KR20180014543A - Apparatus and Method for Analizing Multi-dimensional Data in Simulation based on Performer - Google Patents

Abstract

The present invention relates to a method for analyzing multidimensional data in a simulation based on a performer, which includes the steps of: generating the multidimensional data by extracting an attribute value from each performer according to the performance of the simulation based on the performer; selecting data for each attribute from a user; dividing items which are numbers of the selected data into a preset section to be converted into a nominal value or grouping the data which is the nominal value; retrieving frequent patterns among the converted data by performing frequent pattern mining; selecting an interest core pattern from the user among a plurality of frequency patterns; and visualizing the data by using the selected core patterns. Accordingly, the users can effectively and easily analyze the multidimensional data generated in the simulation based on the performer.

Description

TECHNICAL FIELD The present invention relates to an apparatus and method for analyzing multi-dimensional data in an actor-based simulation,

The present invention relates to an actor-based simulation result data providing technology, and more particularly, to an apparatus and a method for visualizing multidimensional data, which are all attribute values of actors generated as a result of actor-based simulation.

In the actor-based simulation, attribute values of each actor are summed up after simulation and the desired simulation result is output. In this case, because it is difficult to analyze the results by only the predefined aggregation type, it is necessary to analyze the multidimensional data, which is the total attribute value of the actors. To effectively analyze these multidimensional data, various kinds of visualization methods have been proposed.

One of the data visualization methods displays a virtual three-dimensional chart space interface, recognizes a sketch pattern according to a drag and drop operation on a three-dimensional chart space interface through input means by a user, A chart corresponding to the sketch pattern for the data can be generated and displayed through the three-dimensional chart space interface. However, as the number of dimensions increases, it is difficult to analyze them through visualization.

The present invention provides a multidimensional data analysis apparatus and method in an actor-based simulation in which a user can effectively analyze attribute values of actors, which are multidimensional data, generated after performing actor-based simulation.

The present invention relates to a multidimensional data analysis method in an actor-based simulation, comprising: generating multidimensional data by extracting an attribute value from each actor in accordance with actor-based simulation; selecting data for each attribute from a user; Dividing a predetermined section into numerical values among the selected data and converting the divided data into a nominal value or grouping data having a nominal value; searching for frequent patterns in the converted data by performing frequent pattern mining And receiving a key pattern of interest from a user in a plurality of frequent patterns, and visualizing data using the selected key patterns.

According to the configuration of the present invention, users can easily analyze multidimensional data generated in actor-based simulation.

1 is a diagram showing an example of a general simulation data output interface.
2 is a block diagram of a multi-dimensional data analysis apparatus in an actor-based simulation according to an embodiment of the present invention.
3 is a flowchart illustrating a method for analyzing multi-dimensional data in an actor-based simulation according to an exemplary embodiment of the present invention.
FIGS. 4 to 5B are views for explaining an example of analyzing the population dynamics simulation data according to the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and the manner of achieving them, will be apparent from and elucidated with reference to the embodiments described hereinafter in conjunction with the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In the following description of the present invention, detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. , Which may vary depending on the intention or custom of the user, the operator, and the like. Therefore, the definition should be based on the contents throughout this specification.

Each block of the accompanying block diagrams and combinations of steps of the flowcharts may be performed by computer program instructions (execution engines), which may be stored in a general-purpose computer, special purpose computer, or other processor of a programmable data processing apparatus The instructions that are executed through the processor of the computer or other programmable data processing equipment will generate means for performing the functions described in each block or flowchart of the block diagram.

These computer program instructions may also be stored in a computer usable or computer readable memory capable of directing a computer or other programmable data processing apparatus to implement the functionality in a particular manner so that the computer usable or computer readable memory It is also possible for the instructions stored in the block diagram to produce an article of manufacture containing instruction means for performing the functions described in each block or flowchart of the flowchart.

And computer program instructions may be loaded onto a computer or other programmable data processing equipment so that a series of operating steps may be performed on a computer or other programmable data processing equipment to create a computer- It is also possible that the instructions that perform the data processing equipment provide the steps for executing the functions described in each block of the block diagram and at each step of the flowchart.

Also, each block or step may represent a portion of a module, segment, or code that includes one or more executable instructions for executing the specified logical functions, and in some alternative embodiments, It should be noted that functions may occur out of order. For example, two successive blocks or steps may actually be performed substantially concurrently, and it is also possible that the blocks or steps are performed in the reverse order of the function as needed.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the following embodiments of the present invention may be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. The embodiments of the present invention are provided to enable those skilled in the art to more fully understand the present invention.

1 is a diagram showing an example of a general simulation data output interface.

Referring to FIG. 1, a three-dimensional chart space interface includes a two-dimensional chart area located on a wall surface 110 and a three-dimensional chart area located on a floor 120. However, as the number of dimensions increases, there are many difficulties in visualizing and analyzing in this way.

2 is a block diagram of a multi-dimensional data analysis apparatus in an actor-based simulation according to an embodiment of the present invention.

2, a multidimensional data analysis apparatus (hereinafter referred to as "apparatus") in the actor-based simulation includes a simulation unit 210, a multidimensional data generation unit 220, a user data selection unit 230, A frequent pattern mining unit 250, a key pattern selection unit 260, and a data visualization unit 270.

The simulation unit 210 performs an actor based simulation. According to the simulation, attribute values are generated for each actor, and the aggregated values can be given by the users.

The multidimensional data generator 220 extracts attribute values from each actor to generate multidimensional data for further analysis. This is because, in many cases, the users re-analyze attribute values generated by the actor by the simulation unit 210 for precise analysis. At this time, the multidimensional data is expressed in a matrix form. According to one embodiment, the attribute value list of each actor is matched with one row of the matrix.

The user data selection unit 230 may receive data for each attribute from the users. This is because the size of the multidimensional data is large. In this case, it may be selected as a range type or a single value type.

In order to perform frequent pattern mining at a later time, the data conversion unit 240 converts the numerical items into appropriate nominal values and converts them into a nominal value or a nominal value If there is one, they can be grouped into one. At this time, the data is automatically converted to a nominal value using a data mining technique. If the user does not want the automatically converted result, the data conversion unit 240 may allow the user to directly modify the result (the quantization result).

The frequent pattern mining unit 250 performs frequent pattern mining to find frequent patterns.

The key pattern selection unit 260 selects a key pattern of interest from a user during a plurality of frequent patterns.

The data visualization unit 270 visualizes the data with the selected values. According to one embodiment, a frequency distribution table may be displayed for each attribute (item) for the selected area up to now. When an additional analysis request is input from the user, the data visualization unit 270 requests the user data selection unit 230 to perform an operation on the selected value up to the present.

FIG. 3 is a flow chart for explaining a multidimensional data analysis method in the actor-based simulation according to an embodiment of the present invention. FIGS. 4 to 5B illustrate an example of analyzing the dynamics simulation data according to the present invention FIG.

Referring to FIG. 3, the apparatus performs an agent-based simulation (S210). According to the simulation, attribute values are generated for each actor, and the aggregated values can be given by the users. In many cases, however, users re-analyze attribute values generated by actors for precise analysis.

Accordingly, the apparatus extracts attribute values from each actor to generate multidimensional data for further analysis (S220). At this time, the multidimensional data is expressed in a matrix form, and the attribute value list of each actor is matched to one row (Row) of the matrix. For example, the results of the agent - based population dynamics simulation show that other attribute values are related to the actor ID, attempt, gender, manners, relationship with the householder, education level, educational status, economic activity status, And if the total actors are five persons, multidimensional data as shown in FIG. 4 can be generated.

However, since the size of the multidimensional data is large, the apparatus can select data of interest for each attribute column from the users (S230). In this case, a range type may be selected, or a single value type may be selected. For example, the attribute of 'Attempt' may be selected as 'Chungnam' or the status of marriage may be selected as 'Unmarried' or 'Divorce'.

In order to perform frequent pattern mining at a later time, the device converts the numerical items into a nominal value by appropriately dividing a section, or when the nominal value is set to a nominal value, (S340). At this time, data mining (data mining) technique is automatically converted to nominal value (quantization) is done. According to one embodiment, for example, the attribute values of a man may be grouped in units of ten years, or the profession may be grouped into a profession and the other two.

In operation S350, the device determines whether manual conversion is required because the user does not want the converted result automatically. If the manual conversion is required, the device receives the modified result by the user in operation S360. For example, the results of automatic grouping of "ten-year-olds" can be manually grouped into "five-year-olds".

Thereafter, the apparatus performs frequent pattern mining to find frequent patterns (S370). That is, each row is referred to as a transaction, and frequent pattern mining is performed. For example, age: 30-34, male, age: 20-24, male, graduate school, profession can be an example of the results.

Then, the device selects a key pattern of interest of the user during a plurality of frequent patterns (S370). For example, Age: 30-34 may be selected.

The device then visualizes the data with the selected values (S390). According to one embodiment, a frequency distribution table may be displayed for each attribute (item) for the selected area up to now. In other words, it is possible to visualize the frequency distribution charts such as occupation, education level, educational status, relationship with the householder, economic activity status, occupational status, etc. for actors who are in Chungnam, single or divorced, women and 30-34 year olds. 5A and 5B show an example of the frequency distribution table. Then, the apparatus confirms whether a more detailed analysis request for multidimensional data is input from the user (S400), and proceeds to S330 if additional analysis is required.

Claims (1)

Generating multidimensional data by extracting attribute values from each actor in accordance with actor-based simulation;
Selecting data for each attribute from a user,
Dividing a number of items into a nominal value by dividing a predetermined section of the selected data or grouping data having a nominal value,
Performing frequent pattern mining to search for frequent patterns among the converted data;
Receiving a key pattern of interest from a user among a plurality of frequent patterns;
And visualizing the data using the selected core patterns. ≪ RTI ID = 0.0 > [10] < / RTI >

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