CN111008284A - Method and device for executing atlas analysis and service system thereof - Google Patents
Method and device for executing atlas analysis and service system thereof Download PDFInfo
- Publication number
- CN111008284A CN111008284A CN201911204645.6A CN201911204645A CN111008284A CN 111008284 A CN111008284 A CN 111008284A CN 201911204645 A CN201911204645 A CN 201911204645A CN 111008284 A CN111008284 A CN 111008284A
- Authority
- CN
- China
- Prior art keywords
- knowledge template
- knowledge
- template
- data
- graph
- 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.)
- Granted
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
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/338—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to a method and a device for executing atlas analysis and a service system thereof. The map service system comprises a database, a first knowledge template, a second knowledge template and a map data control interface, and the method comprises the following steps: displaying the first knowledge template and the second knowledge template to associate a first set of data tables with a second set of data tables through the graph data control interface, wherein the first knowledge template and the second knowledge template include respective first and second sides; visually connecting a second side of the first knowledge template and a first side of the second knowledge template; and analyzing the association parameters of the first group of data tables and the second group of data tables according to the first knowledge template and the second knowledge template which establish the visual connection. The present invention can provide for analysis and preservation of models to increase reusability of graph computation logic.
Description
Technical Field
The present disclosure relates generally to the field of databases. More particularly, the present disclosure relates to a method, apparatus, and service system for performing a spectrum analysis through a spectrum service system.
Background
At present, a knowledge graph is mainly based on a graph traversal technical scheme, a database provides corresponding interface specifications, an application layer configures related parameters according to different service scenes, and corresponding entities and relationships and related paths and shortest paths are retrieved through graph traversal. However, in the current stage, most graph traversal languages are relatively complex, are not intuitive for users, and are difficult to use and understand. Furthermore, graph traversal is equivalent to one data retrieval, and finally returned result data does not save the logic of traversal. The reusability is poor because there is no concept of a model.
In summary, the prior art is not intuitive to use and cannot save every set relationship as a model for reuse.
Disclosure of Invention
To at least partially solve the technical problems mentioned in the background, aspects of the present disclosure provide a method, an apparatus, and a service system thereof for performing a spectrum analysis through a spectrum service system.
In one aspect, the present disclosure provides a method of performing graph analysis by a graph service system, wherein the graph service system comprises a database, a first knowledge template, a second knowledge template, and a graph data control interface, the method comprising: displaying the first knowledge template and the second knowledge template to associate a first set of data tables with a second set of data tables through the graph data control interface, wherein the first knowledge template and the second knowledge template include respective first and second sides; visually connecting a second side of the first knowledge template and a first side of the second knowledge template; and analyzing the association parameters of the first group of data tables and the second group of data tables according to the first knowledge template and the second knowledge template which establish the visual connection.
In one embodiment, the method further comprises reading a constraint to analyze the associated parameters of the first data table and the second data table after visually connecting the second side of the first knowledge template and the first side of the second knowledge template.
In another embodiment, the method further comprises storing the association parameters of the first set of data tables and the second set of data tables in a storage template of the database together with the restriction condition.
In another aspect, the present disclosure provides a map analysis apparatus applied to a map service system, wherein the map service system includes a map database, the map analysis apparatus including: a database; a first set of data tables and a second set of data tables; a graph data control interface configured to display a first knowledge template and a second knowledge template to associate with the first set of data sheets and the second set of data sheets, the first and second knowledge templates each including a first side and a second side, the second side of the first knowledge template configured to visually connect with the first side of the second knowledge template; and an analysis module configured to analyze the association parameters of the first set of data tables and the second set of data tables based on the first knowledge template and the second knowledge template that have established a visual connection.
In one embodiment, the database further comprises a storage module configured to store the associated parameters and constraints of the first set of data tables and the second set of data tables.
In another embodiment, the parsing module employs the syntax of the Gremlin language and is configured to translate data in the first data table and the second data table and transmit the translated data to the graph database.
In yet another embodiment, the graph database is JanusGraph or HugeGraph.
In yet another aspect, the present disclosure provides a graph service system including a graph analyzing apparatus and a graph database, wherein the graph analyzing apparatus includes: a database configured to store a first set of data tables and a second set of data tables; a graph data control interface configured to display a first knowledge template and a second knowledge template to establish an association with the first set of data tables and the second set of data tables, the first knowledge template and the second knowledge template both including a first side and a second side, the second side of the first knowledge template configured to be visually connected to the first side of the second knowledge template; and an analysis module configured to analyze the association parameters of the first set of data tables and the second set of data tables based on the first knowledge template and the second knowledge template that have established a visual connection.
In one embodiment, the database further includes a storage module for storing the associated parameters of the first set of data tables and the second set of data tables and the limiting conditions.
In another embodiment, the parsing module is translated using the grammar of the Gremlin language.
By the method, the device and the service system disclosed by the invention and the multiple embodiments thereof, the use threshold of the atlas analysis function in the atlas application can be reduced, and the atlas can be intuitively and effectively explored and inferred according to the service by using the dragging operation. In addition, the disclosed solution also provides for analysis and preservation of the model, thus also increasing the reusability of the graph computation logic.
Drawings
The above and other objects, features and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar or corresponding parts and in which:
FIG. 1 is a block diagram illustrating a graph service system according to an embodiment of the present disclosure;
FIG. 2 is a set-up page schematic diagram illustrating a graph data control interface according to an embodiment of the present disclosure;
FIG. 3 is a control page schematic diagram illustrating a graph data control interface according to an embodiment of the present disclosure;
FIG. 4 is a flow chart illustrating a method of profiling according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating a knowledge template according to another embodiment of the present disclosure;
FIG. 6 is a first control page schematic diagram illustrating a graph data control interface according to another embodiment of the present disclosure;
FIG. 7 is a second control page schematic diagram illustrating a graph data control interface according to another embodiment of the present disclosure;
FIG. 8 is a schematic diagram illustrating model analysis of a graph database according to an embodiment of the present disclosure; and
fig. 9 is a flowchart illustrating a profile analysis method according to another embodiment of the present disclosure.
Detailed Description
The technical scheme of the disclosure provides a method and a device for performing atlas analysis through an atlas service system and the service system thereof. Different from the technical scheme in the prior art, the method provides a dragging type atlas analysis technology, and a user can form a flow template only by dragging a knowledge template to be analyzed to a graphic panel according to a business flow and configuring the relationship type among the templates, so that the analysis and calculation can be performed on the whole business flow. The current business process can be stored as an analysis model, and then multiplexing is realized on the same business scene. Because the bottom graph traversal technology is shielded, the user at the front end only needs to interact with the graphical interface, so that the method is more popular and easier to understand, is efficient and convenient to use, and improves the working efficiency.
The technical solution of the present disclosure and various embodiments thereof will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a block diagram illustrating a graph service system 100 according to an embodiment of the present disclosure. As shown in fig. 1, the graph service system 100 may include a graph analysis device 110 and a graph database 120, wherein the graph analysis device 110 may include a database 210, and the database 210 includes one or more storage modules 212 therein, which may be configured to store a plurality of data tables corresponding to a plurality of knowledge templates. In one embodiment, the plurality of data tables may include a first set of data tables and a second set of data tables, wherein the first set of data tables corresponds to the first knowledge template and the second set of data tables corresponds to the second knowledge template.
Further, the atlas analysis device 110 also includes a graph data control interface 220 that may receive input from a database, which may be configured to display the first knowledge template and the second knowledge template. The data control interface 220 will be described below in conjunction with fig. 2.
Fig. 2 shows a setup homepage of the graph data control interface 222 according to an embodiment of the disclosure, which includes or displays a plurality of knowledge templates, such as a first knowledge template, a second knowledge template, a third knowledge template … …, an nth knowledge template (N is an ordinal number of a knowledge template, and is a positive integer), and the like, wherein each knowledge template is built according to different properties, so that each knowledge template stores a plurality of data tables according to the difference of the properties, and the same data table may cover a plurality of properties, and therefore, the data tables in the plurality of knowledge templates may have overlapping contents. For example, in one embodiment, a first knowledge template may be business enterprise data related to evading consumption taxes, which may store data table a1, data table b1, data table c1, data table d1 … …, etc., a second knowledge template may be service enterprise data related to evading consumption taxes, which may store data table a2, data table b1, data table c3, data table d2 … …, etc., and a third knowledge template may be business enterprise data related to goods procurement fees, which may store data table a1, data table b3, data table c3, data table d3 … …, etc., and so on.
An exemplary analysis of these knowledge templates is performed by first comparing the first knowledge template to the second knowledge template, wherein the same data table b1 is found, and it is inferred that the companies listed in data table b1 are of the type of business enterprise and service enterprise and are suspected of evading tax. Then, the first knowledge template and the third knowledge template are compared, wherein the same data table a1 can be found, so that it can be inferred that the company listed in the data table a1 belongs to the type of the trade and trade company and is suspected of evading the consumption tax, and whether other items of evading the consumption tax are left can be known from the cross comparison of the goods purchase fee and the consumption tax. Finally, the second knowledge template is compared with the third knowledge template, wherein the same data table c3 can be found, so that it can be inferred that the company listed in data table c3 belongs to the type of commercial business enterprise and service enterprise, and whether there are other tax evasion items can be known from the cross comparison of the goods purchase fee and the consumption tax.
From the above description in conjunction with fig. 2, those skilled in the art will appreciate that the graph data control interface of the present disclosure may include a plurality of knowledge templates (e.g., a first knowledge template and a second knowledge template), and each knowledge template may have a set of data tables associated or corresponding therewith (e.g., the first knowledge template has a first set of data tables and the second knowledge template has a second set of data tables). Through the property and the content of the knowledge template, the relevance details among the data tables can be acquired, so that the subsequent structural analysis processing of a plurality of data tables is facilitated.
Returning to FIG. 1, the atlas analysis device 110 also includes an analysis module 230 that receives input from the map data control interface 220 and outputs to the map database 120 the relationships and conditions needed to create the model. In one embodiment, when the first knowledge template and the second knowledge template are visually connected, the analysis module 230 may analyze the first set of data tables and the second set of data tables according to the first knowledge template and the second knowledge template with the established visual connection, analyze the association parameters of the first set of data tables and the second set of data tables using a graph traversal language, and transmit the translated data of the first set of data tables and the second set of data tables to the database 120. In one embodiment, the present disclosure uses the graph database query language of the Gremlin language to perform the above parsing operations, which may include the following: 1. vertex (Vertex): generally refers to entities such as: people, accounts, etc.; 2. edge (Edge): generally refers to relationships such as: friendship, transfer actions, etc.; 3. property (Property): the vertices or edges may contain attributes such as: name of person, age of person, time of transfer. The constituent relationship parameters of the vertices, edges, and attributes, etc. may be provided to a graph database to create an entity relationship graph. For example, the entity relationship diagram may be a classmate and friendship diagram, a bank transfer diagram, or the like.
The database 210 of the present disclosure may include one or more storage modules 212 for storing the associated parameters and constraints of the first set of data tables and the second set of data tables as model samples for the next analysis, thereby increasing the reusability of the graph computation logic. In one embodiment, the graph database may be a graph database of JanusGraph, HugeGraph …, or the like. Of course, the graph database of the present disclosure is not limited to a specific graph database, and any graph database that can convert the relationship parameters of vertices, edges, attributes, and other components into a data structure such as a "graph" and can store and query hundreds of billions of vertices and edges can be generalized in the embodiments of the present disclosure.
The operation of the knowledge templates on the graph data control interface will be described in detail below with reference to fig. 2 and 3. The description herein is intended to be illustrative, and not restrictive, and is presented only to facilitate an understanding of the disclosed aspects.
FIG. 3 is a control page schematic diagram illustrating a graph data control interface 320 according to an embodiment of the present disclosure. The first knowledge template 3212, the second knowledge template 3214, the third knowledge template 3216, the fifth knowledge template 3218, the seventh knowledge template 3220 and the ninth knowledge template 3222 which may need to be used may be selected in advance on the setting page of the map data control interface 222 disclosed in fig. 2. The selected knowledge template may be displayed in the first display area 3210 of the graph data control interface 320. It is understood that the knowledge template can be expanded at any time according to the requirement, and is not limited to be added in place at the beginning.
The relevant knowledge templates may then be dragged directly into the second display area 3220 according to the target (subject) to be analyzed, and each knowledge template is also not limited to being selected only once. For example, there is only one first knowledge template 3224 in the second display area 3220 in this disclosure, but there may be two second knowledge templates 3226, namely knowledge template 3226_1 and knowledge template 3226_ 2. Further, according to the configuration of the present disclosure, each knowledge template has a first side (also referred to as "in side") and a second side (also referred to as "out side") on both sides, and the association between the two knowledge templates can be established by connecting the first side and the second side of different knowledge templates. In one embodiment, the first side of each knowledge template may be connected to the second side of another knowledge template, directions are input from the first side and output from the second side, and the start point is connected across each side a plurality of times, and the final end point is connected to the initial start point, such that a complete path having a start point and an end point may be formed.
Another manner of operation of the data control interface of the figures will be described below in conjunction with figures 3 and 1. There are two functional options in the graph data control interface 320, save model 3230 and analyze model 3240, according to aspects of the present disclosure. When the function option of "save model" 3230 is selected, the storage module 212 stores the associated parameters of the first set of data table and the second set of data table received from the analysis module 230 and the constraint, so that the model can be used as a model sample for the next analysis to increase the reusability of the graph computation logic. When the functional option of "model analysis" 3240 is selected, the analysis module 230 will receive input from the graph data control interface 220 and output to the graph database 210 the relationships and conditions required to create the model.
Fig. 4 is a flow chart illustrating a graph analysis method 400 according to an embodiment of the present disclosure, it being understood that the graph data control interface described herein may be the graph data control interface of fig. 1, 2, or 3.
As shown in fig. 4, in step 411, the method 400 may display a first knowledge template and a second knowledge template on a setup page at the beginning of the graph data control interface, and the first knowledge template stores a first set of data tables and the second knowledge template stores a second set of data tables. Thus, when the first knowledge template and the second knowledge template are displayed in the setup page of the graph data control interface, the first set of data tables and the second set of data tables may be caused to also be associated. Further, the first knowledge template and the second knowledge template may include respective first side edges and second side edges, and the first knowledge template and the second knowledge template may be associated via the first side edges and the second side edges. In one embodiment, the establishment of the association is directional, such that the second side of the first knowledge template can only establish an association with the first side of the second knowledge template, and the second side of the second knowledge template can only establish an association with the first side of the first knowledge template.
Next, in step 412, the method 400 may visually connect the second side of the first knowledge template and the first side of the second knowledge template by dragging. Here, "visualization" means that the user can easily select the knowledge templates in a dragging manner at will in the control page of the graph data control interface, and can establish the association between the knowledge templates in a linear connection manner. In step 413, the method 400 may analyze the first set of data tables and the second set of data tables for associated parameters based on the first knowledge template and the second knowledge template for which the visual connection has been established.
To further illustrate the principles of the disclosed solution, a technical solution for operation analysis and analysis modeling of the graph data control interface of the present disclosure will be exemplarily described below with reference to fig. 5 to 8. It is to be understood that the specific knowledge modules and their data sheets shown in fig. 5-8 are merely exemplary of implementations of aspects of the present disclosure, and that one skilled in the art, based on the teachings herein, may contemplate the use of knowledge templates and data sheets of other nature to implement aspects of the present disclosure.
First, fig. 5 shows a knowledge template schematic according to another embodiment of the present disclosure. As shown in fig. 5, exemplary specific knowledge templates "tax-first type of enterprise" knowledge template 5222 and "tax-second type of enterprise" knowledge template 5224 represent two different types of enterprise groups, respectively. In one embodiment, the "tax evasion-first type of business" knowledge template 5222 may include three companies, a1, a2, a3, and the like. Similarly, the "tax evasion-second class of business" knowledge template 5224 may include three companies, b1, b2, b3, and so on. Further, the two can be visually connected through a graph data control interface 6220 (shown in fig. 6) of the present disclosure.
FIG. 6 illustrates a first control page schematic of a graph data control interface 620, according to another embodiment of the present disclosure.
As shown in fig. 6, the graph data control interface 620 may include: a first display area 6210 and a second display area 6220, wherein the first display area 6210 is used to present the "tax evasion-first type of business" knowledge template 6212 and the "tax evasion-second type of business" knowledge template 6214 to be analyzed, and the second display area 6220 is an area where the relationship of the knowledge templates is set. As previously described, in operation, a knowledge template to be correlated may be moved directly from the first display area 6210 to the second display area 6220 by clicking or dragging. As shown in the figure, the second display area in this disclosure presents selected "tax evasion-first type of business" knowledge templates 6222 (selected once) and "tax evasion-second type of business" knowledge templates 6224 (selected twice, i.e., "tax evasion-second type of business" knowledge templates 6224_1, 6224_ 2). The "tax evasion-first class enterprise" knowledge template 6222 and the "tax evasion-second class enterprise" knowledge templates 6224_1, 6224_2 each include respective in-edges (as the first side edge) and out-edges (as the second side edge). The relevance between the input edge and the output edge of different knowledge templates can be established by connecting the two. In one embodiment, the incoming edge may be connected to the outgoing edge, and the direction may be from the incoming edge and out of the outgoing edge.
Further, the outbound edge of the "tax evasion-first class enterprise" knowledge template 6222 is connected to the inbound edge of the "tax evasion-second class enterprise" knowledge template 6224_ 1. The out-edge of the "tax evasion-second class of business" knowledge template 6224_1 is then further connected to the in-edge of the "tax evasion-second class of business" knowledge template 6224_ 2. Here, two identical sets of "tax evasion-second class enterprise" knowledge templates 6224_1, 6226_2 are configured to set conditions for two entries of money that were ever present in the "tax evasion-second class enterprise", keeping in mind that the exit edge of the "tax evasion-second class enterprise" knowledge template 6224_2 is connected back to the entry edge of the "tax evasion-first class enterprise" knowledge template 6222, and thus is a complete path with a start point and an end point. In addition, a constraint condition between each knowledge template can be set, wherein the constraint condition can be an item to be analyzed, and the constraint condition is set by clicking a connecting line 6228 between an outgoing edge and an incoming edge of each knowledge template, and each connecting line corresponds to the associated parameter between each knowledge template.
FIG. 7 is a second control page schematic diagram illustrating a graph data control interface according to another embodiment of the present disclosure.
When the connecting line 6228 shown in fig. 6 is clicked, the second display area 6220 has a setting field 7222 of the restriction condition, and the setting field 7222 includes a first field 7224 and a second field 7226, which have different functions. For example, the first field 7224 shows the setup items already built in, and the second field 7226 is filled in by the user as desired. In one embodiment, the second field 7226 can be selected by the user in the form of a drop-down menu. For example, the illustrated limitation is that the relationship type is "goods transaction" and the number of repetitions is "1", wherein the above-described limitation is not limited in number and may be set according to actual needs.
In addition, the present disclosure also provides two functional options, "delete edges" 7228 and "add constraints" 7230. When the user wants to remove the association between knowledge templates, the functional option of "delete edge" 7228 can be selected directly, thereby blocking the association between two knowledge templates. If the user simply wants to add new restrictions, the user can also select the "Add constraints" 7230 option to expand the restrictions.
Returning to FIG. 6, the graph data control interface 620 also includes two functional options, "save model" 6230 and "model analyze" 6240. When the function option of "save model" 6230 is selected, the storage module 212 stores the associated parameters of the first set of data table and the second set of data table received from the analysis module 230 and the limiting conditions, so that the model can be used as a model sample for the next analysis, and the reusability of the graph computation logic is increased. The functional option of "model analysis" 6240 may be selected when the connections between the incoming and outgoing edges of the knowledge template have been established and the constraints are set. The analysis module 230 will receive constraints from the graph data control interface 620 to analyze the first set of data tables and the second set of data tables for associated parameters and output the relationships and conditions needed to create the model to the graph database 210.
FIG. 8 is a schematic diagram 800 illustrating model analysis of a graph database according to an embodiment of the present disclosure. As shown in fig. 8, if the constraint condition is set as "goods transaction", the analysis module 230 will read the constraint conditions of the goods transaction of companies a1, a2, a3, b1, b2 and b3 according to the associated parameters of the knowledge template 6222 of "tax evasion-first type of enterprise" and the knowledge templates 6224_1 and 6224_2 of "tax evasion-second type of enterprise" established by the visual connection, and then analyze the constraint conditions, and finally transmit the analysis results to the database 120 for imaging processing. In the process of traversing a graph by the graph database, the graph database needs to move along a certain relationship (a connecting line 6228 pointing into an edge and a connecting line 6228 pointing out an edge) and a direction, each vertex of the graph is visited one by one, namely, the graph database moves along a certain direction from a starting vertex, and the process of traversing the graph is completed when an ending vertex is reached.
According to the definition of the graph database language, the data table relationship setting of the present disclosure may be by: 1. vertex: tax evasion-first type of business (a1, a2, a 3) and tax evasion-second type of business (b1, b2, b 3), 2. edge: tax evasion-relationship of first class of business to tax evasion-second class of business, and 3. attribute: the method is characterized in that graphical data can be formed by combining the condition limits of the vertexes, the edges and the attributes aiming at goods transaction. The relevance between companies such as a1, a2, a3, b1, b2 and b3 can be more easily understood through model analysis of graph databases.
As shown in fig. 8, the flow of the goods transaction is clearly known from the direction of the connecting line, wherein the a1 company and the a3 company have two-way relations with the two groups of companies, namely the b1 company and the b2 company, which show that the two groups of companies are probably mutually evasive. Further, the b2 company trades goods more frequently, and thus should be listed as an object for which tax evasion is prioritized.
Fig. 9 is a flow chart illustrating a profile analysis method 900 according to another embodiment of the present disclosure. To better describe the flow diagram, the following description will also refer to the various components or modules shown in fig. 1 and 6. It is to be appreciated that the method 900 can be performed by the graph services system 100, as previously described, the graph services system 100 including the database 210, the graph data control interface 220, and the "tax evasion-first class enterprise" knowledge templates 6212 and the "tax evasion-second class enterprise" knowledge templates 6214. The specific flow of the profile analysis method 900 will be described below.
At step 911, the method 900 preselects a "tax evasion-first class of business" knowledge template 6212 and a "tax evasion-second class of business" knowledge template 6214. Next, at step 912, the method 900 displays a "tax evasion-first type of business" knowledge template 6212 and a "tax evasion-second type of business" knowledge template 6214, wherein the first set of data tables and the second set of data tables may be associated via the graph data control interface. At step 913, the method 900 visually connects the "tax evasion-first class of business" knowledge template 6222 and the "tax evasion-second class of business" knowledge templates 6224_1, 6224_2, and both the "tax evasion-first class of business" knowledge template 6222 and the "tax evasion-second class of business" knowledge templates 6224_1, 6224_2 may include respective in-edges and out-edges. For example, as previously described, the outgoing edge of the "tax evasion-first type of business" knowledge template 6222 may be connected to the incoming edge of the "tax evasion-second type of business" knowledge template 6224_1, and the outgoing edge of the "tax evasion-second type of business" knowledge template 6224_1 may be connected to the incoming edge of the "tax evasion-second type of business" knowledge template 6224_ 2. Here, the two identical sets of "tax evasion-second type of business" knowledge templates 66224_1, 6224_2 are configured to set a condition that "tax evasion-second type of business" has two entries and exits of funds. It should be noted that the outgoing edge of the "tax evasion-second class enterprise" knowledge template 6224_2 needs to be connected back to the incoming edge of the "tax evasion-first class enterprise" knowledge template 6222, so that a complete path with an initial and final point can be formed.
Next, in step 914, the method 900 analyzes the first set of data tables and the second set of data tables for associated parameters based on the "tax evasion-first type of business" knowledge template 6222 and the "tax evasion-second type of business" knowledge templates 6224_1, 6224_2 for which visual connections have been established. In step 915, constraints are set based on the visually connected "tax evasion-first class enterprise" knowledge template 6222 and "tax evasion-second class enterprise" knowledge templates 6224_1, 6224_2, where constraints refer to items to be analyzed, the constraints of the present disclosure being the relationship types of the first set of data tables and the second set of data tables. At step 916, the associated parameters and the constraints used to store the first and second sets of data tables may be used as model samples for the next analysis, increasing the reusability of the graph computation logic. At step 917, the parsing module employs the syntax of the Gremlin language, which receives the constraints from the graph data control interface to parse the first set of data tables and the associated parameters of the second set of data tables, and transmits the parsed parameters to the graph database.
Through the above detailed description of the disclosed embodiments with reference to the drawings, those skilled in the art will understand that the present disclosure discloses a method, an apparatus and a service system for performing a spectrum analysis through a spectrum service system. By providing a dragging-type atlas analysis technology, the use threshold of the atlas analysis function in the atlas application can be reduced, the analysis of the atlas data model can be carried out only by operating on the atlas data control interface, dragging the knowledge template to be analyzed according to the business process and configuring the relationship between the templates, and the reusability of the atlas calculation logic can be increased by means of the storage of the model.
The foregoing detailed description of the embodiments of the present disclosure has been presented for purposes of illustration and description and is intended to be exemplary only and is not intended to be exhaustive or to limit the invention to the precise forms disclosed; meanwhile, for the person skilled in the art, based on the idea of the present disclosure, there may be variations in the specific embodiments and the application scope, and in summary, the present disclosure should not be construed as limiting the present disclosure.
It should be understood that the terms "first," "second," "third," and "fourth," etc. in the claims, description, and drawings of the present disclosure are used to distinguish between different objects and are not used to describe a particular order. The terms "comprises" and "comprising," when used in the specification and claims of this disclosure, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the disclosure herein is for the purpose of describing particular embodiments only, and is not intended to be limiting of the disclosure. As used in the specification and claims of this disclosure, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the term "and/or" as used in the specification and claims of this disclosure refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
As used in this specification and claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The foregoing detailed description of the embodiments of the present disclosure has been presented for purposes of illustration and description and is intended to be exemplary only and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Meanwhile, a person skilled in the art should, according to the idea of the present disclosure, change or modify the embodiments and applications of the present disclosure. In view of the above, this description should not be taken as limiting the present disclosure.
Claims (10)
1. A method of performing graph analysis by a graph service system, wherein the graph service system comprises a database, a first knowledge template, a second knowledge template, and a graph data control interface, the method comprising:
displaying the first knowledge template and the second knowledge template to associate a first set of data tables with a second set of data tables through the graph data control interface, wherein the first knowledge template and the second knowledge template include respective first and second sides;
visually connecting a second side of the first knowledge template and a first side of the second knowledge template; and
and analyzing the association parameters of the first group of data tables and the second group of data tables according to the first knowledge template and the second knowledge template which establish the visual connection.
2. The method of claim 1, further comprising:
after visually connecting the second side of the first knowledge template and the first side of the second knowledge template, reading a constraint to analyze the associated parameters of the first data table and the second data table.
3. The method of claim 1, further comprising:
storing the associated parameters of the first and second sets of data tables in a storage template of the database along with the restriction condition.
4. An atlas analysis apparatus applied to an atlas service system, where the atlas service system includes an atlas database, the atlas analysis apparatus comprising:
a database;
a first set of data tables and a second set of data tables;
a graph data control interface configured to display a first knowledge template and a second knowledge template to establish an association with the first set of data sheets and the second set of data sheets, and the first knowledge template and the second knowledge template include respective first and second sides, the second side of the first knowledge template to visually connect with the first side of the second knowledge template; and the number of the first and second groups,
an analysis module configured to analyze the first set of data tables and the second set of data tables for associated parameters based on the first knowledge template and the second knowledge template that have established a visual connection.
5. The atlas analysis apparatus of claim 4, where the database includes a storage module configured to store associated parameters and constraints for the first and second sets of data tables.
6. The graph analysis device according to claim 4, wherein the analysis module is in the grammar of Gremlin and is configured to translate data in the first data table and the second data table and transmit the translated data to the graph database.
7. The map analysis device of claim 6, wherein the graph database is JanusGraph or HugeGraph.
8. A map service system comprising a map analysis apparatus and a map database, wherein the map analysis apparatus comprises:
a database configured to store a first set of data tables and a second set of data tables;
a graph data control interface configured to display a first knowledge template and a second knowledge template to establish an association with the first set of data tables and a second set of data tables, the first knowledge template and the second knowledge template including respective first and second sides, the second side of the first knowledge template to visually connect with the first side of the second knowledge template; and
an analysis module configured to analyze the first set of data tables and the second set of data tables for associated parameters based on the first knowledge template and the second knowledge template that have established a visual connection.
9. The map service system of claim 8, wherein the database comprises a storage module to store the associated parameters and the constraints for the first and second sets of data tables.
10. The graph service system of claim 9, wherein the analysis module translates using the grammar of the Gremlin language.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911204645.6A CN111008284B (en) | 2019-11-29 | 2019-11-29 | Method and device for executing atlas analysis and service system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911204645.6A CN111008284B (en) | 2019-11-29 | 2019-11-29 | Method and device for executing atlas analysis and service system thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111008284A true CN111008284A (en) | 2020-04-14 |
| CN111008284B CN111008284B (en) | 2021-01-12 |
Family
ID=70112517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911204645.6A Active CN111008284B (en) | 2019-11-29 | 2019-11-29 | Method and device for executing atlas analysis and service system thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111008284B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023077730A1 (en) * | 2021-11-08 | 2023-05-11 | 北京邮电大学 | Knowledge graph-based time series-visualized development method and system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104866593A (en) * | 2015-05-29 | 2015-08-26 | 中国电子科技集团公司第二十八研究所 | Database searching method based on knowledge graph |
| CN105868313A (en) * | 2016-03-25 | 2016-08-17 | 浙江大学 | Mapping knowledge domain questioning and answering system and method based on template matching technique |
| CN105893604A (en) * | 2016-04-21 | 2016-08-24 | 成都数联铭品科技有限公司 | Mass associated information processing and visual implementation method |
| US9697192B1 (en) * | 2013-06-28 | 2017-07-04 | Digital Reasoning Systems, Inc. | Systems and methods for construction, maintenance, and improvement of knowledge representations |
| US20180210913A1 (en) * | 2017-01-23 | 2018-07-26 | International Business Machines Corporation | Crowdsourced discovery of paths in a knowledge graph |
| CN109598509A (en) * | 2018-10-17 | 2019-04-09 | 阿里巴巴集团控股有限公司 | The recognition methods of risk clique and device |
| CN109857870A (en) * | 2019-01-03 | 2019-06-07 | 成都数联铭品科技有限公司 | Visual Ontology for knowledge mapping constructs system and method |
| CN109857917A (en) * | 2018-12-21 | 2019-06-07 | 中国科学院信息工程研究所 | Towards the security knowledge map construction method and system for threatening information |
| CN110297872A (en) * | 2019-06-28 | 2019-10-01 | 浪潮软件集团有限公司 | A kind of building, querying method and the system of sciemtifec and technical sphere knowledge mapping |
| CN110414987A (en) * | 2019-07-18 | 2019-11-05 | 中国工商银行股份有限公司 | Recognition methods, device and the computer system of account aggregation |
-
2019
- 2019-11-29 CN CN201911204645.6A patent/CN111008284B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9697192B1 (en) * | 2013-06-28 | 2017-07-04 | Digital Reasoning Systems, Inc. | Systems and methods for construction, maintenance, and improvement of knowledge representations |
| CN104866593A (en) * | 2015-05-29 | 2015-08-26 | 中国电子科技集团公司第二十八研究所 | Database searching method based on knowledge graph |
| CN105868313A (en) * | 2016-03-25 | 2016-08-17 | 浙江大学 | Mapping knowledge domain questioning and answering system and method based on template matching technique |
| CN105893604A (en) * | 2016-04-21 | 2016-08-24 | 成都数联铭品科技有限公司 | Mass associated information processing and visual implementation method |
| US20180210913A1 (en) * | 2017-01-23 | 2018-07-26 | International Business Machines Corporation | Crowdsourced discovery of paths in a knowledge graph |
| CN109598509A (en) * | 2018-10-17 | 2019-04-09 | 阿里巴巴集团控股有限公司 | The recognition methods of risk clique and device |
| CN109857917A (en) * | 2018-12-21 | 2019-06-07 | 中国科学院信息工程研究所 | Towards the security knowledge map construction method and system for threatening information |
| CN109857870A (en) * | 2019-01-03 | 2019-06-07 | 成都数联铭品科技有限公司 | Visual Ontology for knowledge mapping constructs system and method |
| CN110297872A (en) * | 2019-06-28 | 2019-10-01 | 浪潮软件集团有限公司 | A kind of building, querying method and the system of sciemtifec and technical sphere knowledge mapping |
| CN110414987A (en) * | 2019-07-18 | 2019-11-05 | 中国工商银行股份有限公司 | Recognition methods, device and the computer system of account aggregation |
Non-Patent Citations (2)
| Title |
|---|
| JUAN GÓMEZ-ROMERO: "Visualizing large knowledge graphs: A performance analysis", 《FUTURE GENERATION COMPUTER SYSTEMS》 * |
| 汤庸: "知识图谱及其在学术信息服务领域的应用", 《华南师范大学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023077730A1 (en) * | 2021-11-08 | 2023-05-11 | 北京邮电大学 | Knowledge graph-based time series-visualized development method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111008284B (en) | 2021-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10853383B2 (en) | Interactive parallel coordinates visualizations | |
| US10459938B1 (en) | Punchcard chart visualization for machine data search and analysis system | |
| US11037342B1 (en) | Visualization modules for use within a framework for displaying interactive visualizations of event data | |
| US8839144B2 (en) | Add and combine reports | |
| US9378524B2 (en) | Object-oriented time series generator | |
| US8726177B2 (en) | Systems and methods for generating a database query using a graphical user interface | |
| US9031981B1 (en) | Search around visual queries | |
| US9697110B1 (en) | Codeless system and tool for testing applications | |
| US7734619B2 (en) | Method of presenting lineage diagrams representing query plans | |
| CN110019560A (en) | A kind of querying method and device of knowledge based map | |
| US20150106708A1 (en) | Capturing navigations, explorations, and analysis | |
| US10769721B2 (en) | Intelligent product requirement configurator | |
| US9466041B2 (en) | User selected flow graph modification | |
| US20170109335A1 (en) | Authoring visual representations for text-based documents | |
| US10861202B1 (en) | Sankey graph visualization for machine data search and analysis system | |
| EP2409244A1 (en) | Methods and systems for preserving and accessing information related to decision-making | |
| CN113377486A (en) | Data visualization display method, device, equipment and storage medium | |
| US11948116B2 (en) | Systems and methods for risk data navigation | |
| US20170212942A1 (en) | Database grid search methods and systems | |
| WO2018013518A1 (en) | Image-based shopping system | |
| KR20180005088A (en) | Method and computer program for designing a query | |
| CN109815346A (en) | A kind of method for visualizing and device being associated with map | |
| CN111008284B (en) | Method and device for executing atlas analysis and service system thereof | |
| US20170039741A1 (en) | Multi-dimensional visualization | |
| US20130093771A1 (en) | Modified flow graph depiction |
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 |