Data query method, device, apparatus, and storage medium

Abstract

This invention discloses a data query method. The method includes: obtaining a query request, the query request including a target instance and associated instances corresponding to the target instance; if the request type corresponding to the query request is a non-node type, then determining the target instance corresponding to the query request in a first database, determining the associated data corresponding to the target instance in a second database, and using the target instance and the associated data corresponding to the target instance as the query result corresponding to the query request, wherein the associated data includes associated instances corresponding to the target instance, and the association relationship between the target instance and the associated instances. The above data query method uses a graph database to query multi-level associated data, improving the query speed of multi-level associated data, solving the problem that existing data query methods cannot quickly determine the impact of faults, and thus improving work efficiency.

Description

Technical Field

[0001] This invention relates to the field of computer programs, and more particularly to a data query method, apparatus, device, and storage medium. Background Technology

[0002] Configuration management, as a crucial component of IT Service Management (ITSM), serves as the digital twin of the production operations center, connecting various operations systems. It is the cornerstone for rapidly locating the impact of production events, instantly generating various configuration consumption reports, and facilitating fast multi-level relational queries.

[0003] Daily relationship retrieval in current configuration management is primarily accomplished using relational databases or some NoSQL databases, with MySQL or MongoDB being common examples. MySQL mainly uses foreign keys to establish relationships between different tables to achieve the entire configuration management mesh structure. MongoDB stores instances and relationships separately as files, using unique primary keys to distinguish different instances and relationships. It can meet the needs of fast retrieval of nodes and simple relationships, but it is slightly insufficient for complex relationship retrieval.

[0004] In summary, existing data query methods have the problem of failing to quickly determine the scope of a fault's impact. Summary of the Invention

[0005] This invention provides a data query method, apparatus, device, and storage medium to solve the problem that existing data query methods cannot quickly determine the impact of a fault.

[0006] According to one aspect of the present invention, a data query method is provided, the method comprising:

[0007] Obtain a query request, which includes a target instance and associated instances corresponding to the target instance;

[0008] If the request type corresponding to the query request is a non-node type, then the target instance corresponding to the query request is determined in the first database, the associated data corresponding to the target instance is determined in the second database, and the target instance and the associated data corresponding to the target instance are used as the query result corresponding to the query request. The associated data includes the associated instances corresponding to the target instance and the association relationship between the target instance and the associated instances.

[0009] According to another aspect of the present invention, a data query apparatus is provided, the apparatus comprising:

[0010] The request retrieval module is used to retrieve a query request, which includes a target instance and the associated instance corresponding to the target instance.

[0011] The query module is configured to, if the request type corresponding to the query request is a non-node type, determine the target instance corresponding to the query request in a first database, determine the associated data corresponding to the target instance in a second database, and use the target instance and the associated data corresponding to the target instance as the query result corresponding to the query request, wherein the associated data includes the associated instances corresponding to the target instance and the association relationship between the target instance and the associated instances.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] At least one processor; and

[0014] A memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data query method according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data query method described in any embodiment of the present invention.

[0017] The technical solution of this invention involves obtaining a query request, which includes a target instance and its corresponding associated instances. If the request type is a non-node type, the target instance corresponding to the query request is determined in a first database, and the associated data corresponding to the target instance is determined in a second database. The target instance and its associated data are then used as the query result for the query request. The associated data includes the associated instances corresponding to the target instance and the relationship between the target instance and its associated instances. This data query method uses a graph database to query multi-level associated data, improving the query speed for multi-level associated data and solving the problem of existing data query methods being unable to quickly determine the impact of faults, thereby improving work efficiency.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a data query method provided according to Embodiment 1 of the present invention;

[0021] Figure 2 This is a schematic diagram of the data relationships in the Neo4j database provided in Embodiment 1 of the present invention;

[0022] Figure 3 This is a flowchart of another data query method provided in Embodiment 1 of the present invention;

[0023] Figure 4A This is a schematic diagram of the structure of a data query device according to Embodiment 2 of the present invention;

[0024] Figure 4B This is a schematic diagram of the structure of another data query device provided in Embodiment 2 of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of the electronic device described in Embodiment 3 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1 This is a flowchart of a data query method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations involving multi-level related data queries and real-time data report generation. The method can be executed by a data query device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0030] S110. Obtain the query request. The query request includes the target instance and the associated instances corresponding to the target instance.

[0031] Configuration management, as a digital twin of the system operations and maintenance center, is a mirrored representation of various configuration items in actual production. It mainly includes various configuration instances and the complex relationships between them. Among them, the target instance is the first configuration item instance in configuration management, and the associated instances are configuration item instances that are related to the first configuration item.

[0032] Regarding query requests, for example, when switch A fails, a query command can be entered into the fault query interface. The processor will then generate a corresponding query request based on this command. The query command can be configured as follows: "MATCH(san:san{name:'A'})-[:run]->(storage:storage)-[:run]->(host:host)->[:run]->(app:app)RETURN storage,host,app". This query command can query all applications that may be affected by the failed switch A, i.e., the scope of the failed switch's impact, or the impact area of ​​the failed switch.

[0033] S120. If the request type corresponding to the query request is a non-node type, then determine the target instance corresponding to the query request in the first database, determine the associated data corresponding to the target instance in the second database, and use the target instance and the associated data corresponding to the target instance as the query result corresponding to the query request. The associated data includes the associated instances corresponding to the target instance, and the association relationship between the target instance and the associated instances.

[0034] The query requests can be categorized into node-type and non-node-type requests. Node-type queries retrieve configuration item instances; non-node-type queries retrieve associated configuration item instances and the relationships between them.

[0035] Traditional relational databases and some NoSQL databases tend to become slow and unsuitable for production environments when performing join queries at a certain depth. Graph databases, on the other hand, have an inherent advantage in this regard. Unlike traditional foreign keys, Neo4j stores data in terms of nodes and relationships to establish connections. With the support of built-in graph algorithms such as shortest path algorithms, optimized graph traversal algorithms, centrality algorithms, and community detection algorithms, it performs exceptionally well in terms of the speed of finding relationships.

[0036] Using a configuration management scenario with a maximum model-to-model association depth of 5, a total of 1,000,000 instances, and 50 associations per instance as an example, we tested MongoDB and Neo4j databases. The experimental results are shown in Table 1, demonstrating that Neo4j is the optimal choice for handling complex queries.

[0037] Table 1 Comparison of Related Query Results

[0038]

[0039] Configuration management data mainly consists of four aspects: models, properties, instances, and relationships. In the Neo4j database, these correspond to labels, properties, nodes, and relationships. (See also...) Figure 2 Tags can be created for nodes and attributes using commands. A node can have multiple tags, and nodes of the same type are distinguished by these tags. For example... Figure 2 As shown, both nodes and relationships have attributes.

[0040] For example, the command "CREATE(A:a)" can create a label 'a' for node A. The command to create multiple labels a1, a2, a3 for node A is "CREATE(A:a1:a2:a3)". The command "CREATE(B:b)-[R:r]->(C:c)" can create a label 'r' for the relationship R between nodes B and C, where node B contains label 'b' and node C contains label 'c'.

[0041] Optionally, the first database is a MongoDB database, and the second database is a Neo4j database. The MongoDB database stores instance information and operation logs for configuration items, providing quick access to daily node information and operation logs; all CRUD operations are performed within the MongoDB database. The Neo4j database is responsible for storing complex configuration item relationships in configuration management, enabling quick resource location and retrieval during daily maintenance events, and one-click export of complex reports.

[0042] If the request type is not a node type, the query interface encapsulated by MongoDB database is used to determine the target configuration item instance corresponding to the query request. Then, the query interface encapsulated by Neo4j database is used to determine the associated data corresponding to the target configuration item instance. The associated data corresponding to the target configuration item instance consists of configuration item instances that are related to the target configuration item instance and the relationship between the target configuration item instance and the related configuration item instances. The associated data corresponding to the target configuration item instance is used as the query result.

[0043] Optionally, if the query request type is a node type, the query result is determined by the query interface encapsulated in MongoDB, which identifies the corresponding configuration item instance. Differentiated processing is applied to different query requests, retrieving results from different databases. This improves data query speed, addresses the issue of existing data query methods failing to quickly determine the impact of faults, and ultimately enhances work efficiency.

[0044] Optional, such as Figure 3 As shown, after obtaining the query request, the data query method also includes S130: when a data change is detected in the first database, the data change is synchronized to the second database.

[0045] While Neo4j offers significantly faster retrieval and join speeds compared to other database types, it doesn't perform as well with frequent CRUD operations. MongoDB, on the other hand, uses memory mapping, allowing data writes to be completed in memory and returned to the application immediately, greatly improving concurrency. Therefore, MongoDB is used as the entry point for all configuration management data modifications. When changes are detected in MongoDB, these changes are synchronized to Neo4j in real time to ensure data consistency.

[0046] The database log file records operation log information for configuration items. When an increase in data is detected in the MongoDB database log file due to data changes, this data change is synchronized to the master database in the Neo4j database cluster. The master database then synchronizes this data change to the slave databases in the Neo4j database cluster. Alternatively, the data change is synchronized to the first slave database in the Neo4j database cluster, which then synchronizes it to its corresponding master database. The master database then synchronizes this data change to all slave databases except the first slave database. Real-time monitoring of data changes in the MongoDB database and synchronization of that data to the Neo4j database ensures data consistency across all databases.

[0047] To achieve real-time data synchronization from MongoDB to Neo4j, a long-lived connection can be used to monitor changes in MongoDB's log files. The changed data is then processed into different streams and sent to Neo4j for categorized processing, thus updating the data in Neo4j in real time. This application does not limit the data synchronization method or the method for monitoring changes in MongoDB's log data.

[0048] Optional, such as Figure 3 As shown, the data query method also includes S140: when a data export request is detected, the data export result corresponding to the export request is determined in the second database, and the data export result corresponding to the data export request is output.

[0049] Configuration management can also generate various configuration consumption reports in real time. When a data export request is detected, the Neo4j database concatenates the configuration item instances corresponding to the export request to form a report, which is then output as the result of the export request. Using the Neo4j database to export configuration item reports in real time ensures that the exported data is consistent with the configuration management data, and there will be no data delays.

[0050] For example, the data export command is obtained as follows: "MATCH data=(mysql:mysql)-[r1:run]->(host:host)-[r2:run]->(app:app)return data limit 100". This command determines the data request corresponding to the export command and displays a report representing the MySQL database, host, and application system—the expected data export result. Through paginated queries, the query time can be controlled to within 2 seconds, ensuring that the data remains consistent with the configuration management data and preventing delays.

[0051] The technical solution provided in this embodiment obtains a query request, which includes a target instance and its corresponding associated instances. If the query request type is not a node type, the target instance corresponding to the query request is determined in a first database, and the associated data corresponding to the target instance is determined in a second database. The target instance and its associated data are then used as the query result for the query request. The associated data includes the associated instances corresponding to the target instance and the relationship between the target instance and its associated instances. This enables querying multi-level associated data using a graph database, improving the query speed for multi-level associated data and solving the problem of existing data query methods being unable to quickly determine the impact of faults, thereby improving work efficiency.

[0052] Example 2

[0053] Figure 4A This is a schematic diagram of a data query device provided in Embodiment 2 of the present invention. Figure 4A As shown, the device includes:

[0054] The request retrieval module 210 is used to retrieve query requests, which include the target instance and the associated instances corresponding to the target instance.

[0055] The query module 220 is used to determine the target instance corresponding to the query request in the first database and the associated data corresponding to the target instance in the second database if the request type corresponding to the query request is a non-node type. The target instance and the associated data corresponding to the target instance are used as the query result corresponding to the query request. The associated data includes the associated instances corresponding to the target instance and the association relationship between the target instance and the associated instances.

[0056] Optional, such as Figure 4B As shown, the data query device also includes a synchronization module 230, which is used to synchronize the data change to the second database when a data change is detected in the first database.

[0057] Optionally, the first database is a MongoDB database, and the second database is a Neo4j database.

[0058] Optionally, the synchronization module 230 is specifically used to synchronize the data change to the master database of the second database cluster when it detects that the log file of the first database has generated a data increment caused by data change, so that the master database can synchronize the data change to the corresponding slave database; or, when it detects that the log file of the first database has generated a data increment caused by data change, it synchronizes the data change to the first slave database of the second database cluster, so that the first slave database can synchronize the data change to the corresponding master database, and the master database can synchronize the data change to other slave databases other than the first slave database.

[0059] Optionally, the query module 220 is used to determine the query result corresponding to the query request in the first database if the request type corresponding to the query request is a node type.

[0060] Optional, such as Figure 4B As shown, the data query device also includes a data export module 240, which is used to determine the data export result corresponding to the export request in the second database when a data export request is detected, and output the data export result corresponding to the data export request.

[0061] The data query device provided in this embodiment has a request acquisition module that acquires query requests, and a query module that performs differentiated processing based on different query requests to obtain query results from different databases. This improves the query speed of multi-level related data, solves the problem that existing data query methods cannot quickly determine the impact of faults, and thus improves work efficiency.

[0062] The data query device provided in this embodiment of the invention can execute the data query method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0063] Example 3

[0064] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0065] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0066] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0067] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method data query method.

[0068] In some embodiments, the data query method described in the above embodiments can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data query method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the data query method described in the above embodiments by any other suitable means (e.g., by means of firmware).

[0069] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0073] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0074] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0075] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0076] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data query method, characterized by, include: Obtain a query request, which includes a target instance and associated instances corresponding to the target instance; If the request type corresponding to the query request is a non-node type, then the target instance corresponding to the query request is determined in the first database, the associated data corresponding to the target instance is determined in the second database, and the target instance and the associated data corresponding to the target instance are used as the query result corresponding to the query request. The associated data includes the associated instances corresponding to the target instance and the association relationship between the target instance and the associated instances. When a data change is detected in the first database, the data change is synchronized to the second database; wherein, the method for detecting the data change in the first database is: monitoring the log file changes of the first database using a long connection; The first database is a MongoDB database, and the second database is a Neo4j database; The step of synchronizing the data change to the second database when a change is detected in the first database includes: When a data increment caused by a data change is detected in the log file of the first database, the data change is synchronized to the master database of the second database cluster, so that the master database can synchronize the data change to the corresponding slave database; or, When a data increment caused by a data change is detected in the log file of the first database, the data change is synchronized to the first slave database of the second database cluster, so that the first slave database can synchronize the data change to the corresponding master database, and the master database can synchronize the data change to other slave databases other than the first slave database.

2. The method according to claim 1, characterized in that, After obtaining the query request, the process also includes: If the request type corresponding to the query request is a node type, then the query result corresponding to the query request is determined in the first database.

3. The method according to claim 1, characterized in that, Also includes: When a data export request is detected, the data export result corresponding to the export request is determined in the second database and output.

4. A data query device, characterized in that, include: The request retrieval module is used to retrieve a query request, which includes a target instance and the associated instance corresponding to the target instance. The query module is configured to, if the request type corresponding to the query request is a non-node type, determine the target instance corresponding to the query request in a first database, determine the associated data corresponding to the target instance in a second database, and use the target instance and the associated data corresponding to the target instance as the query result corresponding to the query request, wherein the associated data includes the associated instances corresponding to the target instance and the association relationship between the target instance and the associated instances; The synchronization module is used to synchronize the data changes in the first database to the second database when a data change is detected; wherein, the method for detecting the data change in the first database is to monitor the changes in the log files of the first database using a long connection; The first database is a MongoDB database, and the second database is a Neo4j database; The synchronization module is specifically used to synchronize the data change to the master database of the second database cluster when it detects that the log file of the first database has generated a data increment due to data change, so that the master database can synchronize the data change to the corresponding slave database; or, when it detects that the log file of the first database has generated a data increment due to data change, it synchronizes the data change to the first slave database of the second database cluster, so that the first slave database can synchronize the data change to the corresponding master database, and the master database can synchronize the data change to other slave databases other than the first slave database.

5. The apparatus according to claim 4, characterized in that, Also includes: The data export module is used to determine the data export result corresponding to the data export request in the second database when a data export request is detected, and to output the data export result corresponding to the data export request.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data query method according to any one of claims 1-3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data query method according to any one of claims 1-3.

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