KR20220067820A - The bigdata intergared-processing user interface method for increasing interoperability of heterogeneous platforms - Google Patents

Abstract

The present invention is proposed to increase interoperability between heterogeneous platforms when utilizing big data. To this end, it is necessary to provide an environment where anyone can easily use big data suitable to their needs rather than relying on the level of knowledge of users who want to use big data. The present invention suggests a big data integrated-processing user interface method in order to meet this demand. After a user terminal connects to a big data integrated-processing platform connected to a plurality of heterogeneous platforms, a big data integrated-processing user interface (100) is displayed on a user terminal screen, wherein the big data integrated-processing user interface (100) includes a first UI (101) and a second UI (105) in independent areas, in which the first UI (101) includes a selection menu for selecting events, and the second UI (105) is an area for visually showing a work profile according to connected parameters of a performance model by using the selection menu. When the input means moves any one event of the selection menu of the first UI (101) to the second UI (105) by drag-drop, the request operation corresponding to the selected event is activated.

Description

The BIGDATA INTERGARED-PROCESSING USER INTERFACE METHOD FOR INCREASING INTEROPERABILITY OF HETEROGENEOUS PLATFORMS to increase interoperability between heterogeneous platforms

The present invention relates to a technology for collecting big data through a platform.

In the impending 4th industrial revolution era, the importance of big data collection, analysis and processing based on digital technology and network technology is being highlighted. In this era, the value of data has risen remarkably. Then, in the market, attempts to build their own system by introducing a big data platform that can operate and manage big data appeared in various places.

Big data is collected through various sensors based on IoT technology. In addition, big data is collected from the web through crawling technology. By analyzing the big data collected in this way, it became possible to increase productivity on the one hand and provide new services as well as cost reduction and efficient management on the other hand.

However, there are various industries in the world, and there are various platforms created for collecting big data. In the process of integrated analysis of big data by various industries, there was a problem of inconsistency between data. This has resulted in lower data quality and longer time and higher costs for data purification.

In addition, the user must directly perform complex integrated calculations on the heterogeneous platform, and must go through the process of refining, analyzing, and processing data based on each analyzed and processed data result, but it was difficult to obtain an integrated result.

The inventors of the present invention have completed the present invention after long research and efforts to solve the above problems.

It is possible to develop an optimized platform that enables data integration processing by collecting a large number of data located on heterogeneous platforms. To do this, it would be useful to present a single data view to the user so that it can be used easily and conveniently based on the technology to optimize it and the user's needs.

That is, an object of the present invention is to provide a customized user interface that implements user needs while linking databases between different platforms.

The user-customized user interface of the present invention is intended to visually provide the user with an option to select each detailed step so that the user can easily use the complex steps related to data purification, analysis, processing, and interworking. A user can visually select an option by dragging and dropping while looking at such a user interface.

On the other hand, other objects not specified in the present invention will be additionally considered within the range that can be easily inferred from the following detailed description and effects thereof.

The present invention for achieving the above object as a big data integrated processing user interface method:

After the user terminal accesses the big data integrated processing platform that is connected to a plurality of heterogeneous platforms, the big data integrated processing user interface is displayed on the user terminal screen, wherein the big data integrated processing user interface is a first UI and a second UI in an independent area Including, wherein the first UI is configured with an option menu for selecting events including a plurality of platform-linked events, the second UI is an area that visually shows the flow of the performance model using the option menu,

When the input means moves any one event from the option menu of the first UI to the second UI by dragging and dropping, the parameter of the performance model corresponding to the selected event is generated, and the next one event from the option menu of the first UI When moving to the second UI by drag and drop, the corresponding parameter is created, and is sequentially connected to the previous parameter,

and when the input means presses the execution button of the big data integrated processing user interface, allowing the execution model to be completed with the events selected in the first UI and the workflow visualized in the second UI.

In the big data integrated processing user interface method according to any preferred embodiment of the present invention,

When the input means requests platform interworking in the big data integrated processing user interface, a platform interworking pop-up UI including a list of a plurality of heterogeneous platforms connected to the big data integrated processing platform and a requested operation list is displayed over the second UI It is better to further include the step of pop-up visualization.

In addition, in the big data integrated processing user interface method according to a preferred embodiment of the present invention, when the user terminal presses the execution button to complete the performance model,

The method may further include the step of allowing the simulator to simulate the completed model to activate each request task and confirm the processing process.

In the big data integrated processing user interface method according to a preferred embodiment of the present invention, the method may further include the step of the optimizer learning the completed performance model and feeding back the optimal performance model to the user terminal.

In addition, in the big data integrated processing user interface method according to a preferred embodiment of the present invention, when the input means selects a recommendation icon in the big data integrated processing user interface, the server of the big data integrated processing platform performs optimally The method may further include visualizing a recommendation model UI including recommendation frequency and execution time by popping up on the second UI to recommend a model.

According to the big data integrated processing user interface method of the present invention, all users can easily and conveniently link heterogeneous platforms based on the big data integrated processing user interface of the present invention, even if they lack specialized knowledge related to big data. In addition, a user-created big data utilization model can be derived, and big data of individual platforms can be utilized according to the recommendation and prediction algorithms registered in advance in the server.

Accordingly, the user-customized big data integrated processing user interface of the present invention can simplify the process for using rather complex big data. This made it easier to integrate, refine, and analyze big data using heterogeneous platforms by field in various fields, thereby increasing interoperability between heterogeneous platforms.

On the other hand, even if it is an effect not explicitly mentioned herein, it is added that the effects described in the following specification expected by the technical features of the present invention and their potential effects are treated as described in the specification of the present invention.

1 is a diagram schematically showing a system configuration according to a preferred embodiment of the present invention.
2 schematically shows an example of the configuration of the big data integrated processing user interface 100 of the present invention.
3 shows the concept of creating a workflow while moving the event displayed on the first UI 101 to the second UI 105 by drag and drop in the big data integrated processing user interface 100 of the present invention.
4 shows an example of an option menu UI 160 according to a preferred embodiment of the present invention.
5 shows an example of a platform interworking pop-up UI 170 according to a preferred embodiment of the present invention.
6 shows an example of a recommendation model UI 180 according to a preferred embodiment of the present invention.
7 schematically illustrates the overall process of the method of the present invention.
※ It is revealed that the accompanying drawings are exemplified as a reference for understanding the technical idea of the present invention, and the scope of the present invention is not limited thereby.

Hereinafter, the configuration of the present invention guided by various embodiments of the present invention and effects resulting from the configuration will be described with reference to the drawings. In the description of the present invention, if it is determined that related known functions are obvious to those skilled in the art and may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

1 schematically shows a system configuration according to a preferred embodiment of the present invention.

The user terminal 1 connects to the big data integrated processing platform 10 . This big data integrated processing platform is composed of one or more servers (10). And the server consists of one or more software/hardware devices. The big data integrated processing platform server 10 is connected to N (N is an integer greater than 1) different heterogeneous big data platforms 20, 20... through a communication network. And the big data integrated processing platform server 10 collects big data by accessing the databases 21, 21... of these heterogeneous big data platforms 20, 20.... As such, heterogeneous platforms are interlocked with each other.

Users are diverse and have different needs for collecting and analyzing big data. The present invention provides a means by which such various people can integrate and process big data scattered on various heterogeneous platforms according to their needs.

When the user terminal 1 wants to utilize big data after accessing the big data integrated processing platform server 10, the big data integrated processing platform server 10 provides a big data integrated processing user interface ( 100) is provided. That is, the user terminal 1 can create a performance model related to big data analysis through the big data integrated processing user interface 100 according to the user's needs in a manner described below. With respect to the performance model created by the user's needs, the simulator 12, which is a software module, performs predetermined simulations, and the optimizer 14 performs optimization. Although not shown, it is apparent to those skilled in the art that a plurality of modules for executing a series of processes such as collecting and processing big data are installed in the big data integrated processing platform server.

2 schematically shows a configuration example of a big data integrated processing user interface 100 according to a preferred embodiment of the present invention.

As shown, the big data integrated processing user interface 100 is configured to include a first UI 101 and a second UI 105 made of regions independent from each other. For example, if the first UI 101 is in the left area, the second UI 105 is located in the right area of the big data integrated processing user interface 100 .

The first UI 101 may include a performance model generator 110 and an option menu 120 . The performance model generation unit 110 includes a text box 111 for inputting the name of the performance model to be created by the user.

The option menu 120 displays an event to be included in the performance model by the user. In the illustrated example, an interworking event 121 , a search event 122 , an analysis event 123 , a refinement event 124 , and a processing event 125 for interworking a plurality of platforms are included in the option menu 120 .

In this option menu 120 , the input means of the user terminal may select a specific event and move it to the second UI 105 in the right area. Then, the moved event activates one request task to be executed by the big data integrated processing performance model. That is, the second UI 105 is an area that visually shows the step-by-step flow of one or more performance models. See Figure 3.

3 schematically illustrates a user interface method of dragging and dropping a specific event in the first UI 101 to the second UI 105 . The user inputs this performance model into the text box 111 as "Big Data of Gil-Dong Hong" and gives a file name.

The input means selects the search event 122 from the selection menu 120 and drags it, and then drops it to the second UI 105 . Then, as shown, the search parameter 150 is created and visualized in the second UI 105 .

As such, one or more workflow performance models may be generated in the second UI 105 by the drag-and-drop method of FIG. 3 . If multiple request operation parameters are selected, events such as linking each parameter, deleting a specific parameter, or applying a pre-registered exemplary workflow execution model can be executed. . Let's look at Figure 4 this time.

4 shows a configuration in which the parameters generated in the second UI 105 are connected in a preferred embodiment of the present invention. In addition, examples of the configuration and function of the option menu UI 160 that can be used are shown.

When the input means selects the right menu in the flow box, for example, the Kyung-woong option menu UI 160 for right-clicking the input means is displayed adjacent to the flow and indicates the setting screen of each parameter, and in the example shown, the search flow 150 ) to call the option menu UI 160 .

The option menu UI 160 preferably displays a plurality of icons. For example, a request task icon 161 , a connector icon 162 , a recommendation icon 163 , a delete icon 164 , and a setting icon 165 may be included. It may be displayed as text rather than an icon.

When the requested work icon 161 is pressed, a requested work box to be displayed on the screen is shown, and the requested work name can be corrected and input by double-clicking the requested work box. In addition, by bringing (loading) a previously stored workflow execution model, it is possible to check and modify it in the second UI 105 . When the connector icon 162 is pressed, a connector for connecting a plurality of parameters displayed on the second UI 105 is provided. When the recommendation icon 163 is selected, a list of performance models recommended by the recommendation algorithm is provided. When the delete icon 164 is selected, the corresponding parameter is deleted. When the setting icon 165 is pressed, a screen for detailed setting of the operation of the corresponding flow is popped up. See Figure 5.

Meanwhile, in FIG. 4 , a search flow 150 , a purification flow 151 , a processing flow 152 , and an analysis flow 153 are generated. Big data collected from multiple platforms is stored in each database. A database to be searched is selected from each database of a heterogeneous platform through the platform interworking pop-up UI 170, and the following operations, such as purification, processing, and analysis, are performed.

5 is a pop-up of a list of a plurality of heterogeneous platforms connected to the big data integrated processing platform and a requested task list over the second UI 105 when the input means requests platform interworking in the big data integrated processing user interface 100. An example of the platform-linked pop-up UI 170 that is used is shown.

In a preferred embodiment, when the interlocking event 121 of the first UI 101 is moved to the second UI 105 by drag and drop, an interlocking flow is created, and then the setting icon is pressed in the option menu UI, the platform interlocking popup UI ( 170) can be visualized. In another preferred embodiment, when the setting icon 165 is pressed in the option menu UI 160 of the search flow 150, the platform interworking pop-up UI 170 may be visualized.

This platform interworking pop-up UI 170 can be checked by registering information on a platform to be connected in advance. The registration information includes platform name, IP, PORT, DB name, DB external connection PORT, DB id, password, etc., and a list of multiple heterogeneous platforms can be displayed. It can also show a list of services. The service list may include a list related to the requested operation, for example, result data, DB integration, environment variables or settings of an installation program.

6 shows a schematic configuration of a recommendation model UI 180 that pops up on the second UI 105 when the recommendation icon 163 is selected from the above-described option menu UI according to a preferred embodiment of the present invention.

The big data integrated processing platform server shows one or more optimal recommendation model lists 181 among the recommendation and prediction algorithm models through the recommendation model UI 180 . Preferably, the recommendation frequency and execution time are provided.

As such, a plurality of events in the first UI 101 are moved to the second UI 105 by drag and drop, a plurality of parameters of the performance model are created, and then a request operation step such as connecting them or selecting a recommendation model is performed. After that, the user presses the execution button 195 of the second UI 105 . Then, the work performance model according to the big data utilization step visualized in the second UI 105 is stored and completed.

The simulator of the big data integrated processing platform server simulates the model completed by the user according to a predetermined process, and confirms the processing of each step.

Next, the optimizer learns the user's task performance model and feeds back the optimal task execution order to obtain an optimized result according to the user's needs.

In the embodiment of FIG. 7 , the process from step S100 to step S140 is performed in the big data integrated processing user interface.

After the user stores the platform information to be linked in advance, the user selects an interlocking event from the given option menu and creates interlocking parameters by dragging and dropping (S110). At this time, after dropping for interworking setting, select the setting icon and click the platform to interlock and the desired data on the pop-up platform interlocking popup UI screen. Then, drag and drop events corresponding to data purification, processing, and analysis (S120, S130, S140) to create a workflow task execution model that can utilize big data.

A work performance model is generated through simulation and optimization process (S200), and the model is stored in the result data DB (S210). Big data utilization results can be obtained by integrating and interworking data between each platform according to the task execution model requested by the user.

Through such a user interface method of the present invention, anyone can easily simplify the complicated steps for interworking between heterogeneous flats, big data processing, analysis, and purification, and obtain an optimized result quickly.

For reference, the big data integrated processing user interface method according to an embodiment of the present invention may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the present invention, or may be known and available to those skilled in the art of computer software.

Examples of computer-readable media include hard disks, magnetic media such as floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floppy disks, and ROM, RAM, A hardware device specifically configured to store and execute program instructions, such as flash memory, may be included. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter as well as machine codes such as those generated by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

The protection scope of the present invention is not limited to the description and expression of the embodiments explicitly described above. In addition, it is added once again that the protection scope of the present invention cannot be limited due to obvious changes or substitutions in the technical field to which the present invention pertains.

Claims (5)

After the user terminal accesses the big data integrated processing platform that is connected to a plurality of heterogeneous platforms, the big data integrated processing user interface is displayed on the user terminal screen, wherein the big data integrated processing user interface is a first UI and a second UI in an independent area Including, wherein the first UI is configured with an option menu for selecting events including a plurality of platform-linked events, the second UI is an area that visually shows the flow of the performance model using the option menu,
When the input means moves any one event from the option menu of the first UI to the second UI by dragging and dropping, the parameter of the performance model corresponding to the selected event is generated, and the next one event from the option menu of the first UI is moved to the second UI by drag and drop so that the corresponding parameter is created and sequentially connected to the previous parameter,
When the input means presses the execution button of the big data integrated processing user interface, the big data integrated processing user interface comprising the step of completing the execution model with the events selected in the first UI and the workflow visualized in the second UI Way.
The method of claim 1,
When the input means requests platform interworking in the big data integrated processing user interface, a platform interworking pop-up UI including a list of a plurality of heterogeneous platforms connected to the big data integrated processing platform and a requested operation list is displayed over the second UI Further comprising the step of pop-up visualization, big data integrated processing user interface method.
The method of claim 1,
When the user terminal presses the execution button to complete the performance model,
Big data integrated processing user interface method, further comprising the step of allowing the simulator to link the parameters of each request operation by simulating the completed model and confirm the processing process.
4. The method of claim 3,
The optimizer learns the completed performance model and feeds back the optimal performance model to the user terminal, the big data integrated processing user interface method.
The method of claim 1,
When the input means selects the recommendation icon in the big data integrated processing user interface, the server of the big data integrated processing platform recommends an optimal performance model, but pops up the recommendation model UI including the recommendation frequency and execution time over the second UI Further comprising the step of visualizing to, big data integrated processing user interface method.

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