A method, apparatus and computing device for creating visual data

Abstract

The present invention discloses a method for creating visual data, comprising the steps of: displaying selectable dimensions and dimension combinations to a user according to the dimension combinations stored in the underlying data and the model database; prompting the user to drill up and / or drill down on the dimensions; prompting the user to create a new dimension combination and encode it; calculating the encoded dimension combination data, and using the encoded dimension combination data to calculate the indicators set by the user; partitioning and storing the dimension combination data and indicators corresponding to each code; when the user establishes a data model, prompting the user to select the codes and indicators involved in the data model; after the user selects the codes and indicators involved in the data model, pushing the data model to the model database. The present invention also discloses a corresponding visual data creation system and computing device. According to the technical solution of the present invention, it is possible to realize the combined display of cross-theme indicators, the model can be flexibly expanded, and business needs can be better met.

Description

Technical Field

[0001] The present invention relates to the field of computer and Internet technologies, and in particular to a method, device and computing equipment for creating visual data. Background Art

[0002] The visualization of data is one of the important ways to empower data output from data warehouses. Currently, the more widely used visualization methods include: self-service reports, indicator platforms, self-built data platforms, large screens in corporate exhibition halls, etc. The implementation methods are basically the same, relying on the data warehouse common layer model and dimensional data, organizing data according to project and business application requirements, and generating result table data that meets the requirements based on highly aggregated data. The result data set is configured through BI tools and then presented in a visual interface. In this way, users can intuitively discover the data trend, the proportion of data in each dimension, and the year-on-year and month-on-month fluctuations of the data.

[0003] The summary result data belongs to the data warehouse presentation layer model, which is mostly business-oriented. It aggregates related indicator data according to different dimensions and stores them in time periods such as days, weeks, months, quarters, and years. When analyzing indicator data from different dimensional perspectives and multi-dimensional combinations, it is necessary to develop and design multiple model result tables to store indicator data separately. Data presentation can be developed through customized interfaces to read data front-end presentation, or visual BI tools can be used to directly drag and drop corresponding dimensions and indicator configurations to different components for direct presentation. The design and implementation of the presentation layer data model are mostly business-driven, analyzing data from multiple dimensions. The business perspective is wide and the business changes frequently. In order to meet the ever-changing business needs, it is necessary to continuously develop data models. Each model table is the indicator data calculated by different dimensional combinations, and the model caliber logic and backtracking data are constantly changed and adjusted according to business needs; the BI reporting tool makes corresponding adjustments based on the changes in the data model table to better meet the data visualization presentation. Currently, Kylin, as an OLAP analytical data warehouse, provides the ability to integrate multiple data visualization tools. It can define data models and build cubes in the basic layer data set, realize dimension cross-cutting, indicator data drill-up, drill-down, slicing, and block analysis, and can connect to BI tools to realize multi-dimensional visualization analysis and presentation of data. When making decisions and analyzing data, business parties often view different subject indicators of the same dimension in a centralized manner. However, the multi-dimensional data processing of the above data warehouse is for the same subject domain, and indicators cannot be extended across subject domains. In addition, since multiple subject domains involve multiple cube creation processes, the overhead of storing underlying detailed data is increased, the performance is low when deduplicating massive data, and the cube processing takes too long. When adding new indicators, the cube needs to be rebuilt, and the original indicators need to be rebuilt before they can be viewed, which cannot meet the stability of data presentation.

[0004] With the development of business, a large number of model data tables will be added. The model reusability is low, script execution cannot be concurrent, and backtracking historical data requires updating the entire table. BI report configuration is cumbersome and highly repetitive. Changes to model tables must be accompanied by corresponding adjustments to the visualization tool configuration, which makes operation inflexible and inconvenient.

[0005] Therefore, a visual data creation technology is needed to solve the problems existing in the above technical solutions. Summary of the invention

[0006] To this end, the present invention provides a method, apparatus and computing device for creating visual data to solve or at least alleviate the above problems.

[0007] According to one aspect of the present invention, a method for creating visual data is provided, comprising: displaying selectable dimensions and dimension combinations to a user based on the underlying data and dimension combinations stored in a model database; prompting the user to drill up and / or drill down on the dimensions after the user selects the dimensions and / or dimension combinations; prompting the user to create a new dimension combination and encode the new dimension combination; calculating the encoded dimension combination data, and using the encoded dimension combination data to calculate the indicators set by the user; partitioning and storing the dimension combination data and the indicators corresponding to each code; when the user establishes a data model, prompting the user to select the codes and indicators involved in the data model; after the user selects the codes and indicators involved in the data model, pushing the data model to the model database.

[0008] Optionally, before presenting selectable dimensions and dimension combinations to the user based on the underlying data and the dimension combinations stored in the model database, the method further includes: pre-processing the underlying data.

[0009] Optionally, the pre-processing comprises merging identical dimensions.

[0010] Optionally, calculating the encoded dimensions and dimension combination data includes: calculating each encoded dimension and each dimension combination data simultaneously.

[0011] Optionally, the simultaneously calculating the encoded dimensions and the combined data of the dimensions includes: simultaneously calculating the encoded dimensions and the combined data of the dimensions in a T+1 manner.

[0012] Optionally, the encoding adopts binary encoding, the number of encoding bits is the total number of dimensions included in all dimension combinations, and each bit in the encoding represents one dimension.

[0013] According to one aspect of the present invention, a visual data creation device is provided, including: a first selection module, suitable for displaying selectable dimensions and dimension combinations to a user based on underlying data and dimension combinations stored in a model database; a drill-up and drill-down module, suitable for prompting the user to perform dimension drill-up and / or drill-down after the user selects a dimension and / or dimension combination; an encoding module, suitable for prompting the user to create a new dimension combination and encode the new dimension combination; a calculation module, suitable for calculating the encoded dimension combination data, and using the encoded dimension combination data to calculate the indicators set by the user; a storage module, suitable for partitioning and storing the dimension combination data corresponding to each code and the indicators; a second selection module, suitable for prompting the user to select the code and indicator involved in the data model when the user establishes the data model; and a data set registration module, suitable for pushing the data model to the model database after the user selects the code and indicator involved in the data model.

[0014] Optionally, before presenting selectable dimensions and dimension combinations to the user based on the underlying data and the dimension combinations stored in the model database, the method further includes: pre-processing the underlying data.

[0015] Optionally, the pre-processing comprises merging identical dimensions.

[0016] Optionally, calculating the encoded dimensions and dimension combination data includes: calculating each encoded dimension and each dimension combination data simultaneously.

[0017] Optionally, the simultaneously calculating the encoded dimensions and the combined data of the dimensions includes: simultaneously calculating the encoded dimensions and the combined data of the dimensions in a T+1 manner.

[0018] Optionally, the encoding adopts binary encoding, the number of encoding bits is the total number of dimensions included in all dimension combinations, and each bit in the encoding represents one dimension.

[0019] According to one aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for being executed by the at least one processor, and the program instructions include instructions for executing the visual data creation method as described above.

[0020] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the visualization data creation method as described above.

[0021] According to the technical solution of the present invention, a method and device for creating visual data are provided, which encode each dimensional combination and partition the calculation results of the indicators of each dimensional combination, so as to realize the combined display of cross-subject indicators, flexibly expand the model, better meet business needs, support BI tools to flexibly and quickly configure visual display operations, have a high degree of model reuse, and greatly shorten the model development time.

[0022] In addition, the present invention calculates the indexes for each dimension combination in advance, and the data display directly uses the result data, which makes data reading more convenient. In addition, the present invention improves the script logic, and can support the concurrent execution of massive data deduplication scripts and the concurrent execution of each index data calculation script, thereby improving data processing efficiency.

[0023] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0025] Figure 1 A schematic diagram of the structure of a data visualization system according to an embodiment of the present invention is shown;

[0026] Figure 2 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown;

[0027] Figure 3 A schematic flow chart showing a method for creating visualization data according to an embodiment of the present invention; and

[0028] Figure 4 A schematic diagram of the design process of a data model according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0030] Figure 1 FIG. 4 shows a schematic diagram of the structure of a data visualization system according to an embodiment of the present invention. Figure 1 As shown, the computer 101 can be implemented using a PC, a server, or other devices with data computing and processing capabilities. The computer 101 includes at least an input device, a display device, and a computing device 200, wherein the input device and the display device can be separate or integrated (such as a human-computer interaction interface).

[0031] In an embodiment of the present invention, the computing device 200 is suitable for executing the visualization data creation method, and the BI tool is installed in the computing device 200. The data created by the visualization data creation method is processed by the BI tool and displayed on the visualization interface 102. The visualization data creation method 300 of the present invention will be described in detail below.

[0032] Figure 2 FIG. 2 shows a block diagram of a computing device 200 according to an embodiment of the present invention. Figure 2 As shown, in a basic configuration 202, computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between processor 204 and system memory 206.

[0033] Depending on the desired configuration, the processor 204 can be any type of processing, including but not limited to: a microprocessor (UP), a microcontroller (UC), a digital information processor (DSP), or any combination thereof. The processor 204 can include one or more levels of cache such as a primary cache 210 and a secondary cache 212, a processor core 214, and registers 216. An example processor core 214 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller 218 can be used with the processor 204, or in some implementations, the memory controller 218 can be an internal part of the processor 204.

[0034] Depending on the desired configuration, the system memory 206 can be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. The system memory 206 may include an operating system 220, one or more applications 222, and program data 224. The application 222 is actually a plurality of program instructions, which are used to instruct the processor 204 to perform corresponding operations. In some embodiments, the application 222 can be arranged to cause the processor 204 to operate using the program data 224 on the operating system.

[0035] The computing device 200 may also include a storage interface bus 234. The storage interface bus 234 enables communication from storage devices 232 (e.g., removable storage 236 and non-removable storage 238) to the basic configuration 202 via the bus / interface controller 230. At least a portion of the operating system 220, the application 222, and the data 224 may be stored on the removable storage 236 and / or the non-removable storage 238, and when the computing device 200 is powered on or the application 222 is to be executed, it is loaded into the system memory 206 via the storage interface bus 234 and executed by the one or more processors 204.

[0036] The computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus / interface controller 230. Example output devices 242 include a graphics processing unit 248 and an audio processing unit 250. They can be configured to facilitate communication with various external devices such as a display or speakers via one or more A / V ports 252. Example peripheral interfaces 244 may include a serial interface controller 254 and a parallel interface controller 256, which may be configured to facilitate communication with external devices such as input devices (e.g., keyboards, mice, pens, voice input devices, touch input devices) or other peripherals (e.g., printers, scanners, etc.) via one or more I / O ports 258. Example communication devices 246 may include a network controller 260, which may be arranged to facilitate communication with one or more other computing devices 262 via a network communication link via one or more communication ports 264.

[0037] A network communication link can be an example of a communication medium. Communication media can generally be embodied as computer-readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium. A "modulated data signal" can be a signal in which one or more of its data sets or its changes can be performed in the signal in a manner that encodes information. As non-limiting examples, communication media can include wired media such as a wired network or a dedicated line network, and various wireless media such as sound, radio frequency (RF), microwave, infrared (IR) or other wireless media. The term computer-readable medium used herein can include both storage media and communication media.

[0038] In the computing device 200 according to the present invention, the application 222 includes multiple program instructions for executing the interface request processing method 300, which can instruct the processor 204 to execute the interface request processing method 300 of the present invention so that the computing device 200 executes the interface request processing method 300 of the present invention.

[0039] Figure 3 FIG. 3 is a flowchart of a method 300 for creating visual data according to an embodiment of the present invention. The method 300 is suitable for execution in the aforementioned computing device 200. Figure 3 As shown, the method 300 starts at step S310 .

[0040] like Figure 3 As shown, in step S310, the system prompts the user to create a new dimension combination and encode the new dimension combination. Here, the user can select the required dimensions from the dimensions stored in the model database according to the set theme, and combine the selected dimensions as needed. For each combination, the user needs to assign a code. The model database is suitable for storing data models created by the user. The coding order is sorted according to the dimensional divisions of each layer of the dimension split and drilled down, and the dimensions are sorted in order from total to specific. If there are new dimensions, the new dimensions are still encoded according to the above coding method.

[0041] According to one embodiment, the encoding method adopts a binary encoding method, and the number of encoding bits is the total number of dimensions included in all dimension combinations. Each bit in the encoding represents a dimension, "0" indicates that a dimension is not included in the dimension combination, and "1" indicates that a dimension is included in the dimension combination. For example, a certain business requires a total of 4 dimensions, namely dim1, dim2, dim3, and dim4. Then, the number of encoding bits is set to 4 bits, and the first to fourth bits represent the dimensions dim1, dim2, dim3, and dim4 respectively. Then the code "1000" represents the indicator result aggregated by dim1, the code "1100" represents the result aggregated by dim1 and dim2, and the code "1010" represents the result aggregated by dim1 and dim3. The advantage of binary encoding is that the dimensions included in the encoding can be directly determined based on the encoding.

[0042] According to one embodiment, before prompting the user to create a new dimension combination and encoding the new dimension combination, method 300 also includes the steps of: displaying selectable dimensions and dimension combinations to the user based on the underlying data and dimension combinations stored in the model database; and prompting the user to drill up and / or drill down on the dimensions after the user selects a dimension and / or dimension combination.

[0043] It should be noted that if no data model is stored in the model database, the system will display the selectable dimensions to the user based on the dimensions contained in the underlying data and the subject set by the user; if several data models are stored in the model database, the system will display the selectable dimensions and dimension combinations to the user based on the dimensions contained in the underlying data and the model database and the subject set by the user. The user selects the required dimensions and / or dimension combinations from the selectable dimensions and dimension combinations provided by the system according to business needs, and then the system will prompt the user to split and drill down on each dimension in the selected dimensions and dimension combinations. The user can split and drill down or drill up on some or all of the dimensions as needed to obtain a series of new dimensions. Next, the user needs to recombine the selected dimensions and the new dimensions obtained by drilling down or drilling up, and encode the new dimension combination, that is, execute step S310.

[0044] According to one embodiment, before displaying selectable dimensions and dimension combinations to the user based on the underlying data and the dimension combinations stored in the model database, the method 300 further includes the step of pre-processing the underlying data. It should be noted that the pre-processing here includes merging the same dimensions, integrating the detailed measurement data of different dimensions for a business fact, standardizing the enumeration values ​​and corresponding serial number codes of each dimension, and normalizing invalid dimensions.

[0045] Subsequently, in step S320, the encoded dimension combination data is calculated, and the encoded dimension combination data is used to calculate the user-set indicators. Here, after the user assigns a code to the newly created dimension combination, the system automatically calculates the dimension combination data corresponding to the code, such as aggregation calculation of the same dimension data, aggregation function of big data calculation, such as aggregation of 100 detailed data of dimension a, and obtains a result value: the number of dimension a is 100, or the indicators set by the user are cross-calculated using the data of each dimension, such as the app system, app version number, device model, etc. related to traffic, how many people visit the iPhone 12 model 10.3.2 version in a day, how many pages are visited, and other indicators. In this embodiment, indicators are defined with specific names, and dimension combinations are marked with codes to indicate which dimensions are used to calculate indicators. The enumeration value combination of specific dimensions is to calculate the corresponding indicator value, such as the UV indicator, which includes three dimensions of province, city, and county. The dimension combination code is 111 / 110 / 100, and the calculation forms the data shown in Table 1:

[0046] Table 1 Calculation results of UV index

[0047] Province city county uv Beijing Beijing Haidian District 100 Henan Zhengzhou Xinzheng 200

[0048] In this embodiment, the addition mode is used to split the dimensions, that is, drill down to the fine-grained dimension combination. The addition mode is generally used in the industry to describe the association relationship between indicators. Here, the roll-up and drill-down operations of the specific enumeration values ​​of the dimension combination are referenced. It is effective for specific indicators, such as the pv indicator. The pv indicator of the province dimension drills down to the pv under the province and city dimension combination. The sum of the pvs corresponding to the enumeration values ​​of all cities under each province is the pv of the province. For the browsing time indicator, the browsing time calculated by the app system (Android and iOS) dimension, drills down to the browsing time calculated by the APP system and app version number dimension combination, and the sum of the browsing time corresponding to all app version numbers under the corresponding system is the total time of the app system. For some visualization display tools (with built-in summary function), for specific indicators, you only need to use the finest-grained dimension combination data to complete the roll-up and summary by yourself.

[0049] According to one embodiment, when the system calculates the combined data of each dimension, the logic of the existing script is changed to calculate each dimension and each indicator simultaneously. Specifically, the calculation can be performed in a T+1 manner or other manners.

[0050] Since method 300 is implemented in the data warehouse presentation layer, for data of the same dimension of different themes, the data of each subject in the associated general layer (also called semantic layer or aggregation layer) model can be merged and processed, and flexibly integrated. The multi-dimensional data indicators can be calculated in advance in a T+1 manner, and massive data deduplication scripts can be executed concurrently. The data presentation directly uses the pre-calculated result data, which is easy and efficient to read.

[0051] Subsequently, in step S330, the indicators calculated from the dimension combination data corresponding to each code are partitioned and stored. For example, the dimension combination code 1100 is used to mark the various indicator data calculated under the dimension combination A and B, and the dimension combination 1001 is used to mark the various indicator data calculated under the dimension combination A and D.

[0052] Subsequently, in step S340, when the user establishes a data model, the user is prompted to select the codes and indicators involved in the data model. The user selects the required codes and indicators according to the set theme, and the system can display the available codes and indicators on the computer display. The selection method can be click, drag, or input codes and indicators. After the selection is completed, the data model is considered to be established. Here, the various dimension combinations with codes and the various indicators calculated by the corresponding dimension combinations are solidified into a data model. The indicators are fixed and the dimension combinations can be redundant. A data model can be created as partitioned storage, such as area A (marked with dimension combination code 1100) stores the various indicator data calculated under the A and B dimension combinations, and area B (marked with dimension combination 1001) stores the various indicator data calculated by the A and D dimension combinations. A model can contain extended indicators (indicators have a combination of dimensions with an inclusion relationship), such as indicators belonging to different subject domains and having a certain correlation (such as indicators A and B are calculated to derive indicators such as conversion rate and proportion). Indicator A involves dimension combinations such as a / b / c and b / c / d, and indicator B involves dimension combinations such as a / c / d, a / b / d, and c / d / e. When designing the model, the two indicators A and B calculated from the dimension combination of a / b / c / d / e are stored in the same model, that is, degenerating dimensions, adding indicators, flexible expansion, and saving storage.

[0053] Subsequently, in step S350, the data model established in step S340 is pushed to the model database (such as ElasticSearch or Hbase, etc.) for storage, completing the registration of the data set. The BI tool can query and retrieve a data model in the model database, directly read the registered data set, and filter the data that meets the business needs according to the dimension code, realize data drilling up and down, and immediately present the data by dragging the dimension (according to the dragged dimension, combine and map the data using the corresponding dimension code) and the indicator information to the corresponding component (such as indicator card, line chart, pie chart, and table, etc.), and quickly complete the data visualization configuration.

[0054] According to the method 300 of the present invention, the dimension combination is encoded, which effectively solves the chaotic status quo of multi-dimensional data development. According to the actual situation, multiple dimension combination indicator data can be expanded in the same model table, reducing the number of repeated modeling developments for similar business requirements, and flexibly and conveniently completing the up-drilling and down-drilling display analysis of data. Different encoding partitions are stored to facilitate data backtracking. All approximate dimension combination demand indicators are stored with the same model, which reduces the number of models and saves management costs. When using, only different dimension codes need to be screened to obtain the desired dimension and indicator data, and the model is highly reusable. When configuring the BI tool, dimensions and indicators can be flexibly dragged and dropped. When business requirements change, only the corresponding codes need to be adjusted to achieve it, which greatly saves configuration time and reduces cumbersome configuration and the use of custom SQL. The dimension combination and indicators that have been encoded are calculated in advance, which is equivalent to directly processing the pre-processed underlying data into the final summary data, effectively reducing the time of refreshing the cube, and can well support the aggregation and de-duplication of massive data, and the BI tool can directly read the result data, which is convenient and fast.

[0055] In addition, the present invention improves the existing script editing logic. When the number of dimension combinations is large (for example, more than 3), a parallel script is used to simultaneously calculate each dimension and each indicator, and the concurrent execution of data processing and backtracking data does not affect each other.

[0056] In view of the uncertainty and volatility of Internet companies' business, Method 300 overcomes the problem of instability of the data warehouse presentation layer model (that is, the need to constantly design new models to meet new business needs), flexibly expands to cope with ever-changing business scenarios, quickly realizes data visualization, and reduces the need to frequently adjust BI tools due to business changes.

[0057] Taking the Autohome app as an example, the browsing log traffic data generated by the app every day is the basic data. Now we start to count the pv (page views) and uv (unique visitors / devices) indicators in multiple dimensions.

[0058] Assuming that the pre-statisticed dimensions include: app system (ios / android), app version number, device model, operator, car series level, car series, car model, province, and city, according to the design method of the present invention, the above 9 dimensions are fixed in the above positions in turn, and each position is assigned with binary values ​​of 0 and 1, 0 represents that the dimension is not used, and 1 represents that the dimension is used. Then, the permutations and combinations can be generated in various ways of multi-dimensional combinations, such as 110011100, which represents the statistics of the corresponding pv and uv indicators according to the app system, app version number, car series level, car series, and car model dimension combination, as shown in Table 2.

[0059] Table 2 Correspondence between binary encoding and dimension

[0060]

[0061] The PV and browsing time indicators calculated based on the combination of app system / app version number / car level / car series / car model / province / city are shown in Table 3.

[0062] Table 3 Calculation results of pv and browsing time indicators

[0063]

[0064] The calculation results of each combination code are stored in partitions. When expanding indicators, you only need to refresh and update the data under the corresponding dimension code partition; each dimension code combination is calculated by an independent script, and each dimension code combination is executed concurrently to improve calculation efficiency. The presentation layer model only saves the final calculation results to save storage space; the data front-end visualization can quickly read the required data according to the dimension code and present it immediately, which is convenient for configuration and use. The corresponding binary code combination can also be converted to decimal, and the table can be organized to record the specific dimension combination corresponding to each number (decimal) for visualization reference.

[0065] Figure 4 FIG. 2 shows a schematic diagram of a design process of a data model according to an embodiment of the present invention. Figure 4 As shown, when designing a new data model, the user first confirms the dimensions and dimension levels according to business needs, confirms the number of indicators involved, and then summarizes the dimensions contained in each theme of the general layer. Next, split and drill down on each dimension, then roll up and summarize the dimensions, encode the combination of each dimension, and develop data according to the above rules. After the development is completed and the results are run, it means that the model design is complete. Finally, register the data set, that is, push the model to a specific database for storage. The database here will serve as the database of the BI data platform.

[0066] A visual data creation device according to an embodiment of the present invention includes: a first selection module, adapted to display selectable dimensions and dimension combinations to a user according to underlying data and dimension combinations stored in a model database; a drill-up and drill-down module, adapted to prompt a user to perform dimension drill-up and / or drill-down after the user selects a dimension and / or dimension combination; an encoding module, adapted to prompt a user to create a new dimension combination and encode the new dimension combination; a calculation module, adapted to calculate the encoded dimension combination data and calculate the user-set index using the encoded dimension combination data; a storage module, adapted to partition and store the dimension combination data and index corresponding to each code; a second selection module, adapted to prompt a user to select the code and index involved in the data model when the user establishes a data model; and a data set registration module, adapted to push the data model to the model database after the user selects the code and index involved in the data model.

[0067] As a preferred embodiment of the present invention, before displaying selectable dimensions and dimension combinations to the user based on the underlying data and the dimension combinations stored in the model database, the method further includes: pre-processing the underlying data.

[0068] As a preferred embodiment of the present invention, the pre-processing includes merging the same dimensions.

[0069] As a preferred embodiment of the present invention, calculating the encoded dimensions and dimension combination data includes: calculating each encoded dimension and each dimension combination data simultaneously.

[0070] As a preferred embodiment of the present invention, the simultaneously calculating the encoded dimensions and the combined data of the dimensions includes: simultaneously calculating the encoded dimensions and the combined data of the dimensions in a T+1 manner.

[0071] As a preferred embodiment of the present invention, the encoding adopts binary encoding, the number of encoding bits is the total number of dimensions included in all dimension combinations, and each bit in the encoding represents one dimension.

[0072] The above-mentioned visualization data creation device is implemented by computer software, and its principle and effect are the same as those of the above-mentioned visualization data creation method, which will not be repeated here.

[0073] A9. The device as described in A8 is characterized in that the pre-processing includes merging the same dimensions. A10. The device as described in A7 is characterized in that the calculation of the encoded dimensions and the dimension combination data includes: simultaneously calculating the encoded dimensions and the dimension combination data. A11. The device as described in A7 is characterized in that the simultaneous calculation of the encoded dimensions and the dimension combination data includes: simultaneously calculating the encoded dimensions and the dimension combination data using a T+1 method. A12. The device as described in A7 is characterized in that the encoding adopts a binary encoding method, the number of encoding bits is the total number of dimensions contained in all dimensional combinations, and each bit in the code represents a dimension.

[0074] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present invention, or some aspects or parts of the method and apparatus of the present invention may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0075] In the case where the program code is executed on a programmable computer, the mobile terminal generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. The memory is configured to store the program code; the processor is configured to execute the interface request processing method of the present invention according to the instructions in the program code stored in the memory.

[0076] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0077] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0078] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0079] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0080] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0081] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0082] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0083] In addition, some of the embodiments are described herein as a combination of methods or method elements that can be implemented by a processor of a computer system or by other devices that perform functions. Therefore, a processor with necessary instructions for implementing a method or method element forms a device for implementing the method or method element. In addition, the elements of the device embodiment herein are examples of devices that are used to implement the functions performed by the elements for the purpose of implementing the invention.

[0084] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0085] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative and not restrictive with respect to the scope of the present invention, and the scope of the present invention is defined by the appended claims.

Claims

1. A method for creating visual data, comprising: Displaying selectable dimensions and dimension combinations to users based on the underlying data and dimension combinations stored in the model database; After the user selects a dimension and / or a combination of dimensions, the user is prompted to drill up and / or drill down the dimension to obtain a new dimension; Prompt the user to create a new dimension combination according to the selected dimension and the obtained new dimension, and encode the new dimension combination. The encoding adopts a binary encoding method. The number of encoding bits is the total number of dimensions included in all dimension combinations. Each bit in the encoding represents a dimension. Calculate the encoded dimension combination data, and use the encoded dimension combination data to calculate the indicators set by the user; The dimension combination data corresponding to each code and the index are stored in partitions; When the user establishes a data model, the user is prompted to select the coding and indicators involved in the data model; After the user selects one or more codes and indicators involved in the data model, the data model is pushed to the model database.

2. The method according to claim 1, characterized in that Before presenting selectable dimensions and dimension combinations to the user according to the underlying data and the dimension combinations stored in the model database, the method further includes: Pre-process the underlying data.

3. The method according to claim 2, characterized in that The pre-processing includes merging identical dimensions.

4. The method according to claim 1, characterized in that The calculations on the encoded dimensions and dimension combination data include: The encoded data of each dimension and the combination of each dimension are calculated simultaneously.

5. The method according to claim 1, characterized in that The simultaneous calculation of the encoded data of each dimension and the combined data of each dimension comprises: The T+1 method is used to simultaneously calculate the encoded data of each dimension and the combination of each dimension.

6. A visual data creation device, comprising: A first selection module, adapted to display selectable dimensions and dimension combinations to the user based on the underlying data and dimension combinations stored in the model database; A drill-up and drill-down module, adapted to prompt the user to drill up and / or drill down on a dimension after the user has selected a dimension and / or a combination of dimensions, so as to obtain a new dimension; An encoding module, adapted to prompt the user to create a new dimension combination according to the selected dimension and the obtained new dimension, and encode the new dimension combination, wherein the encoding adopts a binary encoding method, the number of encoding bits is the total number of dimensions included in all dimension combinations, and each bit in the encoding represents a dimension; A calculation module, adapted to calculate the encoded dimensional combination data, and to calculate the index set by the user using the encoded dimensional combination data; A storage module, adapted to partition and store the dimension combination data corresponding to each code and the index; A second selection module, adapted to prompt the user to select the codes and indicators involved in the data model when the user establishes the data model; and The data set registration module is adapted to push the data model to the model database after the user selects one or more codes and indicators involved in the data model.

7. The device according to claim 6, characterized in that Before presenting selectable dimensions and dimension combinations to the user according to the underlying data and the dimension combinations stored in the model database, the method further includes: Pre-process the underlying data.

8. The device according to claim 7, characterized in that The pre-processing includes merging identical dimensions.

9. The device according to claim 6, characterized in that The calculations on the encoded dimensions and dimension combination data include: The encoded data of each dimension and the combination of each dimension are calculated simultaneously.

10. The device according to claim 6, characterized in that The simultaneous calculation of the encoded data of each dimension and the combined data of each dimension comprises: The T+1 method is used to simultaneously calculate the encoded data of each dimension and the combination of each dimension.

11. A computing device comprising: at least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the visualization data creation method according to any one of claims 1 to 5.

12. A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device executes the visualization data creation method according to any one of claims 1 to 5.

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