Data drilling method and device, storage medium and electronic equipment

Abstract

The invention relates to a data drilling method and device, a storage medium and electronic equipment, and relates to the field of data security, and the method comprises the steps: obtaining a data drilling instruction of a user; generating a corresponding data drilling path according to the data drilling instruction; data drilling is conducted in a database according to the data drilling path, and drilling data corresponding to the data drilling instruction is obtained. According to the scheme, the drilling direction of the data can be accurately determined, rapid drilling can be carried out, accurate data analysis can be provided for a user, and the user experience is improved.

Description

Technical Field

[0001] This disclosure relates to the field of data security, and more specifically, to a data drilling method, apparatus, storage medium, and electronic device. Background Technology

[0002] Data drill-down has become a fundamental functional requirement in the fields of data analysis and data visualization. Currently, traditional data drill-down methods often rely on mouse clicks to drill down level by level, requiring users to guess the drill-down direction. This results in long drill-down paths and inaccurate drill-down directions, making it difficult to provide accurate data analysis and ultimately degrading the user experience. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this disclosure provides a data drilling method, apparatus, storage medium, and electronic device.

[0004] According to a first aspect of the present disclosure, a data drill-down method is provided, comprising: Obtain user data drill-down instructions; Generate the corresponding data drilling path according to the data drilling instruction; Data drilling is performed in the database according to the data drilling path to obtain the drilling data corresponding to the data drilling instruction.

[0005] Optionally, the data drilling instructions include: text instructions, voice instructions, gesture instructions, and touch fusion instructions.

[0006] Optionally, generating the corresponding data drilling path according to the data drilling instruction includes: Determine the user's user role and the data hierarchy accessible to the user role; Based on the database, a data sandbox is dynamically generated according to the user-accessible data hierarchy; The data in the data sandbox is subjected to field-level filtering and anonymization. The data drilling path is generated based on the data drilling instructions and the data sandbox after desensitization processing.

[0007] Optionally, data drilling is performed in the database according to the data drilling path to obtain drilling data corresponding to the data drilling instruction, including: Data drilling is performed in the data sandbox according to the data drilling path to obtain drilling data corresponding to the data drilling instruction.

[0008] Optionally, generating the corresponding data drilling path according to the data drilling instruction includes: Based on the isolated forest algorithm, a corresponding data drilling path is generated according to the data drilling instructions.

[0009] Optionally, the method further includes: Based on the Apriori algorithm, corresponding data drilling suggestions are output according to the drilling data.

[0010] Optionally, the method includes: The drill data is rendered and displayed visually through a visualization panel.

[0011] According to a second aspect of the present disclosure, a data drilling apparatus is provided, comprising: The acquisition module is used to acquire the user's data drill-down instructions; The generation module is used to generate a corresponding data drilling path according to the data drilling instruction; The drilling module is used to perform data drilling in the database according to the data drilling path and obtain drilling data corresponding to the data drilling instruction.

[0012] According to a third aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the data drilling method described in the first aspect of the present disclosure.

[0013] According to a fourth aspect of the present disclosure, an electronic device is provided, comprising: A memory on which computer programs are stored; A processor is configured to execute the computer program in the memory to implement the steps of the data drilling method described in the first aspect of this disclosure.

[0014] The above technical solution obtains the user's data drill-down command; generates a corresponding data drill-down path based on the command; and performs data drill-down in the database according to the drill-down path to obtain the drill-down data corresponding to the command. This solution can accurately determine the drill-down direction and perform rapid drill-down, providing users with accurate data analysis and improving user experience.

[0015] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a data drilling method according to an exemplary embodiment.

[0017] Figure 2 This is a flowchart illustrating a data drilling method according to an exemplary embodiment.

[0018] Figure 3 This is a flowchart illustrating a data drilling method according to an exemplary embodiment.

[0019] Figure 4 This is a schematic diagram of a data drilling apparatus 400 according to an exemplary embodiment.

[0020] Figure 5 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. Detailed Implementation

[0021] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0022] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0023] It is understood that in this disclosure, "multiple" refers to two or more, and other quantifiers are similar. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. The singular forms "a," "the," and "the" are also intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It is further understood that the terms "first," "second," etc., are used to describe various types of information, but this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another, and do not indicate a specific order or degree of importance. In fact, expressions such as "first," "second," etc., are completely interchangeable. For example, without departing from the scope of this disclosure, the first time can also be referred to as the second time, and similarly, the second time can also be referred to as the first time.

[0025] It is further understood that although operations are described in a specific order in the accompanying drawings in the embodiments of this disclosure, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the shown operations to be performed to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0026] Figure 1 This is a flowchart illustrating a data drill-down method according to an exemplary embodiment. For example... Figure 1 As shown, the method includes the following steps.

[0027] In step S11, the user's data drill-down instruction is obtained.

[0028] For example, data drill-down is an interactive technique in business intelligence and data analytics that allows users to navigate from highly summarized data views (such as totals and averages) to more detailed, granular data views. Data drill-down is a core function of BI (Business Intelligence Platform) platforms (such as Tableau, Power BI, FineBI, QuickSight, etc.). For instance, it can be applied to sales analysis, drilling down from total annual sales to quarterly sales, monthly sales, sales by region / store, sales by specific salesperson, and individual order details. It can also be applied to financial analysis, drilling down from total annual costs to departmental costs, details of a department's travel expenses, material costs, etc., and viewing specific expense vouchers. It can also be applied to marketing analysis, drilling down from total campaign conversion rates to conversion rates across different channels, conversion rates of different ad creatives within a specific channel, and specific user click behavior data. Furthermore, it can be applied to financial statement analysis; for example, when viewing a summarized profit and loss statement, drilling down to the administrative expenses category allows viewing details such as personnel costs, office expenses, and depreciation. Therefore, data drill-down allows users to effectively analyze multi-level data. When making decisions, drill-down can quickly pinpoint the precise location of the problem. Thus, when users need to perform data analysis, they can issue a data drill-down command in the corresponding visualization panel, select data according to the command, and obtain the relevant drill-down data.

[0029] Optionally, the data drilling instructions include: text instructions, voice instructions, gesture instructions, and touch fusion instructions.

[0030] For example, in a BI platform, users can issue corresponding data drill-down commands through a data visualization panel. These commands can be triggered by the user in different ways, such as text commands, voice commands, gesture commands, and touch-fusion commands. Specifically, a text command refers to the user inputting corresponding text content through the visualization panel; a voice command refers to the BI platform receiving the user's corresponding voice content; a gesture command refers to performing a specified action at a designated location on the visualization panel; and a touch-fusion command is a fusion command consisting of at least two of the text, voice, and gesture commands received by the BI platform. For example, when a user inputs "Query cost data for departing employees in department XX" through a visualization panel, the BI platform uses NLP to identify keywords and generate corresponding data drill-down instructions. Alternatively, when the BI platform captures the user's voice prompt "Analyze the reasons for anomalies in the cost data of department XX," it generates corresponding data drill-down instructions through NLP keyword recognition. Or, when the BI platform's Leap Motion captures a three-finger contraction gesture as a drill-down trigger signal, it generates corresponding data drill-down instructions. Furthermore, when a user long-presses an area on the heatmap of the visualization panel to trigger drill-down of related dimensions, uses the voice command "Analyze the reasons for anomalies in the cost data of department XX," performs gesture operations (circling an area on the heatmap of department cost data), or clicks (highlighting a bar chart), the corresponding data drill-down instructions are integrated through touch. In summary, this method supports multimodal interaction methods such as text, voice, gestures, and touch, significantly reducing the number of steps involved in data drill-down, enabling rapid drill-down on mobile devices, and greatly improving data analysis efficiency.

[0031] In step S12, a corresponding data drilling path is generated based on the data drilling instruction.

[0032] For example, when the BI system receives a data drill-down instruction, it parses the instruction to clarify the user's purpose for data drill-down. Then, a corresponding data drill-down path can be generated based on the instruction, which can be described using a corresponding SQL statement. For instance, if the drill-down instruction is to retrieve the sales figures for each store in East China for the third quarter of a company's operations, a corresponding SQL statement can be generated based on the instruction, and this SQL statement can then be used to query the database for the sales figures for each store in East China for the third quarter.

[0033] Optionally, step S12 includes: generating a corresponding data drilling path based on the data drilling instruction using the isolated forest algorithm.

[0034] For example, in addition to retrieving relevant data from a database, data drill-down can also be used for data analysis via drill-down commands. The Isolation Forest algorithm can identify outliers in the data. When using the Isolation Forest algorithm, all possible next-level drill-down dimensions can be listed based on the data hierarchy of the data model (e.g., drill-down from month to day, from product category to product subcategory, from company to department, etc.). The Isolation Forest algorithm can evaluate each possible drill-down path and calculate the anomaly score for each path. The path with the highest anomaly score is then used as the drill-down path corresponding to the command, and drill-down is performed according to this path to obtain the corresponding drill-down data.

[0035] In step S13, data drilling is performed in the database according to the data drilling path to obtain the drilling data corresponding to the data drilling instruction.

[0036] For example, after determining the data drill-down path, a corresponding SQL statement can be generated. This SQL statement is used to query the database to obtain the corresponding drill-down data.

[0037] Optionally, the method may further include rendering and visualizing the drill-up data through a visualization panel.

[0038] For example, after obtaining the drill-down data, it can be rendered and visualized through a visualization panel. This visualization can include, but is not limited to, pie charts, line charts, bar charts, column charts, table charts, etc., and abnormal data can be marked to alert users to these anomalies. It is understood that this disclosure can employ focus + context visualization technology, where the main view and related views (such as box plots, scatter plots, and maps) are linked in real time during drill-down to avoid context loss and enhance analytical coherence. For example, when analyzing a company's human resources data, during drill-down of the main view (such as an organizational chart), related subviews can be updated synchronously (such as map area coloring and detail table refresh). These subviews can include: subviews... Figure 1 Box plots of cost data for each department (showing percentiles and outliers), sub-views Figure 2 Scatter plot of personnel-cost data.

[0039] The above technical solution obtains the user's data drill-down command; generates a corresponding data drill-down path based on the command; and performs data drill-down in the database according to the drill-down path to obtain the drill-down data corresponding to the command. This solution can accurately determine the drill-down direction and perform rapid drill-down, providing users with accurate data analysis and improving user experience.

[0040] Figure 2 This is a flowchart illustrating a data drill-down method according to an exemplary embodiment. For example... Figure 2 As shown, step S12 includes the following steps.

[0041] In step S121, the user's role and the data hierarchy accessible to the user role are determined.

[0042] For example, BI systems often face the problem of multiple users, each with different roles, granted permissions, and allowed access to different data levels. Therefore, to ensure that each user can only access the data corresponding to their role and avoid data leakage, access control needs to be implemented for different roles. Before data drilling, the user's role can be obtained. It is understood that the correspondence between the user role and the data level that the user role can access can be preset in the corresponding BI system, and the user role can be determined based on the user account logged in to the BI system.

[0043] In step S122, a data sandbox is dynamically generated based on the database and the data hierarchy accessible to the user.

[0044] For example, after determining the data level accessible to the user, a corresponding data sandbox can be generated from the database based on the accessible data level. The data sandbox can contain all the data accessible to the user.

[0045] In step S123, the data in the data sandbox is subjected to field-level filtering and desensitization processing.

[0046] For example, the data in the data sandbox may contain sensitive data and data unrelated to data drilling. Therefore, field-level filtering can be used to filter fields in the data sandbox that users do not need to use, and sensitive data in the data sandbox can be de-identified. This de-identification process includes, but is not limited to, replacement, masking, generalization, encryption, etc., to ensure that even if the data is accessed by users, sensitive information will not be leaked.

[0047] For example, a data sandbox can be dynamically generated based on the user's role (such as HR / manager / executive) and the current data level (company / department / individual); and after the data sandbox is generated, field-level anonymization is performed: such as automatically hiding ID card and bank card number when drilling down to an individual, while retaining analysis fields such as job level and department.

[0048] In step S124, the data drilling path is generated based on the data drilling instruction and the desensitized data sandbox.

[0049] For example, the data sandbox determined by a user's role and the data hierarchy accessible to that role may limit the user's data drill-down scope. Therefore, after determining the data sandbox, a corresponding data drill-down path can be generated based on the data drill-down command and the anonymized data sandbox. In summary, real-time role and data hierarchy authentication during the drill-down process, combined with field-level dynamic anonymization, not only ensures the security of sensitive data but also supports flexible analysis and exploration, and avoids operation interruptions or data leaks.

[0050] Optionally, step S13 includes: performing data drilling in the data sandbox according to the data drilling path to obtain drilling data corresponding to the data drilling instruction.

[0051] For example, after the drilling path is generated, in order to ensure that the data drilled by the user is the data that is allowed to be accessed, data drilling can be performed in the data sandbox according to the data drilling path to obtain the drilling data corresponding to the data drilling instruction.

[0052] Figure 3 This is a flowchart illustrating a data drill-down method according to an exemplary embodiment. For example... Figure 3 As shown, the method also includes the following steps.

[0053] In step S14, based on the Apriori algorithm, corresponding data drilling suggestions are output according to the drilling data.

[0054] For example, the Apriori algorithm is an association rule mining algorithm that can be used to discover frequent itemsets and association rules between items in a dataset. Therefore, the Apriori algorithm can output corresponding data drill-down suggestions based on the drill-up data. In one possible embodiment, anomaly detection is performed by running the Isolation Forest algorithm, and the drill-up data is marked as anomalous. The anomalous data (and normal data) is then transformed into a form suitable for association rule mining. The Apriori algorithm is then run again to generate association rules, and then targeted drill-down suggestions are generated based on these rules. Furthermore, the drill-down suggestions can be displayed on a visualization panel, and can be represented as links. When a user clicks on a drill-down suggestion, data drill-down is performed based on that suggestion.

[0055] For example, in the above embodiments, the Isolation Forest algorithm can identify outliers in the data (e.g., departments or individuals whose cost data deviates from the norm). Then, it uses association rule mining (such as the Apriori algorithm) on the outlier data (and possibly combined with normal data) to discover frequent patterns in the outlier data, thereby providing drill-down suggestions. For example, if a frequent pattern of "high personnel cost data" and "high turnover rate" is found in the outlier data, then the user can be recommended to drill down into the personnel cost situation of departing employees. The results of anomaly detection serve as one of the inputs for root cause recommendation. Simultaneously, root cause recommendation can also be combined with other data (such as normal data) for comparative analysis to discover patterns unique to the outlier data. In summary, this disclosure can automatically detect data anomalies and recommend the optimal drill-down path through AI algorithms (such as Isolation Forest, Apriori, etc.), reducing the user's cognitive load, quickly locating the root cause of data anomalies, and thus improving the depth and accuracy of analysis.

[0056] The above technical solution obtains the user's data drill-down command; generates a corresponding data drill-down path based on the command; and performs data drill-down in the database according to the drill-down path to obtain the drill-down data corresponding to the command. This solution can accurately determine the drill-down direction and perform rapid drill-down, providing users with accurate data analysis and improving user experience.

[0057] Figure 4 This is a schematic diagram illustrating a data drilling apparatus 400 according to an exemplary embodiment. Figure 4 As shown, the device 400 includes: an acquisition module 410, a generation module 420, and a drilling module 430; The acquisition module 410 is used to acquire the user's data drill-down instructions; The generation module 420 is used to generate a corresponding data drilling path according to the data drilling instruction; The drilling module 430 is used to perform data drilling in the database according to the data drilling path and obtain drilling data corresponding to the data drilling instruction.

[0058] Optionally, the data drilling instructions include: text instructions, voice instructions, gesture instructions, and touch fusion instructions.

[0059] Optionally, the generation module 420 includes: a first determining submodule, a second determining module, a processing submodule, and a generation submodule; The first determination submodule is used to determine the user's user role and the data level that the user role can access; The second determination submodule is used to dynamically generate a data sandbox based on the database and the data hierarchy accessible to the user. The processing submodule is used to perform field-level filtering and data anonymization on the data in the data sandbox. The generation submodule is used to generate the data drilling path based on the data drilling instruction and the data sandbox after desensitization.

[0060] Optionally, the drilling module 430 is used to perform data drilling in the data sandbox according to the data drilling path to obtain drilling data corresponding to the data drilling instruction.

[0061] Optionally, the generation module 420 is also used to generate a corresponding data drilling path based on the data drilling instruction according to the isolated forest algorithm.

[0062] Optionally, the generation module 420 is also used to output corresponding data drilling suggestions based on the drilling data using the Apriori algorithm.

[0063] Optionally, the device 400 includes a visualization module for rendering and visualizing the drill data via a visualization panel.

[0064] The above technical solution obtains the user's data drill-down command; generates a corresponding data drill-down path based on the command; and performs data drill-down in the database according to the drill-down path to obtain the drill-down data corresponding to the command. This solution can accurately determine the drill-down direction and perform rapid drill-down, providing users with accurate data analysis and improving user experience.

[0065] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0066] Figure 5 This is a block diagram illustrating an electronic device 500 according to an exemplary embodiment. For example... Figure 5 As shown, the electronic device 500 may include a processor 501 and a memory 502. The electronic device 500 may also include one or more of a multimedia component 503, an input / output (I / O) interface 504, and a communication component 505.

[0067] The processor 501 controls the overall operation of the electronic device 500 to complete all or part of the steps in the data drilling method described above. The memory 502 stores various types of data to support the operation of the electronic device 500. This data may include, for example, instructions for any application or method operating on the electronic device 500, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 503 may include a screen and audio components. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 502 or transmitted via communication component 505. The audio component also includes at least one speaker for outputting audio signals. I / O interface 504 provides an interface between processor 501 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical. Communication component 505 is used for wired or wireless communication between the electronic device 500 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IoT, eMTC, or other 5G technologies, or combinations thereof, is not limited here. Therefore, the corresponding communication component 505 may include: a Wi-Fi module, a Bluetooth module, an NFC module, etc.

[0068] In an exemplary embodiment, the electronic device 500 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the data drilling method described above.

[0069] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the data drilling method described above. For example, the computer-readable storage medium may be the memory 502 including the program instructions described above, which may be executed by the processor 501 of the electronic device 500 to complete the data drilling method described above.

[0070] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the data drilling method described above when executed by the programmable device.

[0071] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0072] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0073] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A data drill-down method, characterized in that, include: Obtain user data drill-down instructions; Generate the corresponding data drilling path according to the data drilling instruction; Data drilling is performed in the database according to the data drilling path to obtain the drilling data corresponding to the data drilling instruction.

2. The method according to claim 1, characterized in that, The data drilling instructions include: text instructions, voice instructions, gesture instructions, and touch fusion instructions.

3. The method according to claim 1, characterized in that, The step of generating the corresponding data drilling path according to the data drilling instruction includes: Determine the user's user role and the data hierarchy accessible to the user role; Based on the database, a data sandbox is dynamically generated according to the user-accessible data hierarchy; The data in the data sandbox is subjected to field-level filtering and anonymization. The data drilling path is generated based on the data drilling instructions and the data sandbox after desensitization processing.

4. The method according to claim 3, characterized in that, Data drilling is performed in the database according to the data drilling path to obtain the drill-up data corresponding to the data drilling instruction, including: Data drilling is performed in the data sandbox according to the data drilling path to obtain drilling data corresponding to the data drilling instruction.

5. The method according to claim 1, characterized in that, The step of generating the corresponding data drilling path according to the data drilling instruction includes: Based on the isolated forest algorithm, a corresponding data drilling path is generated according to the data drilling instructions.

6. The method according to claim 5, characterized in that, The method further includes: Based on the Apriori algorithm, corresponding data drilling suggestions are output according to the drilling data.

7. The method according to claim 1, characterized in that, The method includes: The drill data is rendered and displayed visually through a visualization panel.

8. A data drilling device, characterized in that, include: The acquisition module is used to acquire the user's data drill-down instructions; The generation module is used to generate a corresponding data drilling path according to the data drilling instruction; The drilling module is used to perform data drilling in the database according to the data drilling path and obtain drilling data corresponding to the data drilling instruction.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

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