Multi-source data query analysis method and device

Abstract

The invention discloses a multi-source data query analysis method and device, and relates to the field of big data. A specific embodiment of the method comprises the steps of obtaining a plurality of data sets from different types of data sources through corresponding query engines; matching the configuration information to a corresponding calculation engine according to user configuration; and calculating the plurality of data sets through a calculation engine to obtain a target data set. According to the embodiment, the analysis configuration of the user can be flexibly analyzed into the executable logic, and the analysis result expected by the user is generated in the memory through the calculation engine according to different configuration rules.

Description

Technical Field

[0001] This invention relates to the field of big data, and in particular to a method and apparatus for multi-source data query and analysis. Background Technology

[0002] In current data query and analysis systems, common data visualization formats mainly include basic charts such as two-dimensional tables, line charts, bar charts, line-column charts, bar graphs, stacked percentage charts, pie charts, donut charts, and maps. While these visualization methods can meet routine analysis needs, they often fall short in supporting more complex, flexible, or multi-dimensional analysis scenarios. To achieve this type of analysis, it is usually necessary to use specialized tools to download the data offline and then process it using Excel or other analytical tools. Current data analysis platforms struggle to integrate, analyze, and visualize multi-dimensional data within a single system.

[0003] Furthermore, when using existing technologies for formula calculations, it is typically necessary to download data from different data sources separately, aggregate it locally, and then perform analysis and calculations offline. This makes it difficult to achieve dynamic calculations based on real-time data. This process is not only inefficient but also poses data security risks—once the data is downloaded locally, it is removed from the platform's original security control system, increasing the possibility of data leakage and misuse.

[0004] Currently, most data analysis platforms on the market generate corresponding SQL execution logic based on user-configured analysis and utilize various query engines, such as ETL (extract, transform, load) tools, to complete data queries. The returned results are then processed and displayed. However, when faced with special analysis scenarios such as custom reports and cross-tab reports, existing platforms often lack general support capabilities, requiring customized development and thus limiting analysis efficiency and flexibility. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a multi-source data query and analysis method and apparatus. Embodiments of this invention can query data from different types of data sources through different query engines, and perform correlation calculations of different data in memory, thereby achieving cross-database calculations without relying on traditional ETL tools. Furthermore, since the data is stored in memory, the user's analysis configuration can be flexibly parsed into executable logic, and corresponding calculation engines can be generated for different configuration rules, directly generating the user's expected analysis results in memory through the calculation engines.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a multi-source data query and analysis method is provided, comprising: Obtain multiple datasets from different types of data sources using the corresponding query engines; Match configuration information to the appropriate computing engine based on user configuration; The target dataset is obtained by performing calculations on the multiple datasets using the computing engine.

[0007] Optionally, before matching the configuration information to the appropriate computing engine based on the user configuration, the method further includes: The multiple datasets are correlated, and the correlation results are stored in memory.

[0008] Optionally, the target dataset is obtained by performing calculations on the multiple datasets using the corresponding computing engines, including: The target dataset is obtained by calculating the correlation results using the computing engine.

[0009] Optionally, the target dataset is obtained by performing calculations on the correlation calculation results through the calculation engine, including: The user can configure the relationships, expansion directions, and calculation formulas between the multiple datasets.

[0010] Optionally, the association relationship also includes the association method between the multiple datasets.

[0011] Optionally, the method also includes formatting the target dataset using a rules engine to display it to the user.

[0012] According to a second aspect of the present invention, a multi-source data query and analysis apparatus is provided, comprising: The dataset query module is used to obtain multiple datasets from different types of data sources through the corresponding query engines. The configuration module is used to match configuration information to the corresponding computing engine based on user configuration. The target dataset calculation module is used to calculate the target dataset by means of the calculation engine on the multiple datasets.

[0013] Optionally, the device also includes an association configuration module for configuring associations between multiple datasets.

[0014] Optionally, the device also includes an association calculation module for performing association calculations on multiple datasets and storing the association calculation results in memory.

[0015] Optionally, the device also includes a target dataset formatting module for formatting the target dataset using a rules engine to display it to the user.

[0016] According to a third aspect of the present invention, an electronic device is provided, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the methods of any of the above embodiments.

[0017] According to a fourth aspect of the present invention, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method of any of the above embodiments.

[0018] According to a fifth aspect of the present invention, a computer program product is provided, including a computer program that, when executed by a processor, implements the method of any of the above embodiments.

[0019] One embodiment of the above invention has the following advantages or beneficial effects: data is queried from different types of data sources through different query engines, and the correlation calculations of different data are performed in memory, thereby achieving cross-database calculations without relying on traditional ETL tools. Furthermore, since the data is stored in memory, the user's analysis configuration can be flexibly parsed into executable logic, and corresponding calculation engines can be generated for different configuration rules, directly generating the user's expected analysis results in memory through the calculation engines.

[0020] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0021] The accompanying drawings are provided to better understand this application and do not constitute an undue limitation thereof. Wherein: Figure 1 This is a schematic diagram of the process of a multi-source data query and analysis method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the flow of a multi-source data query and analysis method provided in another embodiment of the present invention; Figure 3 This is a schematic diagram of the process of a multi-source data query and analysis method provided in another embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a multi-source data query and analysis device provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a multi-source data query and analysis device provided in another embodiment of the present invention; Figure 6 This is a schematic diagram of an exemplary system architecture to which embodiments of the present invention can be applied; Figure 7 This is a schematic diagram of the structure of a computer system suitable for implementing terminal devices or servers of the present invention. Detailed Implementation

[0022] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0023] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of this invention comply with the relevant provisions of national laws and regulations.

[0024] Figure 1 This is a schematic diagram illustrating the flow of a multi-source data query and analysis method according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps 101-103.

[0025] Step 101: Obtain multiple datasets from different types of data sources using the corresponding query engines.

[0026] In this embodiment, different types of data sources include data sources commonly used in the art, such as database tables, Excel, Hive, MySQL, ClickHouse, local files, etc.

[0027] You can query data from different types of data sources using the corresponding query engine. For example, you can query a dataset from a Hive data source using HiveSQL. You can also query a dataset from a MySQL data source using MySQLSQL. Furthermore, you can query a dataset from a ClickHouse data source using ClickHouseSQL. Finally, you can query a dataset from a local file data source using LocalFileSQL.

[0028] Step 102: Match the configuration information to the corresponding computing engine according to the user configuration.

[0029] Specifically, users use operating system platforms, such as Excel, to configure configuration information between multiple datasets.

[0030] Optionally, the configuration information may include cell dependencies, expansion direction, calculation formulas, etc. After receiving the user's configuration information, the configuration information is parsed and matched to the corresponding calculation engine.

[0031] Step 103: The target dataset is obtained by performing calculations on multiple datasets using a computing engine.

[0032] Specifically, the computing engine matches the user's flexible and varied configuration information to different memory computing rules based on complete computing rules. Specifically, this invention mainly includes three computing engines: NoneExpandBuilder, RightExpandBuilder, and DownExpandBuilder.

[0033] The three calculation engines are used to implement different calculation methods for the parent cell's result set data in different scenarios: no expansion, right expansion, and downward expansion, thereby enabling users to configure specific settings.

[0034] For example, if the user has configured a top parent cell and a left parent cell, and the expansion direction is set to "no expansion," the calculation primarily considers how to calculate the data result without expansion while satisfying the calculation conditions of the parent cells. Therefore, in this case, the NoneExpandBuilder calculation engine is used. NoneExpandBuilder is a basic calculation engine mainly used to return the current result set directly without further task calculations.

[0035] For example, when the user configuration matches the RightExpandBuilder and DownExpandBuilder calculation engines, it determines whether to trigger cell copy rules for self-copying and blank placeholder copying based on the parent cell information corresponding to each result item in the result set.

[0036] Specifically, RightExpandBuilder is used for horizontal expansion. After configuring an analysis field in a cell, it expands the underlying data corresponding to that field horizontally, and the data in its child cells is also copied and filled in a horizontal expansion manner. Similarly, DownExpandBuilder is used for vertical expansion, and the data in its child cells is copied and filled in a vertical expansion manner.

[0037] In the embodiments of this invention, multiple datasets are obtained from different types of data sources through corresponding query engines; configuration information is matched to the corresponding computing engine according to user configuration; and the computing engine performs calculations on the multiple datasets to obtain the target dataset. The technical solution of this invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of the multiple datasets in computer memory through user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0038] Figure 2This is a schematic diagram of the flowchart of a multi-source data query and analysis method provided in another embodiment of the present invention. Figure 2 As shown, the method includes the following steps 201-205.

[0039] Step 201: Obtain multiple datasets from different types of data sources using the corresponding query engines.

[0040] In this embodiment, different types of data sources include data sources commonly used in the art, such as database tables, Excel, Hive, MySQL, ClickHouse, local files, etc.

[0041] You can query data from different types of data sources using the corresponding query engine. For example, you can query a dataset from a Hive data source using HiveSQL. You can also query a dataset from a MySQL data source using MySQLSQL. Furthermore, you can query a dataset from a ClickHouse data source using ClickHouseSQL. Finally, you can query a dataset from a local file data source using LocalFileSQL.

[0042] Step 202: The user configures the relationships between multiple datasets.

[0043] Specifically, users configure the relationships, expansion directions, and calculation formulas between multiple datasets.

[0044] In one embodiment of the present invention, the association relationship also includes the association relationship between multiple datasets and the association relationship between cells.

[0045] Specifically, the relationships between multiple datasets can include the dataset association method, the dataset association fields, and the dataset calculation method.

[0046] Specifically, the relationships between cells can include cell configuration fields, parent cell relationships, and cell expansion direction.

[0047] Step 203: Perform association calculations on multiple datasets and store the association calculation results in memory.

[0048] Specifically, in practical applications, when it's necessary to correlate multiple datasets from different data sources, before matching configuration information to the appropriate computing engine based on user settings, it's necessary to perform correlation calculations on the multiple datasets and store the results in memory. In traditional software development, data from the same source can be correlated through their own computation, while data from different sources needs to be pre-computed using ETL tools to make them a unique target data source before analysis.

[0049] Compared to existing technologies, the multi-source data query and analysis method provided in this embodiment does not require data preprocessing using ETL tools when it is necessary to correlate multiple datasets from different types of data sources. Users only need to configure the correlation relationships between data sources, and the system can directly complete the correlation calculation and processing by loading the data into the computer memory through the data access interfaces provided by each data source itself.

[0050] For example, when associating multiple datasets from different types of data sources, users can configure the association relationships, expansion directions, and calculation formulas between the multiple datasets, such as left association, right association, and inner association, thereby avoiding the use of a complex ETL scheduling system to generate a new cross-database calculation result set in memory from the data of each dataset.

[0051] Step 204: Match the configuration information to the corresponding computing engine according to the user configuration.

[0052] Step 205: The calculation engine is used to calculate the correlation calculation results to obtain the target dataset.

[0053] Specifically, it can perform group calculations, aggregation calculations, list calculations, data sorting, and calculated fields on the results of related calculations.

[0054] In some embodiments, cell rendering includes cell assembly configuration and cell copying. Specifically, starting with cell assembly configuration, cells are assembled according to the expansion direction, specifically including no expansion, right expansion, and down expansion, i.e., the three calculation engines described in step 103: NoneExpandBuilder, RightExpandBuilder, and DownExpandBuilder.

[0055] In cells without expansion, there is no need to copy rows or columns.

[0056] In the cells that expand to the right, copy the column, which means getting the parent cell, child cells, and empty cells of the column.

[0057] In the downward-expanding cells, copy the row, which means obtaining the parent cell, child cells, and empty cells of the current row.

[0058] Then, generate the configured cells and add them to the list to be executed.

[0059] In cell copying, cells are sequentially extracted from the list to be executed, and the set of field enumeration values ​​corresponding to the cell to be executed is obtained to copy in the direction of expansion.

[0060] In the absence of expansion, there is no need to build a replicator; simply put the data directly into the database, separated by commas.

[0061] In the case of expanding to the right, copy the column, copy the cell, and copy the sub-cell.

[0062] In the case of expanding downwards, copy the row, copy the cell, and copy the sub-cell.

[0063] During the copying process to child cells, the child cells are added to the parent cell's ColumnChildren or RowChildren collection to prepare data for subsequent calculations.

[0064] Finally, calculations are performed to return the data to the user.

[0065] In the embodiments of this invention, multiple datasets are obtained from different types of data sources through corresponding query engines; association calculations are performed on the multiple datasets, and the association calculation results are stored in memory; configuration information is matched to the corresponding calculation engine according to user configuration; the calculation engine calculates the association calculation results to obtain the target dataset. The technical solution of this invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of the multiple datasets in computer memory according to the user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0066] Figure 3 This is a schematic diagram of the flowchart of a multi-source data query and analysis method provided in another embodiment of the present invention. For example... Figure 3 As shown, the method includes the following steps 301-306.

[0067] Step 301: Obtain multiple datasets from different types of data sources using the corresponding query engines.

[0068] In this embodiment, different types of data sources include data sources commonly used in the art, such as database tables, Excel, Hive, MySQL, ClickHouse, local files, etc.

[0069] You can query data from different types of data sources using the corresponding query engine. For example, you can query a dataset from a Hive data source using HiveSQL. You can also query a dataset from a MySQL data source using MySQLSQL. Furthermore, you can query a dataset from a ClickHouse data source using ClickHouseSQL. Finally, you can query a dataset from a local file data source using LocalFileSQL.

[0070] Step 302: The user configures the relationships between multiple datasets.

[0071] Step 303: Perform association calculations on multiple datasets and store the association calculation results in memory.

[0072] Step 304: Match the configuration information to the corresponding computing engine based on the user configuration.

[0073] Step 305: The calculation engine is used to calculate the correlation calculation results to obtain the target dataset.

[0074] Step 306: Format the target dataset using the rules engine to display it to the user.

[0075] For example, the rule engine performs data formatting, conditional attribute calculation, and result sorting calculation on the result set, and finally displays the calculation results to the user.

[0076] Specifically, the rule engine mainly encapsulates the result set of the calculation engine. The rule calculation mainly includes result data formatting, intra-group or global sorting of result set data, and calculation of data condition attributes, and finally displays the calculation results to the user.

[0077] In some embodiments, the forms presented to the user include multidimensional tables, cross tables, and free tables.

[0078] In the embodiments of this invention, multiple datasets are obtained from different types of data sources through corresponding query engines; association calculations are performed on the multiple datasets, and the association calculation results are stored in memory; configuration information is matched to the corresponding calculation engine according to user configuration; the calculation engine calculates the association calculation results to obtain the target dataset; and the target dataset is formatted by a rule engine for display to the user. The technical solution of this invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of multiple datasets in computer memory according to user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0079] Figure 4 This is a schematic diagram of the structure of a multi-source data query and analysis device 400 provided in an embodiment of the present invention. Figure 4 As shown, the device 400 includes: The dataset query module 401 is used to obtain multiple datasets from different types of data sources through the corresponding query engines; Configuration module 402 is used to match configuration information to the corresponding computing engine according to user configuration; The target dataset calculation module 403 is used to calculate multiple datasets through the calculation engine to obtain the target dataset.

[0080] Optionally, the device also includes an association configuration module for configuring associations between multiple datasets.

[0081] Optionally, the device also includes an association calculation module for performing association calculations on multiple datasets and storing the association calculation results in memory.

[0082] Optionally, the association calculation module is also used to format the target dataset through the rules engine for display to the user.

[0083] In the solution of this embodiment of the invention, multiple datasets are obtained from different types of data sources through corresponding query engines via the dataset query module 401; the configuration module 402 matches configuration information to the corresponding calculation engine according to user configuration; and the target dataset calculation module 403 calculates the multiple datasets to obtain the target dataset through the calculation engine. The technical solution of this embodiment of the invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of multiple datasets in computer memory through user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0084] Figure 5 This is a schematic diagram of the structure of a multi-source data query and analysis device 500 provided in another embodiment of the present invention. (See diagram below.) Figure 5 As shown, the device 500 includes: The dataset query module 501 is used to obtain multiple datasets from different types of data sources through the corresponding query engines. The association configuration module 502 is used to configure the association relationships between multiple datasets; Specifically, the association between multiple datasets is configured by receiving configuration information from the user.

[0085] The association calculation module 503 is used to perform association calculations on multiple datasets and store the association calculation results in memory; Configuration module 504 is used to match configuration information to the corresponding computing engine based on user configuration; The target dataset calculation module 505 is used to calculate the associated calculation results through the calculation engine to obtain the target dataset; The target dataset formatting module 506 is used to format the target dataset using the rules engine for display to the user.

[0086] For example, the rule engine performs data formatting, conditional attribute calculation, and result sorting calculation on the result set, and finally displays the calculation results to the user.

[0087] Specifically, the rule engine mainly encapsulates the result set of the calculation engine. The rule calculation mainly includes result data formatting, intra-group or global sorting of result set data, and calculation of data condition attributes, and finally displays the calculation results to the user.

[0088] In some embodiments, the forms presented to the user include multidimensional tables, cross tables, and free tables.

[0089] In the solution of this embodiment of the invention, multiple datasets are obtained from different types of data sources through corresponding query engines via a dataset query module 501; the association relationship configuration module 502 configures the association relationships between the multiple datasets; the association calculation module 503 performs association calculations on the multiple datasets and stores the association calculation results in memory; the configuration module 504 matches the configuration information to the corresponding calculation engine according to the user configuration; the target dataset calculation module 505 calculates the association calculation results to obtain the target dataset through the calculation engine; and the target dataset formatting module 506 formats the target dataset for display to the user through a rule engine. The technical solution of this embodiment of the invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of multiple datasets in computer memory through user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0090] Figure 6 An exemplary system architecture 600 to which embodiments of the present invention can be applied is shown, including terminal devices 601, 602, 603, network 604, and server 605 (only an example).

[0091] Terminal devices 601, 602, and 603 can be various electronic devices with displays and support for web browsing, and have various communication client applications installed. Users can use terminal devices 601, 602, and 603 to interact with server 605 through network 604 to receive or send messages, etc.

[0092] Network 604 is a medium used to provide a communication link between terminal devices 601, 602, 603 and server 605. Network 604 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0093] Server 605 can be a server providing various services, such as a backend management server supporting shopping websites browsed by users using terminal devices 601, 602, and 603 (this is just an example). The backend management server can analyze and process received data such as product information query requests, and feed back the processing results (e.g., target push information, product information—this is just an example) to the terminal devices. It should be noted that the method provided in this embodiment of the invention is generally executed by server 605, and correspondingly, the apparatus is generally set in server 605.

[0094] It should be understood that Figure 6 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0095] The following is for reference. Figure 7 It shows a schematic diagram of the structure of a computer system 700 suitable for implementing a terminal device of the present invention. Figure 7 The terminal device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0096] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 702 or programs loaded from storage section 708 into random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the system 700. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0097] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0098] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs the functions defined above in the system of this invention.

[0099] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0101] The modules described in the embodiments of the present invention can be implemented in software or hardware. These modules can also be housed in a processor, and for example, can be described as: a dataset query unit, a relation configuration unit, a relation calculation unit, a configuration unit, a target dataset calculation unit, and a target dataset formatting unit. The names of these modules do not necessarily limit the module itself; for example, a content acquisition module can also be described as "a module that obtains multiple datasets from different types of data sources through corresponding query engines."

[0102] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: Obtain multiple datasets from different types of data sources using the corresponding query engines; Match configuration information to the appropriate computing engine based on user configuration; The target dataset is obtained by performing calculations on multiple datasets using a computing engine.

[0103] In the embodiments of this invention, multiple datasets are obtained from different types of data sources through corresponding query engines; configuration information is matched to the corresponding computing engine according to user configuration; and the computing engine performs calculations on the multiple datasets to obtain the target dataset. The technical solution of this invention can extract multiple datasets from different types of data sources through corresponding query engines, and then store the calculation results of the multiple datasets in computer memory through user-specified association methods and relationships. This achieves a flexible multi-source data query and analysis method applicable to different analysis scenarios, avoiding the need for customized development and reducing system development and maintenance costs.

[0104] 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 occur depending on 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 multi-source data query and analysis method, characterized in that, include: Obtain multiple datasets from different types of data sources using the corresponding query engines; Match configuration information to the appropriate computing engine based on user configuration; The target dataset is obtained by performing calculations on the multiple datasets using the computing engine.

2. The method according to claim 1, characterized in that, Before matching configuration information to the appropriate computing engine based on user configuration, the process also includes: The multiple datasets are correlated, and the correlation results are stored in memory.

3. The method according to claim 2, characterized in that, The target dataset is obtained by performing calculations on the multiple datasets using the corresponding computing engines, including: The target dataset is obtained by calculating the correlation results using the computing engine.

4. The method according to claim 3, characterized in that, The target dataset is obtained by calculating the correlation results using the computing engine, including: The user can configure the relationships, expansion directions, and calculation formulas between the multiple datasets.

5. The method according to claim 4, characterized in that, The association also includes the association methods between the multiple datasets.

6. The method according to claim 5, characterized in that, Also includes: The target dataset is formatted using a rules engine to be displayed to the user.

7. A multi-source data query and analysis device, characterized in that, include: The dataset query module is used to obtain multiple datasets from different types of data sources through the corresponding query engines. The configuration module is used to match configuration information to the corresponding computing engine based on user configuration. The target dataset calculation module is used to calculate the target dataset by means of the calculation engine on the multiple datasets.

8. An electronic device for multi-source data query and analysis, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-6.

9. A computer-readable medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-6.

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