CN113139102B - Data processing method, device, nonvolatile storage medium and processor - Google Patents
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Abstract
The invention discloses a data processing method, a data processing device, a nonvolatile storage medium and a processor. Wherein the method comprises the following steps: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the graph type matched with the target data according to the multiple similarities and the graph database. The method and the device solve the technical problem that the type of the chart matched with the target data is difficult to determine.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data processing method, apparatus, nonvolatile storage medium, and processor.
Background
The data visualization technology can help people find out needed information more quickly, and can help people grasp the changes of different data sets more quickly. However, while the visualization technology is greatly developed, in many scenes, due to low expertise and insufficient experience of a data user in the aspect of data visualization, the data user often does not know what data visualization form is adopted to obtain better data visualization effect, and more visual data analysis results are obtained.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a data processing method, a data processing device, a nonvolatile storage medium and a processor, which are used for at least solving the technical problem that the type of a chart matched with target data is difficult to determine.
According to an aspect of an embodiment of the present invention, there is provided a data processing method including: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the graph type matched with the target data according to the similarity and the graph database.
Optionally, determining a chart type matching the target data according to the plurality of similarities and the chart database includes: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity with the target data in the plurality of first data; and determining the graph type matched with the target data according to the third data and the graph type of the third data.
Optionally, determining the chart type matching the target data according to the third data and the chart type of the third data includes: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the data corresponding to the third chart type in the third data is the most.
Optionally, in the case that the number of data corresponding to at least two third chart types is the largest, determining that the third chart type included in the chart types corresponding to the third data is the chart type matched with the target data includes: determining fourth data of which the similarity with the target data is ranked in a (k+1) -th bit among the plurality of first data; and determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as the chart type matched with the target data, wherein the data corresponding to the fourth chart type in the third data and the fourth data is the most.
Optionally, determining a plurality of similarities obtained by comparing the target data with the plurality of first data respectively includes: obtaining fifth data, wherein the fifth data is any one data in the first data; acquiring the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
According to another aspect of the embodiment of the present invention, there is also provided a data processing apparatus including: the acquisition module is used for acquiring target data; the first determining module is used for determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database also comprises at least one first chart type corresponding to the plurality of first data; and the second determining module is used for determining the graph type matched with the target data according to the plurality of similarities and the graph database.
Optionally, the second determining module includes: a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit is configured to determine a number k of second data corresponding to a second chart type among the plurality of first data, where the first chart type includes the second chart type, and the number k of the second data is smaller than a number of data corresponding to any other chart type, and the other chart types are chart types other than the second chart type in the first chart type; the second determining unit is configured to determine k third data with the largest similarity with the target data from the plurality of first data; the third determining unit is configured to determine a chart type matching the target data according to the third data and the chart type of the third data.
Optionally, the first determining module includes: the device comprises a first acquisition unit, a second acquisition unit and a third determination unit, wherein the first acquisition unit is used for acquiring fifth data, and the fifth data is any one of the first data; the second obtaining unit is configured to obtain a header of the target data and a header of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; the third determining unit is configured to determine a similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
According to still another aspect of the embodiments of the present invention, there is further provided a nonvolatile storage medium, where the nonvolatile storage medium includes a stored program, and when the program runs, the device in which the nonvolatile storage medium is controlled to execute any one of the data processing methods described above.
According to still another aspect of the embodiments of the present invention, there is further provided a processor, where the processor is configured to execute a program, where the program executes any one of the data processing methods described above.
In the embodiment of the invention, the method of processing the target data according to the chart database is adopted, the multiple similarities obtained by comparing the target data with the multiple first data stored in the chart database respectively are determined, and the chart type matched with the target data is determined according to the multiple similarities and the chart database, so that the purpose of determining the chart type matched with the target data is achieved, the technical effect of simply and accurately determining the chart type matched with the target data is realized, and the technical problem that the chart type matched with the target data is difficult to determine is solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present invention;
fig. 2 is a block diagram of a data processing apparatus according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided a data processing method, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
Fig. 1 is a flow chart of a data processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
Step S102, acquiring target data. The target data may be a set of data to be processed, and acquiring the set of data to be processed may include acquiring a data type, a data source, a title of the data, a specific data value, and the like of the set of data.
Step S104, determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data.
As an alternative implementation manner, the chart database may be preset, and the chart database stores a plurality of first data and a first chart type corresponding to each first data, where each first data may be a set of data, and the set of first data may include a data type, a data source, a title of the data, a specific data value, and the like of the set of data. The method comprises the steps of traversing each first data in a chart database, comparing target data with each first data, and determining the similarity of the target data with a plurality of first data. Each group of first data in the chart database and the first chart type corresponding to the first data can be a pair of combinations of verified data and charts with good corresponding effects, the first chart type can well reflect the characteristics and the effective information of the first data after being checked or marked in advance.
Step S106, determining the graph type matched with the target data according to the multiple similarities and the graph database. In this step, the data with high similarity to the target data can be obtained by judging the similarity between the plurality of first data in the graph database and the target data, and the graph type matched with the target data is determined according to the data with high similarity and the graph type of the data as reference basis. The chart type matched with the target data may be a chart type suitable for showing characteristics or information of the target data, and the chart type may be a line chart, a graph, a bar chart, a pie chart and the like, which are not limited herein.
Through the steps, the method for processing the target data according to the chart database is adopted, the multiple similarities obtained by comparing the target data with the multiple first data stored in the chart database respectively are determined, and the chart type matched with the target data is determined according to the multiple similarities and the chart database, so that the purpose of determining the chart type matched with the target data is achieved, the technical effect of simply and accurately determining the chart type matched with the target data is achieved, and the technical problem that the chart type matched with the target data is difficult to determine is solved.
As an alternative embodiment, the plurality of similarities obtained by comparing the target data with the plurality of first data, respectively, may be obtained by: obtaining fifth data, wherein the fifth data is any one data in the first data; acquiring a title of target data and a title of fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data. In this embodiment, the target data may be compared with each first data in the graph database in sequence to obtain a plurality of similarities, and the fifth data is any one of the first data.
As an alternative implementation manner, the similarity of two data may be determined according to a plurality of dimension parameters such as a title of the data, a data source of the data, a type of the data, and the like, wherein the data source of the data may include a source of collected data, and the data is collected time data, space data, user identification data, and the like; the type of data may include a structure of data, for example, the type of data may include a discrete type, a continuous type, a date type, and the like.
Table 1 is an information table of data provided according to the present alternative embodiment, and as shown in table 1, parameters of each of 4 different data are shown, where a title of data 1 is a "click rate change curve", an X-axis data source thereof is time, an X-axis data type thereof is date type, a Y-axis data source thereof is click rate, a Y-axis data type thereof is numerical value, and an icon type corresponding to the data 1 is a line graph. The data parameters of data 2, data 3 and data 4 are shown in table 1, and will not be described in detail.
TABLE 1
As an alternative embodiment, after acquiring the respective titles, data sources, and data types of the target data and the fifth data, the similarity of the two data may be determined by:
in step S11, the titles of the two data are compared, and in the case that at least 3 identical Chinese characters exist in the two titles, 1 score is counted, and each additional Chinese character is added with 1 score.
In step S12, the data sources of the independent variables of the two data are compared, and if the data sources are the same, the score is 1.
Step S13, comparing the data types of the independent variables of the two data, and if the data types are the same, recording 1 point.
In step S14, the data sources of the dependent variables of the two data are compared, and if the data sources are the same, 1 point is recorded.
In step S15, the data types of the dependent variables of the two data are compared, and if the data types are the same, a score of 1 is recorded.
And step S16, adding the total scores of the steps S11 to S15 to obtain the similarity of the target data and the fifth data, wherein the larger the added value is, the larger the similarity is.
As an alternative embodiment, determining the chart type matching the target data according to the plurality of similarities and the chart database may be as follows: determining the quantity k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the quantity k of the second data is smaller than the quantity of data corresponding to any one other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity with the target data in the first data; and determining the graph type matched with the target data according to the third data and the graph type of the third data.
In this alternative embodiment, the second chart type is a specific chart type in the first chart type, and the determining the second chart type may be as follows: counting the number of first data corresponding to all chart types respectively in the first data, for example, 2 data corresponding to the line graph, 3 data corresponding to the histogram and 5 data corresponding to the pie graph in 10 first data; determining a second chart type, wherein the data corresponding to the second chart type is second data, the number of the second data is the smallest one of the numbers of the data corresponding to all types of chart types, and the smallest number of the data corresponding to the chart type is 2 in the example, so that the second chart type is a line graph, and k is equal to 2; then ordering the first data according to the size of the similarity, determining k first data with the largest similarity as third data, and in the example, determining 2 data with the largest similarity as third data; and then determining the graph type matched with the target data according to the k third data and the graph types corresponding to the k third data, for example, if all the third data with the maximum similarity are the line graphs, determining the graph type matched with the target data as the line graphs.
As an alternative embodiment, determining the chart type matching the target data according to the third data and the chart type of the third data may be performed by: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the data corresponding to the third chart type in the third data is the most.
As an optional implementation manner, the third data may correspond to a plurality of chart types, and by determining the number of data corresponding to each of the plurality of chart types, the chart type with the largest number of data corresponding to the third data may be determined, that is, the third chart type, that is, the chart type matched with the target data.
As an alternative embodiment, in the case where the number of data corresponding to at least two third chart types is the largest, it may be determined that the third chart type included in the chart type corresponding to the third data is the chart type matching the target data by: determining fourth data of which the similarity with the target data is ranked in the (k+1) -th bit among the plurality of first data; and determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as the chart type matched with the target data, wherein the data corresponding to the fourth chart type in the third data and the fourth data is the most.
The present alternative embodiment may unambiguously determine the fourth chart type matching the target data by introducing the fourth data having the similarity ranked in the k+1-th bit when a plurality of third chart types are included in the third data, wherein the fourth chart type may be the most number and only one of the corresponding data in the third data and the fourth data.
As an alternative embodiment, the chart type of the target data match may be determined as follows.
Step S21, obtaining the data title of the target data A, the data source and the data type of the data independent variable, the data source and the data type of the dependent variable, the value range and other information.
In step S22, a plurality of data B in the database is acquired, where the data B may be placed in the same logical location (e.g. on the same cloud host) as the device running the embodiment, or may be placed in different logical locations (e.g. one in public cloud and the other in private cloud).
Step S23, calculating the number of the data B, and marking the number as n;
Step S24, calculating the ratio of different graph types in the data B, for example, 20% of the line graph, 30% of the graph and 50% of the histogram;
Step S25, comparing the target data A with the plurality of data B respectively to obtain similarity S AB between the target data A and the plurality of data B respectively, and obtaining S AB1,SAB2,SAB3,SAB4 and so on until S ABn;
Step S26, determining the number k of data corresponding to the graph type with the smallest duty ratio in step S24, for example, in this embodiment, k=20%;
step S27, sorting n S AB according to the size, taking k S AB with the largest similarity, and recording data B corresponding to each S AB;
Step S28, determining the graph types corresponding to the k data B in the step S27, and determining the graph type with the largest corresponding data as the graph type matched with the target data A according to the data quantity corresponding to the k data B in the graph types;
Step S29, if more than one graph type with the largest number of corresponding data B exists in the step S28, the data which is arranged in the k+1st bit when the similarity magnitude sequence is acquired is added to the k data B, and the step S28 is repeated until the graph type with the largest number of unique corresponding data is determined, and the graph type is determined as the graph type of the matching target data A.
Example 2
According to an embodiment of the present invention, there is also provided a data processing apparatus for implementing the above data processing method, and fig. 2 is a block diagram of a data processing apparatus according to an embodiment of the present invention, as shown in fig. 2, the data processing apparatus includes: the acquisition module 22, the first determination module 24 and the second determination module 26 are described below with respect to the data processing apparatus.
An acquisition module 22 for acquiring target data;
The first determining module 24 is connected to the acquiring module 22, and is configured to determine a plurality of similarities obtained by comparing the target data with a plurality of first data, where the plurality of first data is stored in a graph database, and the graph database further includes at least one first graph type corresponding to the plurality of first data;
The second determining module 26 is connected to the first determining module 24, and is configured to determine a chart type matching the target data according to the similarities and the chart database.
Here, the above-mentioned obtaining module 22, the first determining module 24 and the second determining module 26 correspond to steps S102 to S106 in embodiment 1, and the three modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to those disclosed in embodiment 1 above.
As an alternative embodiment, the second determining module 26 may include a first determining unit 261, a second determining unit 262 and a third determining unit 263, where the first determining unit 261 is configured to determine, of the plurality of first data, a number k of second data corresponding to a second chart type, where the first chart type includes the second chart type, the number k of the second data is smaller than a number of data corresponding to any other chart type, and the other chart types are chart types other than the second chart type in the first chart type; a second determining unit 262 for determining k third data having the greatest similarity with the target data among the plurality of first data; a third determining unit 263 for determining a graph type matching the target data according to the third data and the graph type of the third data.
As an alternative embodiment, the first determining module 24 may include a first acquiring unit 241, a second acquiring unit 242 and a third determining unit 243, where the first acquiring unit 241 is configured to acquire fifth data, and the fifth data is any one of the first data; a second acquisition unit 242 configured to acquire a title of the target data and a title of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; the third determining unit 243 is configured to determine the similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
Example 3
Embodiments of the present invention may provide a computer device, optionally in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be used to store software programs and modules, such as program instructions/modules corresponding to the data processing methods and apparatuses in the embodiments of the present invention, and the processor executes the software programs and modules stored in the memory, thereby executing various functional applications and data processing, that is, implementing the data processing methods described above. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located relative to the processor, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may call the information and the application program stored in the memory through the transmission device to perform the following steps: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the graph type matched with the target data according to the multiple similarities and the graph database.
Optionally, the above processor may further execute program code for: determining a chart type matching the target data according to the plurality of similarities and the chart database, wherein the determining comprises the following steps: determining the quantity k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the quantity k of the second data is smaller than the quantity of data corresponding to any one other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity with the target data in the first data; and determining the graph type matched with the target data according to the third data and the graph type of the third data.
Optionally, the above processor may further execute program code for: determining a chart type matching the target data according to the third data and the chart type of the third data, including: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the data corresponding to the third chart type in the third data is the most.
Optionally, the above processor may further execute program code for: and determining that the third chart type included in the chart type corresponding to the third data is the chart type matched with the target data under the condition that the quantity of the data corresponding to the at least two third chart types is the largest, wherein the method comprises the following steps: determining fourth data of which the similarity with the target data is ranked in the (k+1) -th bit among the plurality of first data; and determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as the chart type matched with the target data, wherein the data corresponding to the fourth chart type in the third data and the fourth data is the most.
Optionally, the above processor may further execute program code for: determining a plurality of similarities obtained by comparing the target data with the plurality of first data respectively, wherein the method comprises the following steps: obtaining fifth data, wherein the fifth data is any one data in the first data; acquiring a title of target data and a title of fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program for instructing a terminal device to execute in association with hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: flash disk, read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
Example 4
Embodiments of the present invention also provide a nonvolatile storage medium. Alternatively, in the present embodiment, the above-described nonvolatile storage medium may be used to store program codes executed by the data processing method provided in the above-described embodiment 1.
Alternatively, in this embodiment, the above-mentioned nonvolatile storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: acquiring target data; determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data; and determining the graph type matched with the target data according to the multiple similarities and the graph database.
Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: determining a chart type matching the target data according to the plurality of similarities and the chart database, wherein the determining comprises the following steps: determining the quantity k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the quantity k of the second data is smaller than the quantity of data corresponding to any one other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity with the target data in the first data; and determining the graph type matched with the target data according to the third data and the graph type of the third data.
Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: determining a chart type matching the target data according to the third data and the chart type of the third data, including: and determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the data corresponding to the third chart type in the third data is the most.
Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: and determining that the third chart type included in the chart type corresponding to the third data is the chart type matched with the target data under the condition that the quantity of the data corresponding to the at least two third chart types is the largest, wherein the method comprises the following steps: determining fourth data of which the similarity with the target data is ranked in the (k+1) -th bit among the plurality of first data; and determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as the chart type matched with the target data, wherein the data corresponding to the fourth chart type in the third data and the fourth data is the most.
Optionally, in the present embodiment, the non-volatile storage medium is arranged to store program code for performing the steps of: determining a plurality of similarities obtained by comparing the target data with the plurality of first data respectively, wherein the method comprises the following steps: obtaining fifth data, wherein the fifth data is any one data in the first data; acquiring a title of target data and a title of fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data; and determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (8)
1. A method of data processing, comprising:
acquiring target data;
Determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database further comprises at least one first chart type corresponding to the plurality of first data;
Determining a chart type matched with the target data according to the plurality of similarities and the chart database;
Determining a chart type matching the target data according to the plurality of similarities and the chart database, wherein the determining comprises the following steps: determining the number k of second data corresponding to a second chart type in the plurality of first data, wherein the first chart type comprises the second chart type, the number k of the second data is smaller than the number of data corresponding to any other chart type, and the other chart types are chart types except the second chart type in the first chart type; determining k third data with the maximum similarity with the target data in the plurality of first data; and determining the graph type matched with the target data according to the third data and the graph type of the third data.
2. The method of claim 1, wherein determining a chart type matching the target data based on the third data and the chart type of the third data comprises:
And determining a third chart type included in the chart type corresponding to the third data as the chart type matched with the target data, wherein the data corresponding to the third chart type in the third data is the most.
3. The method according to claim 2, wherein, in the case where the number of data corresponding to at least two third chart types is the largest, determining that a third chart type included in the chart types corresponding to the third data is a chart type matching the target data includes:
Determining fourth data of which the similarity with the target data is ranked in a (k+1) -th bit among the plurality of first data;
And determining a fourth chart type included in the chart type corresponding to the third data and the chart type corresponding to the fourth data as the chart type matched with the target data, wherein the data corresponding to the fourth chart type in the third data and the fourth data is the most.
4. The method of claim 1, wherein determining a plurality of similarities for the target data compared with the plurality of first data, respectively, comprises:
obtaining fifth data, wherein the fifth data is any one data in the first data;
Acquiring the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data;
And determining the similarity of the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
5. A data processing apparatus, comprising:
The acquisition module is used for acquiring target data;
The first determining module is used for determining a plurality of similarities obtained by comparing the target data with a plurality of first data respectively, wherein the plurality of first data are stored in a chart database, and the chart database also comprises at least one first chart type corresponding to the plurality of first data;
The second determining module is used for determining the graph type matched with the target data according to the plurality of similarities and the graph database;
The second determining module includes: a first determining unit, a second determining unit and a third determining unit, wherein the first determining unit is configured to determine a number k of second data corresponding to a second chart type among the plurality of first data, where the first chart type includes the second chart type, and the number k of the second data is smaller than a number of data corresponding to any other chart type, and the other chart types are chart types other than the second chart type in the first chart type; the second determining unit is configured to determine k third data with the largest similarity with the target data from the plurality of first data; the third determining unit is configured to determine a chart type matching the target data according to the third data and the chart type of the third data.
6. The apparatus of claim 5, wherein the first determining module comprises: a first acquisition unit, a second acquisition unit and a third determination unit, wherein,
The first acquisition unit is configured to acquire fifth data, where the fifth data is any one of the first data;
The second obtaining unit is configured to obtain a header of the target data and a header of the fifth data, a data source of the target data and a data source of the fifth data, and a data type of the target data and a data type of the fifth data;
The third determining unit is configured to determine a similarity between the target data and the fifth data according to the title of the target data and the title of the fifth data, the data source of the target data and the data source of the fifth data, and the data type of the target data and the data type of the fifth data.
7. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the data processing method according to any one of claims 1 to 4.
8. A processor for running a program, wherein the program when run performs the data processing method of any one of claims 1 to 4.
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| CN107169119A (en) * | 2017-05-26 | 2017-09-15 | 九次方大数据信息集团有限公司 | The automation visualization rendering method and system recognized based on data structure |
| CN112015774A (en) * | 2020-09-25 | 2020-12-01 | 北京百度网讯科技有限公司 | Chart recommendation method and device, electronic equipment and storage medium |
| CN112306312A (en) * | 2019-07-29 | 2021-02-02 | 阿里巴巴集团控股有限公司 | Data processing method, data processing system, information creating method and device |
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| CN107169119A (en) * | 2017-05-26 | 2017-09-15 | 九次方大数据信息集团有限公司 | The automation visualization rendering method and system recognized based on data structure |
| CN112306312A (en) * | 2019-07-29 | 2021-02-02 | 阿里巴巴集团控股有限公司 | Data processing method, data processing system, information creating method and device |
| CN112015774A (en) * | 2020-09-25 | 2020-12-01 | 北京百度网讯科技有限公司 | Chart recommendation method and device, electronic equipment and storage medium |
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