CN114090838B - Method, system, electronic device and storage medium for visually displaying big data - Google Patents
Method, system, electronic device and storage medium for visually displaying big data Download PDFInfo
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Abstract
The application relates to a method, a system, an electronic device and a storage medium for large data visual display, wherein the method comprises the following steps: acquiring offline sub-graph data through an http request, analyzing the offline sub-graph data, and sorting all node data in the offline sub-graph data; mapping the classified node data to a decimal color numerical value, performing grid pre-layout on the classification index, and calculating a coordinate value of each node data on the grid under a Cartesian rectangular coordinate system; and performing visual graph rendering on each node according to the coordinate values, and binding events to obtain a graph data display form under a macroscopic angle. By the method and the device, the problems of information redundancy and inaccurate decision when data mining is carried out on big data in the graph database are solved, and the working efficiency of an analyst and the capacity of mining data values from graph data are effectively improved.
Description
Technical Field
The present application relates to the field of graph database technologies, and in particular, to a method, a system, an electronic device, and a storage medium for visually displaying big data.
Background
With the continuous development of information technology, mass relational data under different scenes are stored in a computer and used for meeting the requirements of users on data query or data mining and the like. However, today, there is no good solution for macroscopic display, semantic visualization display, etc. of mass data in databases.
In the related art, data analysts often can only perform bottom-up data analysis from a microscopic perspective through specific data indexes, thereby causing information redundancy and inaccurate decision making of service graph analysis.
At present, no effective solution is provided aiming at the problems of information redundancy and inaccurate decision when data mining is carried out on big data in a graph database in the related technology.
Disclosure of Invention
The embodiment of the application provides a method, a system, an electronic device and a storage medium for visually displaying big data, so as to at least solve the problems of information redundancy and inaccurate decision when data mining is carried out on the big data in a graph database in the related technology.
In a first aspect, an embodiment of the present application provides a method for visually displaying big data, where the method includes:
acquiring offline sub-graph data through an http request, analyzing the offline sub-graph data, and sorting all node data in the offline sub-graph data;
mapping the classified node data to decimal color values, performing grid pre-layout on the classification indexes, and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system;
and performing visual graph rendering on each node according to the coordinate values, and binding events to obtain a graph data display form under a macroscopic angle.
In some embodiments, before obtaining offline sub-graph data through an http request, the method includes:
and connecting the graph database, acquiring a data table in the graph database, creating a subgraph of the spatial total graph data through graph database grammar, and calculating the subgraph in an off-line manner to obtain the off-line subgraph data.
In some embodiments, sorting all node data in the offline sub-graph data includes:
and constructing an adjacency list, sequencing the classified cdlp values of all the node data, classifying and aggregating all the node data into different arrays according to the classified cdlp values, generating cdlpMap and storing the cdlpMap in the adjacency list.
In some embodiments, mapping the classified node data to a decimal color value, and pre-arranging the classification index in a grid, and calculating a coordinate value of each node data on the grid in a cartesian rectangular coordinate system includes:
performing index traversal on the cdlpMap to generate the classification index and a first array;
traversing the first array to acquire the current index information and node type of the node;
and calculating to obtain coordinate values under a Cartesian rectangular coordinate system corresponding to the nodes according to the classification indexes and the current index information, and calculating to obtain decimal color values corresponding to the nodes currently according to the classification indexes.
In some embodiments, performing a visual graph rendering on each node according to the coordinate values, and binding an event includes:
traversing and rendering each node through a fillArc function of canvas;
and generating a corresponding index color through a color-tracker algorithm according to the index of each node, recording the index color into an adjacency list, and gathering all nodes through a d3-force physical engine to obtain the graph data display form under the macroscopic angle.
In some of these embodiments, after obtaining the graph data presentation shape at a macroscopic angle, the method comprises:
carrying out node relation degree query by constructing database sentences to obtain node relation degrees;
and carrying out size normalization on the node relation degree, and redrawing the graphs of all the nodes through a canvas API (application programming interface) to obtain a graph data display form under a microscopic angle.
In some embodiments, the normalizing the node relationships in size and the graphically redrawing all nodes through the canvas API includes:
acquiring a subgraph relationship degree in a current canvas by constructing create graph and from graph grammars of a graph database, and dynamically generating a node size according to the access degree of nodes in a subgraph;
traversing all nodes, acquiring the maximum value and the minimum value of the relationship degree of all the nodes, normalizing the nodes to the range of the node size, and storing the nodes in node data;
when the graphs of all the nodes are redrawn through the canvas API, the square root of the node size is calculated to obtain a smoother node radius, and the node radius is used for drawing.
In a second aspect, an embodiment of the present application provides a system for visually displaying big data, where the system includes:
the acquisition and analysis module is used for acquiring offline sub-graph data through an http request, analyzing the offline sub-graph data and sorting all node data in the offline sub-graph data;
the pre-layout module is used for mapping the classified node data to decimal color values, carrying out grid pre-layout on the classification indexes and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system;
and the rendering and drawing module is used for performing visual graph rendering on each node according to the coordinate values, binding events and obtaining a graph data display form under a macroscopic angle.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the processor implements the method for visualizing and displaying big data as described in the first aspect.
In a fourth aspect, the present application provides a storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the method for visualizing presentation of big data as described in the first aspect above.
Compared with the related technology, the big data visualization display method provided by the embodiment of the application obtains the offline sub-graph data through the http request, analyzes the offline sub-graph data, and sorts and orders all node data in the offline sub-graph data; mapping the classified node data to decimal color values, performing grid pre-layout on the classification indexes, and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system; and performing visual graph rendering on each node according to the coordinate values, and binding events to obtain a graph data display form under a macroscopic angle.
According to the method and the device, the large data are visually displayed from a macroscopic view, free switching from macroscopic view to microscopic view is realized, the image data mining from top to bottom and from macroscopic view to microscopic view is realized, the working efficiency of an analyst and the capability of mining the data value from the image data can be effectively improved, and the problems of information redundancy and inaccurate decision existing when the large data in the image database are subjected to data mining are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of an application environment of a method for visualizing presentation of big data according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for visualization of big data according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a graph data presentation from a macroscopic perspective according to an embodiment of the present application;
FIG. 4 is a diagram illustrating a morphology of image data at a microscopic angle according to an embodiment of the present application;
FIG. 5 is a block diagram of a system for visualization of big data according to an embodiment of the present application;
fig. 6 is an internal structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly and implicitly understood by one of ordinary skill in the art that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The method for visually displaying big data provided by the present application can be applied to the application environment shown in fig. 1, and fig. 1 is an application environment schematic diagram of the method for visually displaying big data according to the embodiment of the present application, as shown in fig. 1. The terminal 11 and the server 10 communicate with each other via a network. The server 10 acquires the offline sub-graph data through the http request, analyzes the offline sub-graph data, and sorts and orders all node data in the offline sub-graph data; mapping the classified node data to decimal color values, performing grid pre-layout on the classification indexes, and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system; and performing visual graph rendering on each node according to the coordinate values, binding events, obtaining a graph data display form under a macroscopic angle, and displaying the graph data display form on the equipment 11. The terminal 11 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 10 may be implemented by an independent server or a server cluster formed by a plurality of servers.
The present embodiment provides a method for visually displaying big data, and fig. 2 is a flowchart of the method for visually displaying big data according to the embodiment of the present application, and as shown in fig. 2, the flowchart includes the following steps:
step S201, obtaining off-line sub-graph data through an http request, analyzing the off-line sub-graph data, and sorting all node data in the off-line sub-graph data;
preferably, in this embodiment, before obtaining the offline sub-graph data through the http request, first, the graph database is connected, a corresponding data table in the graph database is selected and obtained, a sub-graph of the spatial full-graph data is created through the graph database syntax, and the sub-graph is computed offline, so as to obtain the offline sub-graph data.
Then, the page acquires the obtained offline sub-graph data through an http request;
further, analyzing the offline sub-graph data, and constructing and obtaining corresponding JSON data through a regular expression, namely constructing node data in the offline sub-graph data into JSON data;
then, all the node data are sorted and sorted, wherein the specific steps comprise:
s1, constructing an adjacency list;
s2, sorting the classified cdlp (unity detection label propagation) values of all the node data;
s3, classifying and aggregating all node data into different arrays according to the classified cdlp values, generating cdlpMaps and storing the cdlpMaps into an adjacency list;
step S202, mapping the classified node data to a decimal color numerical value, performing grid pre-layout on the classification index, and calculating a coordinate value of each node data on the grid under a Cartesian rectangular coordinate system;
preferably, in this embodiment, the grid pre-layout and coordinate value calculation are performed on the classified node data,
specifically, first, index traversal is performed on the cdlpMap generated in step S201, and a classification index and a first array are generated at the same time;
traversing the first array to obtain the specific current index information subIndex of a certain node and the type of the node;
then, performing self-defined calculation through the classification index and the current index information subIndex, for example, multiplying the self-defined ratios respectively to calculate coordinate values under a Cartesian rectangular coordinate system corresponding to the node, and further, calculating through the classification index to obtain a decimal color value currently corresponding to the node;
and S203, performing visual graph rendering on each node according to the coordinate values, and binding events to obtain a graph data display form under a macroscopic angle.
Fig. 3 is a schematic diagram of a graph data display form under a macro angle according to an embodiment of the present application, and as shown in fig. 3, in this embodiment, a visual image is rendered for each node according to the obtained coordinate value, and a corresponding event is bound, so as to achieve a purpose of drawing the graph data display form under the macro angle.
Specifically, first, each node is traversed and rendered through the fillArc function of canvas;
then generating a corresponding index color through a color-tracker algorithm according to the index of each node, and recording the index color into an adjacency list to realize event binding; rendering the showdcanvas according to the index color, when a user clicks the canvas, acquiring the index color of the corresponding coordinate on the showdcanvas according to the event response coordinate, and acquiring the node under the response coordinate through the index color;
meanwhile, in the embodiment, all nodes are gathered through the d3-force physics engine to obtain the graph data display form under the macro angle. Specifically, a d3-force algorithm is called, centripetal force, collision force and multi-body force are applied by using d3-force to gather all nodes, the maximum frame number of d3-force is configured to be 50, d3-force monitoring is operated, fx of all nodes is set as the x value of the nodes, the nodes are fixed, and finally the macro drawing effect shown in fig. 3 is obtained. And the analysis and the mining of the graph data under the macroscopic angle are realized.
In some embodiments, after obtaining the graph data display form under the macro angle, the user may click a node to realize the macro-to-micro switching, which includes the following specific steps:
firstly, taking out corresponding node data from an adjacency list according to an index color;
then, constructing a corresponding graph database query grammar through the node vid, and performing node relation degree query to obtain the node relation degree;
finally, by judging whether the current view is in a view data display form under a macroscopic angle or not, if so, emptying data of the global view, resetting the data in the current view and rendering to obtain a microscopic sub-image view; and when the judgment result is not yes, performing different operations, namely the current view is the display form of the image data under the microscopic angle.
Fig. 4 is a schematic view of the image data display form at the microscopic angle according to the embodiment of the present application, and as shown in fig. 4, the step of drawing the image data display form at the microscopic angle in the embodiment includes:
after the node relation degree is obtained, obtaining the subgraph relation degree in the current canvas by constructing the create graph grammar and the from graph grammar of the graph database, and dynamically generating the node size according to the in-out degree of the nodes in the subgraph;
then, traversing all nodes, acquiring the maximum value and the minimum value of the relationship degree of all the nodes, normalizing the nodes to the range of the node size, and storing the nodes in node data;
then, when the graphs of all the nodes are redrawn through the canvas API, calculating the square root of the node size to obtain a smoother node radius, and drawing through the node radius;
and finally, drawing to obtain a diagram data display form under a microscopic angle.
It should be noted that different connecting lines between nodes in fig. 4 indicate node relationships between nodes, for example, like indicates a preference, team represents team friends, serve indicates service, and the like.
Through the steps S201 to S203, the embodiment not only performs the visual display of the big data from the macroscopic perspective, but also realizes the free switching from the macroscopic view to the microscopic view, and from top to bottom, and from the macroscopic view to the microscopic view data mining, so that the working efficiency of the analyst and the capability of mining the data value from the view data can be effectively improved, and the problems of information redundancy and inaccurate decision when the big data in the graph database is mined are solved.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams 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 illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment also provides a system for visualizing and displaying big data, which is used for implementing the above embodiments and preferred embodiments, and the description of the system that has been already made is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a big data visualization system according to an embodiment of the present application, and as shown in fig. 5, the system includes an acquisition parsing module 51, a pre-layout module 52, and a rendering and drawing module 53:
the obtaining and analyzing module 51 is configured to obtain offline sub-graph data through an http request, analyze the offline sub-graph data, and sort all node data in the offline sub-graph data; a pre-layout module 52, configured to map the classified node data to a decimal color value, perform grid pre-layout on the classification index, and calculate a coordinate value of each node data on the grid in a cartesian rectangular coordinate system; and the rendering and drawing module 53 is configured to perform visual graph rendering on each node according to the coordinate values, and bind an event to obtain a graph data display form under a macroscopic angle.
Through the system, the embodiment not only performs visual display of big data from a macroscopic angle, but also realizes free switching from macroscopic to microscopic, and from top to bottom and from macroscopic to microscopic image data mining, so that the working efficiency of an analyst and the capability of mining data values from image data can be effectively improved, and the problems of information redundancy and inaccurate decision when the big data in a graph database is subjected to data mining are solved.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
It should be noted that the above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the above modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
The present embodiment also provides an electronic device, comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the steps of any of the method embodiments described above.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
In addition, in combination with the method for visualizing and displaying big data in the foregoing embodiments, the embodiments of the present application may provide a storage medium to implement. The storage medium has a computer program stored thereon; the computer program, when executed by a processor, implements any one of the above-described methods for visualizing big data.
In one embodiment, a computer device is provided, which may be a terminal. The computer device comprises a processor, a memory, a network interface, a display screen and an input device which are connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for visualization of big data. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
In an embodiment, fig. 6 is a schematic internal structure diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 6, there is provided an electronic device, which may be a server, and its internal structure diagram may be as shown in fig. 6. The electronic device comprises a processor, a network interface, an internal memory and a non-volatile memory connected by an internal bus, wherein the non-volatile memory stores an operating system, a computer program and a database. The processor is used for providing calculation and control capability, the network interface is used for communicating with an external terminal through network connection, the internal memory is used for providing an environment for an operating system and the running of a computer program, the computer program is executed by the processor to realize a method for large data visual display, and the database is used for storing data.
Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application, and does not constitute a limitation on the electronic device to which the present application is applied, and a particular electronic device may include more or less components than those shown in the drawings, or may combine certain components, or have a different arrangement of components.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. A method for visually displaying big data, which is characterized by comprising the following steps:
obtaining offline sub-graph data through an http request, analyzing the offline sub-graph data, and sorting all node data in the offline sub-graph data, specifically comprising: constructing an adjacency list, sequencing the classified cdlp values of all the node data, classifying and aggregating all the node data into different arrays according to the classified cdlp values, generating cdlpMap and storing the cdlpMap in the adjacency list;
mapping the classified node data to decimal color values, performing grid pre-layout on the classification indexes, and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system;
performing visual graph rendering on each node according to the coordinate values, and binding events to obtain a graph data display form under a macroscopic angle, wherein the specific steps comprise: traversing and rendering each node through a fillArc function of canvas; and generating a corresponding index color through a color-tracker algorithm according to the index of each node, recording the index color into an adjacency list, and gathering all nodes through a d3-force physical engine to obtain the graph data display form under the macroscopic angle.
2. The method of claim 1, wherein before obtaining offline subgraph data via http request, the method comprises:
and connecting the graph database, acquiring a data table in the graph database, creating a subgraph of the spatial total graph data through graph database grammar, and calculating the subgraph in an off-line manner to obtain the off-line subgraph data.
3. The method of claim 1, wherein mapping the sorted node data to decimal color values and pre-arranging the sorting indices into a grid, and wherein calculating coordinate values of each node data on the grid in a cartesian rectangular coordinate system comprises:
performing index traversal on the cdlpMap to generate the classification index and a first array;
traversing the first array to acquire the current index information and node type of the node;
and calculating to obtain coordinate values under a Cartesian rectangular coordinate system corresponding to the nodes according to the classification indexes and the current index information, and calculating to obtain decimal color values corresponding to the nodes currently according to the classification indexes.
4. The method of claim 1, wherein after obtaining the graph data presentation shape at a macroscopic level, the method comprises:
carrying out node relation degree query by constructing database statements to obtain the node relation degree;
and carrying out size normalization on the node relation degree, and redrawing the graphs of all the nodes through a canvas API (application programming interface) to obtain a graph data display form under a microscopic angle.
5. The method of claim 4, wherein the normalizing the node relationships in size and the graphically redrawing all nodes via a canvas API comprises:
acquiring a subgraph relationship degree in a current canvas by constructing create graph and from graph grammars of a graph database, and dynamically generating a node size according to the access degree of nodes in a subgraph;
traversing all nodes, acquiring the maximum value and the minimum value of the relationship degree of all the nodes, normalizing the nodes to the range of the node size, and storing the nodes in node data;
when the graphs of all the nodes are redrawn through the canvas API, the square root of the node size is calculated to obtain a smoother node radius, and the node radius is used for drawing.
6. A system for visualization presentation of big data, the system comprising:
the obtaining and analyzing module is used for obtaining offline sub-graph data through an http request, analyzing the offline sub-graph data, and sorting all node data in the offline sub-graph data, and specifically includes: constructing an adjacency list, sequencing the classified cdlp values of all the node data, classifying and aggregating all the node data into different arrays according to the classified cdlp values, generating cdlpMap and storing the cdlpMap in the adjacency list;
the pre-layout module is used for mapping the classified node data to decimal color values, carrying out grid pre-layout on the classification indexes and calculating the coordinate value of each node data on the grid under a Cartesian rectangular coordinate system;
the rendering and drawing module is used for performing visual graph rendering on each node according to the coordinate values, binding events and obtaining a graph data display form under a macroscopic angle, and the rendering and drawing module specifically comprises the following steps: traversing and rendering each node through a fillArc function of canvas; and generating a corresponding index color through a color-tracker algorithm according to the index of each node, recording the index color into an adjacency list, and gathering all nodes through a d3-force physical engine to obtain the graph data display form under the macroscopic angle.
7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the method for visualizing presentation of big data according to any of claims 1 to 5.
8. A storage medium, in which a computer program is stored, wherein the computer program is configured to execute the method of visually presenting big data according to any one of claims 1 to 5 when running.
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| CN116502716B (en) * | 2023-06-27 | 2023-09-26 | 深圳大学 | Knowledge graph layout method, device, equipment and medium |
| CN116630567B (en) * | 2023-07-24 | 2023-09-29 | 中国电子科技集团公司第十五研究所 | Geometric modeling and rendering method for ellipsoidal route slice of digital earth |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111061921A (en) * | 2019-12-04 | 2020-04-24 | 智器云南京信息科技有限公司 | Image layout implementation method, system, terminal equipment and storage medium |
| CN112148932A (en) * | 2020-10-12 | 2020-12-29 | 平安科技（深圳）有限公司 | Visualization method, system, computer device and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113010612B (en) * | 2021-03-02 | 2024-04-12 | 中国工商银行股份有限公司 | Visual construction method, query method and device for graph data |
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