CN110909017B - Data analysis method and system - Google Patents
Data analysis method and system Download PDFInfo
- Publication number
- CN110909017B CN110909017B CN201911095118.6A CN201911095118A CN110909017B CN 110909017 B CN110909017 B CN 110909017B CN 201911095118 A CN201911095118 A CN 201911095118A CN 110909017 B CN110909017 B CN 110909017B
- Authority
- CN
- China
- Prior art keywords
- information
- analysis
- field
- chart
- calculation
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
- G06F16/2448—Query languages for particular applications; for extensibility, e.g. user defined types
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
- G06F16/244—Grouping and aggregation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
Abstract
The application relates to a data analysis method and system, the method comprising: acquiring an analysis chart and analyzing the analysis chart; assembling the analyzed information into an execution script, and sending the execution script to a calculation engine so that the calculation engine performs data analysis according to the execution script; and receiving a calculation result returned by the calculation engine, and carrying out visualization processing on the calculation result according to the analysis chart. The scheme of the application can be realized based on the browser without using a professional tool, so that a user does not need to master the professional technology; the data analysis content required by free construction can be required according to the requirement, and the data analysis result can be obtained in real time, so that the requirement of convenient and rapid analysis is met.
Description
Technical Field
The application relates to the technical field of business intelligence, in particular to a data analysis method and device.
Background
In the field of traditional BI (Business Intelligence ), related reports are generally built by technicians through professional BI software, the building period is long, the overall consumption of building and adjustment is large, the feedback period of a building result is long, and the first-line requirements cannot be responded quickly.
With the development of the business, the breadth and depth of the business are both increased; based on the corresponding service scene, the required content for data analysis is also increasing; at the same time, there are increasing regulatory requirements for already established data analysis content. The requirements for data analysis are more and more flexible, the requirements for construction efficiency are higher and higher, and how to rapidly and effectively meet the requirements for mass data analysis at a business side becomes a difficulty of enterprises.
In the related art, the efficiency of building the data analysis report by the traditional BI technology is low, the requirements on the professional technology are high, and the service requirements cannot be responded quickly. The data analysis product based on the Web browser is generally weak in support of data quantity, simple in function and incapable of analyzing large data quantity.
Disclosure of Invention
To overcome at least some of the problems in the related art, the present application provides a data analysis method and system.
According to a first aspect of embodiments of the present application, there is provided a data analysis method, including:
acquiring an analysis chart and analyzing the analysis chart;
assembling the analyzed information into an execution script, and sending the execution script to a calculation engine so that the calculation engine performs data analysis according to the execution script;
and receiving a calculation result returned by the calculation engine, and carrying out visualization processing on the calculation result according to the analysis chart.
Further, the parsing the analysis chart includes:
and analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart.
Further, the field information includes: field name, field length, field type;
the dimension information is the dimension which needs to be analyzed and associated in the data analysis process;
the index information is a calculation mode for calculating and processing the data line in the data analysis process.
Further, the assembling the parsed information into the execution script includes:
carrying out semantic analysis and splitting on the calculation result;
assembling basic field information according to the field information;
assembling any multi-level aggregation according to the dimension information;
and assembling various operation contents based on arbitrary multi-level aggregation according to the index information.
Further, the step of performing visualization processing on the calculation result according to the analysis chart includes:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
According to a second aspect of embodiments of the present application, there is provided a data analysis system comprising:
the chart analysis module is used for acquiring an analysis chart and analyzing the analysis chart;
the script assembly module is used for assembling the analyzed information into an execution script and sending the execution script to the calculation engine;
the calculation engine is used for carrying out data analysis according to the execution script;
and the result analysis module is used for receiving the calculation result returned by the calculation engine and carrying out visualization processing on the calculation result according to the analysis chart.
Further, the chart analysis module analyzes the analysis chart, which specifically includes:
and analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart.
Further, the field information includes: field name, field length, field type;
the dimension information is the dimension which needs to be analyzed and associated in the data analysis process;
the index information is a calculation mode for calculating and processing the data line in the data analysis process.
Further, the script assembling module assembles the parsed information into an execution script, which specifically includes:
carrying out semantic analysis and splitting on the calculation result;
assembling basic field information according to the field information;
assembling any multi-level aggregation according to the dimension information;
and assembling various operation contents based on arbitrary multi-level aggregation according to the index information.
Further, the result analysis module performs visualization processing on the calculation result according to the analysis chart, and specifically includes:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
the scheme of the application can be realized based on the browser without using a professional tool, so that a user does not need to master the professional technology; the data analysis content required by free construction can be required according to the requirement, and the data analysis result can be obtained in real time, so that the requirement of convenient and rapid analysis is met.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart illustrating a method of data analysis according to an exemplary embodiment.
FIG. 2 is a schematic diagram of an analysis chart, according to an exemplary embodiment.
FIG. 3 is a flowchart illustrating a script assembly, according to an example embodiment.
FIG. 4 is a flow chart illustrating a result parsing in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of methods and systems that are consistent with aspects of the present application, as detailed in the accompanying claims.
FIG. 1 is a flow chart illustrating a method of data analysis according to an exemplary embodiment. The method comprises the following steps:
step S1: acquiring an analysis chart and analyzing the analysis chart;
step S2: assembling the analyzed information into an execution script, and sending the execution script to a calculation engine so that the calculation engine performs data analysis according to the execution script;
step S3: and receiving a calculation result returned by the calculation engine, and carrying out visualization processing on the calculation result according to the analysis chart.
The scheme of the application can be realized based on the browser without using a professional tool, so that a user does not need to master the professional technology; the data analysis content required by free construction can be required according to the requirement, and the data analysis result can be obtained in real time, so that the requirement of convenient and rapid analysis is met.
The scheme of the application is based on a common Web browser, and provides a data analysis function of seeing and obtaining massive data, namely the graph originally designed is the same as the graph of the final analysis result. The system mainly comprises a chart design module, a chart analysis module, a script assembly module, a calculation engine and a result analysis module.
The user can directly access the page of the visual chart design module through a common Web browser, data analysis contents required by the page design of the chart design module are translated by the chart analysis module, the data analysis contents designed by the user are further assembled by corresponding query scripts, the calculation result is queried through a calculation engine, the analysis is carried out through the result analysis module, and finally the result is displayed in the browser at the user side in a visual mode.
The scheme of the application is expanded and explained below by combining with a specific application scene.
Some preparation work is required before the implementation of the solution of the present application. The method is characterized in that model information needing to be subjected to data analysis is prepared in advance, and the prepared content mainly comprises all field information of a model, and the field information comprises several core contents including field names, field types and field lengths. After the preparation is completed, the subsequent implementation steps can be performed.
1. And visualizing the chart design. And adopting a chart design module to perform man-machine interaction, and receiving chart contents designed by a user. And providing user operation by using the visual interface obtained by the user, and performing chart design. After the design is completed, the system stores the design content for the next stage.
According to the scheme, the self-service visual design page is adopted, so that a user is supported to complete corresponding data analysis requirements in a mouse dragging mode, and professional technical knowledge is not needed. It should be noted that, the operation mode of mouse dragging is an existing man-machine interaction technology, and has a ready open source scheme, for example, the Vue front end frame can be directly used.
Referring to fig. 2, when a user performs a chart design using the chart design module, field information, which is the model in the preparation work, is used, and thus the field information used in the chart is known. Through the chart design module, one or more fields can be directly designated as dimensions or indexes to be used, and if the fields are used as indexes, corresponding calculation modes including maximum value, minimum value, summation, counting, average value and the like can be set. Specific conditional constraints may be set for any field, for example equal to/containing some content/greater than a certain value, with judgment constraints being made by the type information of the field. Auxiliary information such as result ordering and the like is also included for various charts. The final content of the design is formed into a data structure and passed to the chart parsing module.
When the scheme of the application is implemented, fields such as gender, user type, account name, age and the like are provided on the page of the browser for user operation selection. In the program, all metadata information is recorded in these fields.
For example, the user needs to design a table with "gender, user type, account number, age, and age. The user only needs to select the fields of gender, user type, account name and age, and the account is set to be in a statistical quantity, and the ages need to find the maximum value and the minimum value respectively.
2. And the chart analysis module analyzes the user design chart content stored in the previous stage.
In some embodiments, the parsing the analysis chart includes:
and analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart.
The chart analysis module decomposes the data structure formed by the chart design module, and analyzes the content by combining a pre-stored data model to obtain the content to be analyzed, and the chart analysis module comprises: field information, condition information, dimension information, index information, and the like.
The field information is the field information to be analyzed, and comprises key information such as field name, field length, field type and the like.
The condition information, namely the constraint condition set in the last stage, is used for screening the field information.
Dimension information, that is, dimensions in the data analysis process that need to be analyzed for correlation, provides aggregation based mainly on dimensions.
The index information is a calculation mode for calculating and processing the data line in the data analysis process. I.e. according to a certain calculation rule, the calculation mode of calculating and processing the corresponding fields, such as summation, counting, maximum value, minimum value, etc.
The scheme supports free data analysis in a multi-dimensional and multi-index computing mode, and results can be directly presented in a form and graph mode.
Referring to the previous embodiment, 5 fields are parsed to be used, one for each of gender, user type, account name, and two for age. The user sets 1 account name and 2 ages to be processed as indexes, and the rest fields are automatically used as dimensions. The condition information is also set independently in the first step, and the setting can be resolved in the first step.
3. And the script assembling module uses the information obtained by the analyzer to assemble the script.
In some embodiments, the assembling the parsed information into the execution script includes:
carrying out semantic analysis and splitting on the calculation result;
assembling basic field information according to the field information;
assembling any multi-level aggregation according to the dimension information;
and assembling various operation contents based on arbitrary multi-level aggregation according to the index information.
The script assembly module further processes the content analyzed by the chart analysis module, and assembles the analyzed content into an execution script required by the next calculation engine. An elastic search cluster can be used as a calculation engine, and a script assembly module can be used for freely assembling and forming an executable script of the elastic search according to requirements. The method mainly comprises the steps of splicing basic field information, splicing any multi-level aggregation and splicing various operation contents based on any multi-level aggregation.
Referring to fig. 3, a process flow of a script assembly module is shown, mainly comprising:
receiving the content which has been parsed;
content is assembled into scripts that can run in an elastic search.
More specifically, the script assembling module performs script assembling and splicing according to the set dimension information, index information (including calculation mode information) and condition information and field information of the model. Taking assembly standard SQL as an example, dimension information is assembled into content in group by, index information is assembled into content in select, and condition information is assembled into content in where. Taking the elastic search as an example, the dimension information is assembled into contents in aggregation (aggregation in the elastic search is hierarchical nested, so the multi-dimensional aggregation information described above is also multi-level aggregation information here), the index information is mainly assembled into sub-node contents (such as max, count, etc.) in aggregation, and the condition information is assembled into contents in query.
With reference to the foregoing embodiments, according to the parsed content, the adaptation computation engine generates a script, such as Json's assembly using elastic search, and the final result set uses unique identification codes to identify fields, which can be understood as aliases of the fields. For example, two ages in the example, identified as age_1 and age_2 may be distinguished.
4. And sending the assembled script to a calculation engine for calculation.
The computing engine can use the elastic search cluster and the execution script generated by the script assembly module, so that the required computing result can be directly obtained. According to the scheme, the response speed of the Web application is guaranteed through the real-time operation capability of the clusters.
According to the scheme, rapid calculation support of mass data can be realized, under the condition of hundred million-level data volume, multidimensional combination analysis (within 10 dimensions) is carried out, the response time is controlled at the second level, and the performance is not weaker than that of the traditional BI software.
5. And the result analysis module analyzes the calculation result information.
In some embodiments, the visualizing the calculation result according to the analysis chart includes:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
The result analysis module carries out intelligent analysis on the complex JSON of the elastic search cluster calculation result, and respectively analyzes field information, dimension information, aggregation information, calculation result information and the like of the response according to the content designed by the user. The relevant information is finally presented in a user interface for use by the user in a graphical visualization.
Referring to fig. 4, a process flow of the result parsing module is shown, mainly including:
receiving JSON completed by the elastiscsearch calculation;
the JSON content is dynamically analyzed, and all common field information, dimension information, aggregation information and calculation result information can be analyzed;
the results are presented to the user as visual content.
More specifically, the result parsing module needs to parse in two aspects: on the one hand, the analysis of the result data; and on the other hand, the corresponding relation analysis of the data and the content in the earliest chart design module.
The resolution of the resulting data includes several major components: basic field information, dimension information and index information are matched with the user-defined setting (such as field alias) of the fields in the previous steps, so that result data can be associated, and the basic field information, the multi-level aggregation information and the operation result information are analyzed by taking an elastic search as an example. It should be noted that, according to the scheme, a field unique identifier in a result set is specified through a determined coding rule, and association matching can be performed through the identifier; the result set has the unique identification of the field for association matching, and the parsed field information, dimension information and index information are the same as the corresponding information in the previous step.
The corresponding relation analysis of the contents of the chart design module is that after the relevant field information in the result information is in one-to-one correspondence or mapping with the field information used in the chart analysis module and the script assembly module, the relevant field information in the calculation result can be obtained through translation, and finally, the relevant field information is corresponding to the contents set in the chart design module by a user. And finally, feeding the data information back to the chart design module and presenting the data information to a user.
The data information mainly refers to the result information of query and calculation, for example, one field is summed, and then the data information in the result is the accumulated and summed value of the field.
The data information feedback is performed through the interaction of front and rear interfaces. For example, the interface may be a common http request interface, and the interface may be parsed according to a agreed message format. Taking the table as an example, specifically: the returned message mainly comprises two parts: part of the information is metadata information, namely, the header of the table is described, and the fields are also distinguished and identified by the unique identification codes described above; the other part is specific table data. After receiving the message content, the chart design module analyzes the two parts of content and associates the corresponding content, so that the final content can be displayed.
With reference to the foregoing embodiments, according to the field unique identifier (field alias), result data corresponding to the corresponding field can be obtained; with the data, the form can be filled with data and finally presented to the user with the desired content. For example, the original Chinese name of the field is "name", the English name is "name", and the display result also has the field. If the user-defined field name is "user name", the program will generate a unique identification code according to a certain rule (for example, the rule currently used is that statistics is performed on the same field which is used, and the field name+number is assembled), for example, a new field identification "name_123" is generated, and the specified result set gives corresponding result data according to "name_123", so that the data in the result, the field metadata information, the field name set by the user and other contents can be uniformly associated, mapped and matched.
According to the scheme, special client software is not required to be installed, and the application can be operated by directly using a common Web browser, so that the application is more convenient and faster than the traditional BI software; graphical interface, business personnel can freely and quickly construct the required data analysis content according to own needs without professional technology; and the data analysis result can be obtained in real time, thereby meeting the requirements of convenient and rapid analysis.
The present application also provides the following embodiments:
a data analysis system, comprising:
the chart analysis module is used for acquiring an analysis chart and analyzing the analysis chart;
the script assembly module is used for assembling the analyzed information into an execution script and sending the execution script to the calculation engine;
the calculation engine is used for carrying out data analysis according to the execution script;
and the result analysis module is used for receiving the calculation result returned by the calculation engine and carrying out visualization processing on the calculation result according to the analysis chart.
In some embodiments, the chart parsing module parses the analysis chart, and specifically includes:
and analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart.
In some embodiments, the field information includes: field name, field length, field type;
the dimension information is the dimension which needs to be analyzed and associated in the data analysis process;
the index information is a calculation mode for calculating and processing the data line in the data analysis process.
In some embodiments, the script assembling module assembles the parsed information into an execution script, which specifically includes:
carrying out semantic analysis and splitting on the calculation result;
assembling basic field information according to the field information;
assembling any multi-level aggregation according to the dimension information;
and assembling various operation contents based on arbitrary multi-level aggregation according to the index information.
In some embodiments, the result analysis module performs visualization processing on the calculation result according to the analysis chart, and specifically includes:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
The specific steps in which the various modules perform operations in relation to the systems of the embodiments described above have been described in detail in relation to the embodiments of the method and are not described in detail herein.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (6)
1. A method of data analysis, comprising:
the method comprises the steps of obtaining an analysis chart and analyzing the analysis chart, and specifically comprises the following steps: analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart;
assembling the analyzed information into an execution script, and sending the execution script to a calculation engine so that the calculation engine performs data analysis according to the execution script, wherein the assembling the analyzed information into the execution script specifically comprises the following steps: carrying out semantic analysis and splitting on the calculation result; assembling basic field information according to the field information; assembling any multi-level aggregation according to the dimension information; splicing various operation contents based on arbitrary multi-level aggregation according to the index information;
and receiving a calculation result returned by the calculation engine, and carrying out visualization processing on the calculation result according to the analysis chart, wherein the calculation engine comprises an elastic search cluster.
2. The method of claim 1, wherein the field information comprises: field name, field length, field type;
the dimension information is the dimension which needs to be analyzed and associated in the data analysis process;
the index information is a calculation mode for calculating and processing the data line in the data analysis process.
3. The method according to claim 1 or 2, wherein the visualizing the calculation result according to the analysis chart comprises:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
4. A data analysis system, comprising:
the chart analysis module is used for acquiring an analysis chart and analyzing the analysis chart, and specifically comprises the following steps: analyzing field information, dimension information and index information of data analysis to be performed from the analysis chart;
the script assembly module is used for assembling the analyzed information into an execution script and sending the execution script to the calculation engine, wherein the assembling of the analyzed information into the execution script specifically comprises the following steps: carrying out semantic analysis and splitting on the calculation result; assembling basic field information according to the field information; assembling any multi-level aggregation according to the dimension information; splicing various operation contents based on arbitrary multi-level aggregation according to the index information;
the calculation engine is used for carrying out data analysis according to the execution script;
and the result analysis module is used for receiving the calculation result returned by the calculation engine and carrying out visualization processing on the calculation result according to the analysis chart, wherein the calculation engine comprises an elastic search cluster.
5. The system of claim 4, wherein the field information comprises: field name, field length, field type;
the dimension information is the dimension which needs to be analyzed and associated in the data analysis process;
the index information is a calculation mode for calculating and processing the data line in the data analysis process.
6. The system according to claim 4 or 5, wherein the result analysis module performs visualization processing on the calculation result according to the analysis chart, and specifically includes:
resolving the calculation result into field information, dimension information and calculation result information;
matching the analyzed field information with the field information in the analysis chart;
integrating the analyzed calculation result information into the analysis chart according to the matching result;
outputting the integrated analysis chart.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911095118.6A CN110909017B (en) | 2019-11-11 | 2019-11-11 | Data analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911095118.6A CN110909017B (en) | 2019-11-11 | 2019-11-11 | Data analysis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110909017A CN110909017A (en) | 2020-03-24 |
| CN110909017B true CN110909017B (en) | 2023-05-02 |
Family
ID=69816646
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911095118.6A Active CN110909017B (en) | 2019-11-11 | 2019-11-11 | Data analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110909017B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112256789B (en) * | 2020-10-19 | 2022-06-17 | 杭州比智科技有限公司 | Intelligent visual data analysis method and device |
| CN112347161A (en) * | 2020-11-18 | 2021-02-09 | 未来电视有限公司 | Data analysis processing method, device, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106354786A (en) * | 2016-08-23 | 2017-01-25 | 冯村 | Visual analysis method and system |
| CN107967359A (en) * | 2017-12-21 | 2018-04-27 | 百度在线网络技术（北京）有限公司 | Data visualization analysis method, system, terminal and computer-readable recording medium |
| CN108228874A (en) * | 2018-01-18 | 2018-06-29 | 北京邮电大学 | World knowledge collection of illustrative plates visualization device and method based on artificial intelligence technology |
| CN108710652A (en) * | 2018-05-09 | 2018-10-26 | 长城计算机软件与系统有限公司 | A kind of data analysing method and system, storage medium based on statistics |
| CN108804513A (en) * | 2018-04-24 | 2018-11-13 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Automatic visual analysis method for big data platform |
| CN109814864A (en) * | 2019-01-02 | 2019-05-28 | 北京永洪商智科技有限公司 | A kind of data visualization method, visualization system, Web browsing system and equipment |
| CN110019397A (en) * | 2017-12-06 | 2019-07-16 | 北京京东尚科信息技术有限公司 | For carrying out the method and device of data processing |
-
2019
- 2019-11-11 CN CN201911095118.6A patent/CN110909017B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106354786A (en) * | 2016-08-23 | 2017-01-25 | 冯村 | Visual analysis method and system |
| CN110019397A (en) * | 2017-12-06 | 2019-07-16 | 北京京东尚科信息技术有限公司 | For carrying out the method and device of data processing |
| CN107967359A (en) * | 2017-12-21 | 2018-04-27 | 百度在线网络技术（北京）有限公司 | Data visualization analysis method, system, terminal and computer-readable recording medium |
| CN108228874A (en) * | 2018-01-18 | 2018-06-29 | 北京邮电大学 | World knowledge collection of illustrative plates visualization device and method based on artificial intelligence technology |
| CN108804513A (en) * | 2018-04-24 | 2018-11-13 | 华东计算技术研究所（中国电子科技集团公司第三十二研究所） | Automatic visual analysis method for big data platform |
| CN108710652A (en) * | 2018-05-09 | 2018-10-26 | 长城计算机软件与系统有限公司 | A kind of data analysing method and system, storage medium based on statistics |
| CN109814864A (en) * | 2019-01-02 | 2019-05-28 | 北京永洪商智科技有限公司 | A kind of data visualization method, visualization system, Web browsing system and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110909017A (en) | 2020-03-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11429600B2 (en) | Loading queries using search points | |
| CN108038222B (en) | System of entity-attribute framework for information system modeling and data access | |
| CN110717319B (en) | Self-service report generation method, device, computing equipment and system | |
| US7640496B1 (en) | Method and apparatus for generating report views | |
| US11003682B2 (en) | Metrics analysis workflow | |
| US7668860B2 (en) | Apparatus and method for constructing and using a semantic abstraction for querying hierarchical data | |
| KR102330547B1 (en) | Building reports | |
| US8255368B2 (en) | Apparatus and method for positioning user-created data in OLAP data sources | |
| US8417690B2 (en) | Automatically avoiding unconstrained cartesian product joins | |
| US20110208692A1 (en) | Generation of star schemas from snowflake schemas containing a large number of dimensions | |
| US11494395B2 (en) | Creating dashboards for viewing data in a data storage system based on natural language requests | |
| CN110909017B (en) | Data analysis method and system | |
| US20170193036A1 (en) | Framework for joining datasets | |
| US20190034247A1 (en) | Creating alerts associated with a data storage system based on natural language requests | |
| CN116468010A (en) | Report generation method, device, terminal and storage medium | |
| JP6781820B2 (en) | Distributed Computing Framework and Distributed Computing Method (DISTRIBUTED COMPUTING FRAMEWORK AND DISTRIBUTED COMPUTING METHOD) | |
| CN115576974A (en) | Data processing method, device, equipment and medium | |
| CN108647340B (en) | Multi-dimensional data real-time analysis method based on dynamic cross table | |
| CN116578585B (en) | Data query method, device, electronic equipment and storage medium | |
| CN111966342A (en) | Rule configuration and analysis method, system and equipment based on similar natural language | |
| CN114297443B (en) | Processing method, device, equipment and storage medium of graph data query statement | |
| US20230177046A1 (en) | Fast table search for visualization of complex hierarchy data | |
| CN108153834B (en) | Method and device for querying data by commercial intelligent application and electronic equipment | |
| CN111414330B (en) | Data editing method and system, data processing device and storage medium | |
| CN116089454B (en) | Dynamic log analysis method and system |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |