device and system | |
| CN111160567A (en) | Method, device and system for realizing automatic hyper-parameter selection | |
| CN111506775B (en) | Label processing method, device, electronic equipment and readable storage medium | |
| US11341197B2 (en) | Recommendation system based on adjustable virtual indicium | |
| CN110442499B (en) | Method and device for testing and improving page response performance and terminal equipment | |
| CN108664610B (en) | Method and apparatus for processing data | |
| CN111814445A (en) | Data table generation method, device and system | |
| CN110673908A (en) | Interface generation method, device, medium and electronic equipment | |
| CN115994807A (en) | Material recommendation method, device and system | |
| CN110554892A (en) | Information acquisition method and device | |
| CN115965089A (en) | Machine learning method for interface feature display across time zones or geographic regions | |
| US9578083B1 (en) | Dynamically designing shared content | |
| CN111078989B (en) | Application program recommendation method and device and electronic equipment | |
| US10970533B2 (en) | Methods and systems for finding elements in optical character recognition documents | |
| CN112905424A (en) | Monitoring software customization generation method, system, equipment and storage medium | |
| CN112749235A (en) | Method and device for analyzing classification result and electronic equipment | |
| CN111339432A (en) | Recommendation method and device of electronic object and electronic equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |