CN111898004A - Data mining method and device, electronic equipment and readable storage medium thereof - Google Patents
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Abstract
The present invention relates to the field of computer technologies, and in particular, to a data mining method and apparatus, an electronic device, and a readable computer storage medium. The method comprises the steps of defining transaction entity information and transaction relation information by receiving a transaction knowledge graph of a target subject, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction; and acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information. Based on the scheme, the distributed computing cluster can be utilized, the high-efficiency data mining speed is realized by utilizing the existing data mining model according to the defined entity information and the relationship information, the problem that the prior art excessively depends on expert experience is reduced, and the transaction data mining speed is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data mining method, apparatus, electronic device, and readable computer storage medium
Background
The knowledge graph expresses the relationship between the nodes by using a graph consisting of the nodes and the relationship, and has the characteristics of intuition, naturalness, directness and high efficiency. In the financial field, knowledge maps of types such as credit card anti-fraud maps, transaction knowledge maps, anti-money laundering knowledge maps, credit knowledge maps, internal control knowledge maps, enterprise knowledge maps and the like can be constructed according to different services, and then applications such as risk assessment, anti-money laundering, anti-fraud and the like can be carried out on the basis of the knowledge maps, wherein the applications based on the knowledge maps mainly realize service logic data mining by searching for a certain defined characteristic relationship. However, at present, the mining of the service data is mainly found by expert experience, and usually some target features are found according to some features limited to the relation in the knowledge graph, such as the features of the access degree, the centrality, the clustering coefficient and the like, and then the service judgment is combined, so that the timeliness is poor and the high dependency on the experience of personnel exists.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides a data mining method, where the method includes:
receiving a transaction knowledge-graph of a target subject,
defining transaction entity information and transaction relation information, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction;
and acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
Optionally, the method further comprises storing the received transaction knowledge graph in an ORC format in a distributed system.
Optionally, the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
Optionally, the obtaining of the target entity in the transaction knowledge graph by using the selected knowledge graph mining model is specifically obtaining the target entity by using a distributed architecture model; wherein further comprising:
uniformly distributing the entities in the transaction knowledge graph to each device in a computing cluster;
and in the computing cluster, identifying entities with the similarity meeting a threshold value in the transaction knowledge graph by using a target iterative algorithm and acquiring target entities.
On the other hand, the embodiment of the invention also provides a data mining device, which comprises a receiving module, a storage module, a definition module and a processing module, wherein the storage module is used for storing the definition module;
the receiving module is used for receiving the transaction knowledge graph of the target subject,
the defining module is used for defining transaction entity information and transaction relation information, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction;
the storage module is used for storing the transaction knowledge spectrogram and defined transaction entity information and transaction relationship information;
and the processing module is used for acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
Optionally, the storage module is further configured to store the received transaction knowledge graph in a distributed system in an ORC format.
Optionally, the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
Optionally, the processing module is further configured to obtain the target entity by using a distributed architecture model; the processing module further comprises an allocation unit and a calculation unit, wherein:
the distribution unit is used for uniformly distributing the entities in the transaction knowledge graph to each device in the computing cluster;
and the computing unit is used for identifying the entity with the similarity meeting the threshold in the transaction knowledge graph by using a target iterative algorithm and acquiring a target entity.
On the other hand, the embodiment of the invention also provides an electronic device, which comprises a display, a processor and a memory; wherein the content of the first and second substances,
the memory is used for storing operation instructions;
the processor is configured to execute the method according to any one of the embodiments by calling the operation instruction.
In another aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored on the storage medium, and the computer program, when executed by a processor, implements the method described in any of the above embodiments.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
according to the scheme provided by the embodiment of the application, transaction entity information and transaction relation information are defined by receiving a transaction knowledge graph of a target main body, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of transaction; and acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information. Based on the scheme, the distributed computing cluster can be utilized, the high-efficiency data mining speed is realized by utilizing the existing data mining model according to the defined entity information and the relationship information, the problem that the prior art excessively depends on expert experience is reduced, and the transaction data mining speed is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a data mining method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data mining device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, 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 will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 shows a schematic flowchart of a data mining method provided in an embodiment of the present application, and as shown in fig. 1, the method includes:
step S101, receiving a transaction knowledge graph of a target subject,
step S102, transaction entity information and transaction relation information are defined, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of transaction;
and S103, acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
In alternative embodiments, the transaction entity information may also include geographic location information of the entity, device number information of the entity, and other information that may be used by the entity to handle business reservations or necessities in the field of financial transactions. The transaction relationship information may also include the flow direction of the transaction, the amount of money involved in the transaction, the time and frequency of occurrence of the transaction, remark information of the transaction, or other business information of the transaction bid.
Optionally, the method further comprises storing the received transaction knowledge graph in an ORC format in a distributed system.
Optionally, the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
Optionally, the obtaining of the target entity in the transaction knowledge graph by using the selected knowledge graph mining model is specifically obtaining the target entity by using a distributed architecture model; wherein further comprising: uniformly distributing the entities in the transaction knowledge graph to each device in a computing cluster; and in the computing cluster, identifying entities with the similarity meeting a threshold value in the transaction knowledge graph by using a target iterative algorithm and acquiring target entities. Compared with the prior art, the technical scheme of the embodiment can utilize a distributed computing mining mode to facilitate mining of the knowledge graph with richer content and wider range.
To introduce the technical solution of the present application more clearly, a concept of a sub-diagram is introduced to introduce the technical solution in combination with the specific embodiment. In the financial transaction knowledge graph, the sub-graph refers to a graph in which a node set and an edge set of a graph in a financial transaction knowledge graph are respectively a subset of a node set and a subset of an edge set of a certain graph, where the node is the transaction entity, and the edge is the transaction relationship, and in short, a set formed by the transaction entity and the transaction relationship is a sub-graph, that is, a minimum unit in the knowledge graph. In the business of money laundering in the financial industry, credit card fraud and the like, whether money laundering or fraud suspicion exists is often judged by searching for some subgraphs with certain characteristics. In this embodiment, a sub-graph structure and a constraint, that is, an entity and a relationship are defined according to a mining purpose. At the same time, a transaction knowledge graph of the target subject, such as the transaction knowledge graph of bank a, is obtained and metadata and sub-graph structure definitions of the transaction knowledge graph are stored using a MySQL database system, storing transaction knowledge graph data in distributed system (HDFS) in ORC format. Reading knowledge map metadata and a sub-graph structure (namely transaction entity information and transaction relation information) needing to be mined from MySQL by using a selected knowledge map mining model, reading transaction knowledge map data from a distributed system HDFS, and uniformly distributing entities in the transaction knowledge map to each device in a computing cluster by using a synchronous Parallel computing model (BulkSynchronous Parallel, abbreviated as BSP) and using Giraph as a distributed BSP model computing engine; in the computing cluster, identifying entities with similarity meeting a threshold in the transaction knowledge graph by using a target iterative algorithm and acquiring target entities, wherein the sub-graphs with similarity meeting the threshold identified by the iterative algorithm can be divided into three steps: firstly, equipment on each distributed node in a computing cluster is utilized to compute messages required to be sent by each transaction entity in a transaction knowledge graph in parallel, and the messages are sent to related adjacent transaction entities; and secondly, filtering and merging the received messages by each transaction entity in the transaction knowledge Graph according to the defined entity information and relationship information serving as limiting conditions, and calculating the similarity between the currently mined sub-Graph structure (comprising the entity information and the relationship information of the transaction) and the user-defined sub-Graph structure (comprising the defined entity information and the relationship information) by using a Graph Kernel (Graph Kernel) algorithm after all the messages are collected. Determining whether to continue iteration and send a synchronization message to a main transaction entity after each entity node in the transaction knowledge graph is calculated; and thirdly, the main transaction entity in the distributed system starts the next iteration after receiving the synchronous information of the calculation completion of all the related transaction entities. And after the maximum iteration times are reached, the selected knowledge graph mining model outputs all similar sub-graph structures with the similarity meeting the threshold value to the distributed system, so that all target entities meeting the definition are mined from the transaction knowledge graph.
Based on the same principle as the method shown in fig. 1, fig. 2 shows that the embodiment of the present application provides a data mining apparatus, as shown in fig. 2, the apparatus includes a 201 receiving module, a 201 storing module, a 203 defining module, and a 204 processing module, wherein;
the receiving module is used for receiving the transaction knowledge graph of the target subject,
the defining module is used for defining transaction entity information and transaction relation information, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction;
the storage module is used for storing the transaction knowledge spectrogram and defined transaction entity information and transaction relationship information;
and the processing module is used for acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
Optionally, the storage module is further configured to store the received transaction knowledge graph in a distributed system in an ORC format.
Optionally, the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
Optionally, the processing module is further configured to obtain the target entity by using a distributed architecture model; the processing module further comprises an allocation unit and a calculation unit, wherein:
the distribution unit is used for uniformly distributing the entities in the transaction knowledge graph to each device in the computing cluster;
and the computing unit is used for identifying the entity with the similarity meeting the threshold in the transaction knowledge graph by using a target iterative algorithm and acquiring a target entity.
It is understood that the above modules of the data mining apparatus in the present embodiment have functions of implementing the corresponding steps of the data mining method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. For the functional description of each module of the data mining device, reference may be specifically made to the corresponding description of the data mining method in the embodiment shown in fig. 1, and details are not described here again.
As an example, fig. 3 shows a schematic structural diagram of an electronic device to which an embodiment of the present application is applicable, and as shown in fig. 3, the electronic device 2000 includes: a processor 2001 and a memory 2003. Wherein the processor 2001 is coupled to a memory 2003, such as via a bus 2002. Optionally, the electronic device 2000 may also include a transceiver 2004. It should be noted that the transceiver 2004 is not limited to one in practical applications, and the structure of the electronic device 2000 is not limited to the embodiment of the present application.
The processor 2001 is applied to the embodiment of the present application to implement the method shown in the above method embodiment. The transceiver 2004 may include a receiver and a transmitter, and the transceiver 2004 is applied to the embodiments of the present application to implement the functions of the electronic device of the embodiments of the present application to communicate with other devices when executed.
The Processor 2001 may be a CPU (Central Processing Unit), general Processor, DSP (Digital Signal Processor), ASIC (Application specific integrated Circuit), FPGA (Field Programmable Gate Array) or other Programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 2001 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs and microprocessors, and the like.
The Memory 2003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically erasable programmable Read Only Memory), a CD-ROM (Compact disk Read Only Memory) or other optical disk storage, optical disk storage (including Compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these.
Optionally, the memory 2003 is used for storing application program code for performing the disclosed aspects, and is controlled in execution by the processor 2001. The processor 2001 is used to execute the application program code stored in the memory 2003 to implement the methods provided in any of the embodiments of the present application.
The electronic device provided by the embodiment of the application is applicable to any embodiment of the method, and is not described herein again.
The present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method shown in the above method embodiments.
The computer-readable storage medium provided in the embodiments of the present application is applicable to any of the embodiments of the foregoing method, and is not described herein again.
According to the scheme provided by the embodiment of the application, transaction entity information and transaction relation information are defined by receiving a transaction knowledge graph of a target main body, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of transaction; and acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information. Based on the scheme, the distributed computing cluster can be utilized, the high-efficiency data mining speed is realized by utilizing the existing data mining model according to the defined entity information and the relationship information, the problem that the prior art excessively depends on expert experience is reduced, and the transaction data mining speed is improved.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method of data mining, the method comprising:
receiving a transaction knowledge-graph of a target subject,
defining transaction entity information and transaction relation information, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction;
and acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
2. The data mining method of claim 1, further comprising storing the received transaction knowledge graph in a distributed system in an ORC format.
3. The data mining method of claim 2, wherein the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
4. The data mining method according to any one of claims 1 to 3, wherein the obtaining of the target entity in the transaction knowledge graph by using the selected knowledge graph mining model is specifically obtaining the target entity by using a distributed architecture model; wherein further comprising:
uniformly distributing the entities in the transaction knowledge graph to each device in a computing cluster;
and in the computing cluster, identifying entities with the similarity meeting a threshold value in the transaction knowledge graph by using a target iterative algorithm and acquiring target entities.
5. The data mining device is characterized by comprising a receiving module, a storage module, a definition module and a processing module, wherein the receiving module is used for receiving the data;
the receiving module is used for receiving the transaction knowledge graph of the target subject,
the defining module is used for defining transaction entity information and transaction relation information, wherein the transaction entity information at least comprises identity information of a transaction entity, and the transaction relation information at least comprises vector information of a transaction;
the storage module is used for storing the transaction knowledge spectrogram and defined transaction entity information and transaction relationship information;
and the processing module is used for acquiring a target entity in the transaction knowledge graph by using the selected knowledge graph data mining model according to the defined entity information and the relationship information.
6. The data mining device of claim 5, wherein the storage module is further configured to store the received transaction knowledge graph in a distributed system in an ORC format.
7. The data mining device of claim 6, wherein the selected knowledge-graph data mining model is an overall synchronous parallel computing model.
8. The data mining device of any of claims 5-7, wherein the processing module is further configured to obtain the target entity using a distributed architecture model; the processing module further comprises an allocation unit and a calculation unit, wherein:
the distribution unit is used for uniformly distributing the entities in the transaction knowledge graph to each device in the computing cluster;
and the computing unit is used for identifying the entity with the similarity meeting the threshold in the transaction knowledge graph by using a target iterative algorithm and acquiring a target entity.
9. An electronic device comprising a display, a processor, and a memory; wherein the content of the first and second substances,
the memory is used for storing operation instructions;
the processor is used for executing the method of any one of claims 1-4 by calling the operation instruction.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-4.
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