CN110795458B - Interactive data analysis method, device, electronic equipment and computer readable storage medium - Google Patents

Abstract

The application discloses an interactive data analysis method, an interactive data analysis device, electronic equipment and a computer-readable storage medium. The method comprises the steps that an index field and a data aggregation type for data retrieval are determined based on data analysis selection operation executed by a user on a visual chart displayed at the front end; retrieving data to be analyzed corresponding to the index field from a preset database; performing data aggregation analysis on the data to be analyzed according to the data aggregation type; and displaying the aggregation analysis result at the front end, thereby realizing interactive data analysis based on the visual chart and improving the data analysis efficiency.

Description

Interactive data analysis method, apparatus, electronic device, and computer-readable storage medium

technical field

The present application relates to the technical field of data analysis, and in particular, to an interactive data analysis method, apparatus, electronic device, and computer-readable storage medium.

Background technique

At present, when performing data analysis based on visualization tools, since the charts created based on the visualization tools are not interactive, the created charts can only be viewed, but data analysis operations such as data filtering cannot be performed based on the charts, resulting in low data statistics efficiency. Poor user experience.

SUMMARY OF THE INVENTION

In a first aspect, an embodiment of the present application provides an interactive data analysis method, the method comprising:

Determine the index field for data retrieval and the data aggregation type based on the data analysis selection operation performed by the user on the visual chart displayed on the front end;

Retrieve the data to be analyzed corresponding to the index field from a preset database;

Perform data aggregation analysis on the data to be analyzed according to the data aggregation type;

The aggregated analysis results are displayed on the front end.

In a second aspect, an embodiment of the present application further provides an interactive data analysis device, the device comprising:

The information determination module is used to determine the index field and data aggregation type for data retrieval based on the data analysis selection operation performed by the user on the visual chart displayed on the front end;

a data retrieval module for retrieving the data to be analyzed corresponding to the index field from a preset database;

an aggregation analysis module, configured to perform data aggregation analysis on the data to be analyzed according to the data aggregation type;

The result display module is used to display the aggregated analysis result on the front end.

In a third aspect, embodiments of the present application further provide an electronic device, including a processor, a memory, and a computer program stored on the memory and executable on the processor, where the computer program is executed by the processor When implementing the steps of the interactive data analysis method described above.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the above-mentioned interactive data analysis method is implemented. step.

In the above technical solutions given in the embodiments of the present application, data index fields and data aggregation types can be determined according to data analysis and selection operations performed by the user on the visual chart displayed on the front end, and then data retrieval, Filter, aggregate, and display the aggregated results on the front end to effectively improve data analysis efficiency and greatly improve user experience.

Description of drawings

The drawings described herein are used to provide further understanding of the present application and constitute a part of the present application. The schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the attached image:

FIG. 1 is a schematic flowchart of an interactive data analysis method provided in Embodiment 1 of the present application.

FIG. 2 is a schematic diagram of a visualization chart provided in Embodiment 1 of the present application.

FIG. 3 is a schematic flowchart of the interactive data analysis method provided in the second embodiment of the present application.

FIG. 4 is a schematic block structure diagram of an interactive data analysis apparatus provided in Embodiment 3 of the present application.

FIG. 5 is a schematic block structure diagram of an electronic device provided in Embodiment 4 of the present application.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

In order to facilitate data query, retrieval, statistics, etc., the data in the database is generally stored according to the corresponding storage type, and the data stored in the database corresponds to a data retrieval table. The data retrieval table can include multiple retrieval types. The retrieval type of the index can also correspond to multiple different index fields, and each index field can also include multiple sub-index fields.

For example, assuming that the data stored in the database is the population data of a country, then multiple search types can be age, gender, city of residence, occupation, etc., and the index field under the search type of city of residence can include, for example, Beijing, Guangzhou , Shanghai, Nanjing, Shaanxi and other index fields, and the Shaanxi index field can include sub-index fields such as Xi'an, Xianyang, Weinan, Baoji, etc., that is to say, a data index table can include multi-level index fields It can be set according to the actual situation.

However, when using Kibana, a visual analysis tool, to visually analyze data stored in ElasticSearch, the charts created based on Kibana, a visual analysis tool, are not interactive, resulting in low data analysis efficiency and poor user experience. Among them, EIasticSearch is a search server based on Lucene. It provides a distributed multi-user capable full-text search engine based on a RESTful web interface. Developed in Java and released as open source under the terms of the Apache License, Elasticsearch is the current popular enterprise search engine. Kibana is an open source analysis and visualization visual analysis tool for Elasticsearch. It is used to search and view data stored interactively in the Elasticsearch index, and perform advanced data analysis and display through various charts.

In view of this, the embodiments of the present application provide an interactive data analysis method, apparatus, electronic device, and computer-readable storage medium, so as to realize interactive data analysis based on visual charts, improve data analysis efficiency, and improve user experience. The technical solutions provided in the present application will be introduced below with reference to the accompanying drawings.

Example 1

As shown in FIG. 1, which is a schematic flowchart of the interactive data analysis method provided in the first embodiment of the present application, the interactive data analysis method can be performed by, but is not limited to, an electronic device, and the interactive data analysis method can include the following steps:

Step 102: Determine an index field and a data aggregation type for data retrieval based on the data analysis selection operation performed by the user on the visual chart displayed on the front end.

In the first embodiment, the front end refers to a user interface for displaying data information such as charts and for users to perform operations such as data analysis and selection operations. The terminal that performs data interaction with the electronic device is not limited in the first embodiment.

As an optional implementation manner, the process of determining the index field in step 102 may be: determining the chart area when the user performs the data analysis and selection operation on the visual chart displayed on the front end; Index fields serve as index fields for data retrieval.

For example, please refer to FIG. 2, which is the population distribution table when the retrieval type is the city of residence. Then, assuming that the chart area in the data analysis selection operation in FIG. 2 is A, then, the index included in the chart area A The fields are city 1 and city 2, and then it can be determined that the index fields used for data retrieval are city 1 and city 2.

As another optional implementation manner, when there is a data analysis selection operation, an option floating layer for index field selection can be displayed, so that the user can directly select one or more index fields based on the option floating layer as the Index field for data retrieval.

It can be understood that, when the interactive data analysis is performed in the first embodiment, the electronic device may be preset with, but not limited to, the above two methods of determining the index field.

Further, the data aggregation type is a data operation rule used by the electronic device to perform data operation on the data to be analyzed retrieved based on the index field. For example, the data aggregation type may include, but is not limited to, grouping aggregation, averaging Data ratio aggregation, data set merge operation, data set intersection operation, data set complement operation, data set deviation operation, etc.

As an implementation manner, the determination process of the data aggregation type given in step 102 may be: when there is a data analysis selection operation, the type selection floating layer used for data aggregation type selection is displayed on the front end; Type Selection The aggregation type selection operation performed on the floating layer determines the data aggregation type.

As another implementation manner, the process of determining the data aggregation type given in step 102 may also be: when the corresponding relationship between the data analysis selection operation and the aggregation type is preset in the electronic device, select the operation and the aggregation type according to the data analysis. Correspondence determines the type of data aggregation.

For example, the preset correspondence is that the data aggregation type corresponding to one or more click operations is grouping aggregation, the data aggregation type corresponding to continuous clicking operations by the chart area is intersection aggregation, and so on. For example, assuming that the data analysis operation is to click on the index field A and the index field B in sequence, then, according to the preset corresponding relationship, it can be obtained that the data aggregation type is grouping aggregation and the like.

It can be understood that, when performing interactive data analysis, the electronic device may have, but is not limited to, the determination manners of the above two types of data aggregation.

In step 104, the data to be analyzed corresponding to the index field is retrieved from a preset database.

Step 106: Perform data aggregation analysis on the data to be analyzed according to the data aggregation type.

In the first embodiment, the aggregation function agg() can be used to realize the aggregation analysis of the data to be analyzed, such as the aforementioned group aggregation, average aggregation, maximum/small value aggregation, data proportion aggregation, data set merge operation, Data set intersection operation, data set complement operation, data set deviation operation, etc.

Step 108, displaying the aggregated analysis result on the front end.

In the first embodiment, when displaying the aggregated analysis result, the aggregated analysis result may be displayed on the front end in the form of a visual chart, or the aggregated analysis result may be displayed on the front end in the form of a data set. Among them, as an implementation method, it is assumed that when the aggregated analysis results need to be displayed on the front end as a visual chart, the chart type for visual chart display can be determined based on the chart type selection operation performed by the user on the visual chart displayed on the front end, and then according to Aggregate analysis results and chart types to generate a visual chart and display the visual chart on the front end.

Or, determine the chart type according to the type of aggregate analysis results. For example, a column chart is used to display integer data, a bar chart is used to display string type data, a pie chart is used to display enumeration type data, and a time axis is used to display time type. Data, etc., and then generate a visual chart based on the aggregated analysis results and chart types, and display the visual chart on the front end.

As another implementation, assuming that the aggregated analysis results need to be displayed on the front end as datasets, then when the corresponding data to be analyzed is retrieved from the database according to the index field, the data to be analyzed can be directly displayed as datasets. The method is displayed on the front end for users to view. For example, Shaanxi: {x1, x2, x3, x4, ...} etc.

Further, as an optional implementation manner, the interactive data analysis method provided in Embodiment 1 of the present application may further include the following steps:

Step 1010, when there is an interactive data analysis operation initiated based on the front end, query the storage type of the data to be analyzed corresponding to the interactive data analysis operation in the database, and the data index table corresponding to the storage type.

Step 1012 , the types of index fields used for data retrieval and the number of different types of index fields in the statistics data index table will be displayed on the front end according to the statistics results.

In this embodiment 1, the interactive data analysis operation may be the same as or different from the aforementioned data analysis selection operation. This embodiment does not limit this. The settings of the above steps 1010 and 1012 can make the user more intuitive, It is convenient to understand the data index table information of the data stored in the database, such as retrieval type, index fields, etc., and then realize more efficient and accurate data analysis based on the statistical results.

As an optional implementation manner, the following describes the implementation process of the interactive data analysis method provided in Embodiment 1 of the present application by taking ElasticSearch as the database and Kibana as the visual analysis tool as an example.

(1) When there is an interactive data analysis operation initiated by the front end, query the storage type (mapping) of the data to be analyzed on ElasticSearch, the data index table corresponding to the storage type, and the index field (Field) included in the data index table. ).

(2) Functions such as Value Count can be used to count the types of index fields used for data retrieval and the number of different types of index fields, and the statistical results can be displayed on the front end in the form of visual charts (such as list charts, etc.) For users to perform data analysis based on the visual chart.

(3) Based on the data analysis and selection operation performed by the user on the visual chart displayed on the front end, the index field and data aggregation type for data retrieval are determined. The data aggregation type is passed to the electronic device, and the data to be analyzed is retrieved from the database saved on ElasticSearch according to the attribute name, and the data to be analyzed is aggregated and analyzed according to the data aggregation type (such as agg aggregation function), and the aggregated analysis results are visualized in the form of charts. Show the front end.

For example, retrieval and aggregation analysis of the data to be analyzed can be performed on ElasticSearch through the following pseudocode.

When the index field is determined according to the data analysis selection operation, the index field can be one or more values, such as the index fields corresponding to age, surname, height, etc., and data retrieval is performed according to the determined one or more index fields, for example, You can use the bool of ElasticSearch for conditional filtering, and then perform agg aggregation to generate a new data set, which is returned to the front-end, and the front-end is displayed with a chart, and the retrieval conditions are recorded.

In actual implementation, interactive multi-level data filtering based on visual charts can be realized by continuously performing the index fields, determination of data aggregation types, data retrieval, aggregation, and display operations in the above (3) on the visual charts displayed on the front end. For example, the first-level index field can be country, the second-level index field can be further determined on the basis of the first-level index field to obtain the province, and the third-level index field can be further determined based on the second-level index field to obtain the urban area etc., this embodiment will not be repeated here.

In the above-mentioned interactive data analysis method provided in the first embodiment of the present application, the data index field and the data aggregation type can be determined according to the data analysis selection operation performed by the user on the visual chart displayed on the front end, and then the data index field and the data aggregation type can be determined according to the index field and the data aggregation type. Perform data retrieval, filtering, aggregation, and display the aggregated results on the front end to realize interactive data analysis based on visual charts, which can not only effectively improve the efficiency of data analysis, but also greatly improve the user experience.

Embodiment 2

Compared with the interactive data analysis method given in the first embodiment, the interactive data analysis method given in the second embodiment of the present application adds a chart drag operation initiated based on a visual chart to perform merging, merging, difference, and data sets. Complementary data analysis operations. The interactive data analysis method may include the steps shown in FIG. 3 , the contents of which are as follows.

Step 202 , when the data analysis selection operation is a chart drag operation, determine an index field according to the data information represented by the dragged visual chart, and retrieve data to be analyzed corresponding to the index field from a preset database.

In the second embodiment, the chart drag operation may be a drag operation performed on one or more individual charts, or may be a drag operation performed on a part of the chart area on the visualization chart. This does not limit.

For example, assuming that the visual chart displayed on the front end includes chart T1 and chart T2, when there is a chart drag operation on the front end, such as dragging chart T1 to chart T2, dragging chart T2 to chart T1, dragging chart T1 separately, The graph T1 or the graph T2 can be dragged separately, etc., which is not limited in this embodiment.

As an optional implementation manner, the step of determining the index field according to the data information represented by the dragged visual chart includes: taking the chart at the start position of the chart drag operation as the first sub-chart, and the chart at the end position As the second sub-diagram, the first index field is determined according to the data information displayed in the first sub-diagram, and the second index field is determined according to the data information displayed in the second sub-diagram.

Based on this, the step of retrieving the data to be analyzed corresponding to the index field from the preset database includes: retrieving from the preset database the first data set corresponding to the first index field and the second index field For the corresponding second data set, the first data set and the second data set are used as the data to be analyzed.

Step 204 , displaying a preset type selection floating layer in the front end, in response to an aggregation type selection operation initiated based on the type selection floating layer.

Step 206: Perform data aggregation analysis on the data to be analyzed according to the aggregation type selection operation, and display the aggregation analysis result on the front end.

Optionally, the aggregation type selection operation in the second embodiment may be at least one of a data set merge operation, a data set intersection operation, a data set complement operation, and a data set deviation operation. For example, the dataset retrieved from the database according to the index field A is A1{x1, x2, x3}, and the dataset retrieved from the database according to the index field B is B1{x2, x5, x8}, then, assuming the aggregation The type selection operation is a data set merging operation, that is to say, it is necessary to perform a data set merging operation on the data sets A1{x1, x2, x3} and B1{x2, x5, x8}, and then the data sets C1{x1, x2, x3, x5, x8}.

For another example, suppose that the aggregation type selection operation is a dataset intersection operation, that is to say, the dataset intersection operation needs to be performed on datasets A1{x1, x2, x3} and B1{x2, x5, x8} to obtain dataset C1 {x2}.

In the second embodiment, the implementation process of the interactive data analysis method provided in the embodiment of the present application is described by taking ElasticSearch as the preset database in the electronic device and the visual analysis tool as Kibana as an example.

Assuming that the chart drag operation is to drag the chart T1 to the chart T2, then, taking the chart T1 as the first sub-chart and the chart T2 as the second sub-chart, determine the first sub-chart corresponding to the data information displayed in the first sub-chart T1. An index field, and a second index field corresponding to the data information displayed in the second sub-chart. In addition, the feedback type selects the floating layer to display on the front end, for the user to select the floating layer based on the type to perform the aggregation type selection operation. For example, the Restful interface obtains the first index field, the second index field and the aggregation type selection operation from the front end, and retrieves the first data set corresponding to the first index field and the first data set corresponding to the first index field from ElasticSearch according to the first index field and the second index field. The second dataset corresponding to the second index field.

As an implementation manner, it is assumed that the aggregation type selection operation is a data set merging operation, then, the data set merging operation can be performed on the first data set and the second data set through, but not limited to, the following pseudocode:

As another implementation manner, assuming that the aggregation type selection operation is a data set intersection operation, then, the data set intersection operation can be performed on the first data set and the second data set through, but not limited to, the following pseudocode:

As yet another implementation manner, assuming that the aggregation type selection operation is the operation of finding the left difference of the data set, then, the following pseudo code can be used to perform the left difference of the data set on the first data set and the second data set. operate:

As another implementation manner, assuming that the aggregation type selection operation is the operation of finding the right difference set of the data set, then, the following pseudo code can be used to perform the right difference set of the data set on the first data set and the second data set. operate:

As another implementation manner, assuming that the aggregation type selection operation is a data set complement operation, then, the first data set and the second data set can be subjected to a data set complement operation through, but not limited to, the following pseudocode:

It should be noted that the aggregation type selection operation included in the interactive data analysis in this embodiment may be, but not limited to, the above-mentioned types, and may be specifically set according to actual requirements.

In the above technical solution given in the second embodiment, interactive data analysis can be realized by dragging the chart, which can greatly improve the efficiency of data statistics and analysis. At the same time, it also effectively solves the problem that graphical visualization tools such as Kibana do not have the ability to interactively analyze charts, resulting in the need to manually write the corresponding DSL query statement for each query condition in order to generate the data that exists in the Kibana chart. Analysis of low efficiency and difficult problems.

Embodiment 3

FIG. 4 is a schematic structural diagram of the interactive data analysis apparatus 10 provided in the third embodiment. Referring to FIG. 4 , in a software implementation, the interactive data analysis apparatus 10 may include an information determination module 110 , a data retrieval module 120 , an aggregate analysis module 130 , and a result display module 140 . in:

an information determination module 110, configured to determine an index field and a data aggregation type for data retrieval based on the data analysis selection operation performed by the user on the visual chart displayed on the front end;

a data retrieval module 120, configured to retrieve the data to be analyzed corresponding to the index field from a preset database;

an aggregation analysis module 130, configured to perform data aggregation analysis on the data to be analyzed according to the data aggregation type;

The result display module 140 is configured to display the aggregated analysis result on the front end.

In the above-mentioned interactive data analysis method provided in the first embodiment of the present application, the data index field and the data aggregation type can be determined according to the data analysis selection operation performed by the user on the visual chart displayed on the front end, and then the data index field and the data aggregation type can be determined according to the index field and the data aggregation type. Perform data retrieval, filtering, aggregation, and display the aggregated results on the front end to effectively improve data analysis efficiency and greatly improve user experience.

As an optional implementation manner, the information determination module 110 can be specifically configured to determine the chart area when the user performs the data analysis and selection operation on the visual chart displayed on the front end; the index field corresponding to the data information included in the chart area is used as Index field for data retrieval.

As another optional implementation, the information determination module 110 may also be specifically configured to display the type selection floating layer used for data aggregation type selection on the front end when there is a data analysis selection operation;

Determine the data aggregation type based on the aggregation type selection operation performed by the user on the type selection floating layer displayed on the front end; or/and

When the corresponding relationship between the data analysis selection operation and the aggregation type is preset, the data aggregation type is determined according to the data analysis selection operation and the corresponding relationship.

Further, the interactive data analysis device may also include:

The index information query module is used to query the storage type of the data to be analyzed corresponding to the interactive data analysis operation in the database and the data index table corresponding to the storage type when there is an interactive data analysis operation initiated by the front end.

The index information display module is used to count the types of index fields used for data retrieval in the data index table and the number of different types of index fields, and will be displayed on the front end according to the statistical results.

Further, the interactive data analysis device may also include:

The data to be analyzed determination module is used to determine the index field according to the data information represented by the dragged visual chart when the data analysis selection operation is a chart drag operation, and retrieve the corresponding index field from the preset database. data to be analyzed. Wherein, as an optional implementation manner, the data-to-be-analyzed determination module can be specifically configured to use the chart at the starting position of the chart drag operation as the first sub-chart, and the chart at the end position as the second sub-chart. The first index field is determined by the data information displayed in a sub-diagram, and the second index field is determined according to the data information displayed in the second sub-diagram; and the first data corresponding to the first index field is retrieved from a preset database set, and the second data set corresponding to the second index field, the first data set and the second data set are used as the data to be analyzed.

The floating layer display module is used to display the preset type selection floating layer in the front end, and respond to the aggregation type selection operation initiated based on the type selection floating layer.

The result display module is also used to select the operation according to the aggregation type to perform data aggregation analysis on the data to be analyzed, and display the aggregated analysis results on the front end.

Since the interactive data analysis device provided in the third embodiment has the same or corresponding technical features as the foregoing first or second embodiment, the detailed description of the interactive data analysis device 10 may refer to the foregoing first or second embodiment. The detailed description of the interactive data analysis method in Section 2 will not be repeated in this embodiment. For example, for the detailed description of the information determination module 110, reference may be made to the description of step 102 in the first embodiment, the detailed description of the data retrieval module 120 may refer to the description of step 104 in the first embodiment, and the detailed description of the aggregation analysis module 130. Refer to the description of step 106 in the foregoing first embodiment and so on.

Embodiment 4

FIG. 5 is a schematic structural diagram of an electronic device given in the fourth embodiment. Referring to FIG. 5 , at the hardware level, the electronic device includes a processor, and optionally an internal bus, a network interface, and a memory. The memory may include memory, such as high-speed random-access memory (Random-Access Memory, RAM), or may also include non-volatile memory (non-Volatile memory), such as at least one disk memory. Of course, the electronic equipment may also include hardware required for other services.

The processor, the network interface, and the memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard) bus. StandardArchitecture, extended industry standard structure) bus, etc. The bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one bidirectional arrow is shown in FIG. 5, but it does not mean that there is only one bus or one type of bus.

memory for storing programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include memory and non-volatile memory and provide instructions and data to the processor.

The processor reads the corresponding computer program from the non-volatile memory into the memory and runs it, forming a shared resource access control device on a logical level. The processor executes the program stored in the memory, and is specifically used to perform the following operations:

Determine the index field and data aggregation type for data retrieval based on the data analysis selection operation performed by the user on the visual chart displayed on the front end;

Retrieve the data to be analyzed corresponding to the index field from the preset database;

Perform data aggregation analysis on the data to be analyzed according to the data aggregation type;

Display the aggregated analysis results on the front end.

The above-mentioned method performed by the interactive data analysis apparatus 10 disclosed in the embodiment shown in FIG. 4 of this specification may be applied to a processor, or implemented by a processor. A processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above-mentioned method can be completed by a hardware integrated logic circuit in a processor or an instruction in the form of software.

The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of this specification can be implemented or executed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the methods disclosed in conjunction with the embodiments of this specification may be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other storage media mature in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.

The electronic device can also execute the interactive data analysis method shown in FIG. 1, and realize the functions of the interactive data analysis apparatus 10 in the embodiment shown in FIG.

Of course, in addition to software implementations, the electronic devices in the embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subjects of the following processing procedures are not limited to each logic A unit can also be a hardware or logic device.

Embodiment 5

Embodiment 5 of the present specification also provides a computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and the one or more programs include instructions, and the instructions, when used by a portable electronic device including multiple application programs When the device is executed, the portable electronic device can be made to execute the method of the embodiment shown in FIG. 2 , and is specifically used to execute the following method:

Determine the index field and data aggregation type for data retrieval based on the data analysis selection operation performed by the user on the visual chart displayed on the front end;

Retrieve the data to be analyzed corresponding to the index field from the preset database;

Perform data aggregation analysis on the data to be analyzed according to the data aggregation type;

Display the aggregated analysis results on the front end.

In the above technical solution given in the fifth embodiment, the data index field and the data aggregation type can be determined according to the data analysis and selection operation performed by the user on the visual chart displayed on the front end, and then data retrieval, Filter, aggregate, and display the aggregated results on the front end to effectively improve data analysis efficiency and greatly improve user experience.

In conclusion, the above are only preferred embodiments of the present specification, and are not intended to limit the protection scope of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification shall be included within the protection scope of this specification.

The systems, devices, modules or units described in the above embodiments may be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or A combination of any of these devices.

Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology. Information may be computer readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article of manufacture or device comprising a series of elements includes not only those elements but also Other elements not expressly listed, or which are inherent to such a process, method, article of manufacture, or apparatus are also included. Without further limitation, an element qualified by the phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, article of manufacture or apparatus that includes the element.

Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for related parts, please refer to the partial descriptions of the method embodiments.

Claims (8)

1. A method of interactive data analysis, the method comprising:

determining an index field and a data aggregation type for data retrieval based on data analysis selection operation performed by a user on a visual chart displayed at the front end;

retrieving data to be analyzed corresponding to the index field from a preset database;

performing data aggregation analysis on the data to be analyzed according to the data aggregation type;

displaying the results of the aggregation analysis at the front end;

wherein the step of determining an index field for data retrieval based on a data analysis selection operation performed by a user on the front-end displayed visualization chart comprises:

determining a chart region when the user performs the data analysis selection operation on the visualized chart displayed at the front end;

taking an index field corresponding to the data information included in the chart area as the index field for data retrieval; or/and

when data analysis selection operation exists, displaying an option floating layer for index field selection, so that a user directly selects one or more index fields based on the option floating layer as index fields for data retrieval;

a step of determining a data aggregation type based on a data analysis selection operation performed by a user on the front-end displayed visualization chart, comprising:

when the data analysis selection operation exists, displaying a type selection floating layer for data aggregation type selection at the front end;

determining the data aggregation type based on an aggregation type selection operation executed by a user on the front-end displayed type selection floating layer; or/and

and when the corresponding relation between the data analysis selection operation and the aggregation type is preset, determining the data aggregation type according to the data analysis selection operation and the corresponding relation.

2. The interactive data analysis method of claim 1, further comprising:

when interactive data analysis operation initiated based on the front end exists, inquiring the storage type of the data to be analyzed in the database corresponding to the interactive data analysis operation and a data index table corresponding to the storage type;

and counting the types of the index fields used for data retrieval in the data index table and the number of the index fields of different types, and displaying a counting result at the front end.

3. The interactive data analysis method of claim 1, further comprising:

when the data analysis selection operation is a chart dragging operation, determining an index field according to data information represented by a dragged visual chart, and retrieving data to be analyzed corresponding to the index field from a preset database;

displaying a preset type selection floating layer at the front end, and responding to the aggregation type selection operation of a user based on the type selection floating layer;

and performing data aggregation analysis on the data to be analyzed according to the aggregation type selection operation, and displaying an aggregation analysis result at the front end.

4. The interactive data analysis method of claim 3, wherein the step of determining an index field from the data information represented by the dragged visual chart comprises:

taking the chart at the starting position of the chart dragging operation as a first sub-chart and the chart at the ending position as a second sub-chart, determining a first index field according to the data information displayed by the first sub-chart, and determining a second index field according to the data information displayed by the second sub-chart;

the step of retrieving the data to be analyzed corresponding to the index field from a preset database includes:

and retrieving a first data set corresponding to the first index field and a second data set corresponding to the second index field from a preset database, and taking the first data set and the second data set as data to be analyzed.

5. The interactive data analysis method of claim 3, wherein the aggregation type selection operation comprises at least one of a data set merge operation, a data set intersection operation, a data set complementation operation, and a data set biasing operation.

6. An interactive data analysis apparatus, the apparatus comprising:

the information determining module is used for determining an index field and a data aggregation type for data retrieval based on data analysis selection operation executed by a user on a visual chart displayed at the front end;

the data retrieval module is used for retrieving the data to be analyzed corresponding to the index field from a preset database;

the aggregation analysis module is used for carrying out data aggregation analysis on the data to be analyzed according to the data aggregation type;

the result display module is used for displaying the aggregation analysis result at the front end;

the information determining module is specifically configured to determine a chart area when a user performs data analysis selection operation on a visual chart displayed at a front end; taking an index field corresponding to the data information included in the graph area as an index field for data retrieval; or/and

when data analysis selection operation exists, displaying an option floating layer for index field selection, so that a user directly selects one or more index fields based on the option floating layer as index fields for data retrieval;

the information determining module is further specifically configured to display a type selection floating layer for data aggregation type selection at the front end when data analysis selection operation exists;

determining a data aggregation type based on an aggregation type selection operation executed by a user on a front-end display type selection floating layer; or/and

and when the corresponding relation between the data analysis selection operation and the aggregation type is preset, determining the data aggregation type according to the data analysis selection operation and the corresponding relation.

7. An electronic device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the interactive data analysis method according to any one of claims 1 to 5.

8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the interactive data analysis method according to any one of claims 1 to 5.

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