KR102368875B1 - Method for apparatus for visualizing dataset associations - Google Patents

Abstract

Provided are a method and apparatus for visualizing the relation between datasets. A method for visualizing the relation between datasets according to an embodiment of the present invention includes the steps of: analyzing a plurality of data sets and identifying a first data set and a second data set having a first association relationship; visualizing a first object indicating the first dataset and a second object indicating the second dataset, and connecting the first object and the second object with a connecting line as an expression of the first association relationship; determining and visualizing a height of the connecting line based on the strength of the first association relationship; analyzing a second association relationship between the first dataset and the second dataset; and determining and visualizing a color or thickness of the connecting line based on the strength of the second association relationship.

Description

Dataset association relationship visualization method and apparatus

The present invention relates to a method and apparatus for visualizing a data set association relationship. More particularly, it relates to a method and apparatus for expressing and visualizing a relationship between data sets using a graphic element.

Data visualization means visually expressing and delivering data analysis results so that users can easily understand them. Infographics, which summarize a lot of data in one gram, can be said to be a representative method of data visualization.

Recently, as interest in big data increases, the need for big data visualization to visually describe a lot of information and provide necessary information efficiently and clearly is increasing. However, according to the passage of time, only fragmentary information visualization is in progress, such as expressing the collection amount of big data as a bar graph or displaying the collection location of big data on a map.

On the other hand, big data includes a large number of datasets, and attempts to analyze the relationship between these datasets are insufficient. In addition, a model for how to visualize the correlation between datasets included in big data has not been developed.

Korean Patent Publication No. 10-2020-0102238 (published on August 31, 2020)

The technical problem to be solved by the present invention is to provide a method and apparatus for visualizing a relationship between datasets by visualizing a plurality of relationships between datasets with various graphic elements to intuitively understand the results of correlation analysis.

Another technical problem to be solved by the present invention is to provide a method and apparatus for visualizing data set associations that can more accurately analyze various associations of data sets.

Another technical problem to be solved by the present invention is to provide a method and apparatus for visualizing data set associations for visualizing a plurality of associations between data sets in one chart.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above technical problem, a data set association relationship visualization method according to an embodiment of the present invention includes analyzing a plurality of data sets to identify a first data set and a second data set having a first association relationship Visualizing a first object pointing to the first dataset and a second object pointing to the second dataset, and connecting the first object and the second object with a connecting line as an expression of the first relation step, determining and visualizing the height of the connection line based on the strength of the first association relationship; analyzing a second association relationship between the first dataset and the second dataset; The method may include determining and visualizing a color or thickness of the connecting line based on the strength of the association relationship.

In an embodiment, the first association relationship is a spatial association relationship, and the method calculates a distance between a spatial value included in the first dataset and a spatial value included in the second dataset, and the calculation The method may further include calculating the strength of a first association relationship between the first dataset and the second dataset by using the distance.

In an embodiment, the second association relationship is a time-based association relationship, and the analyzing of the second association relationship is performed between a time value included in the first dataset and a time value included in the second dataset. The method may include calculating a difference and calculating a strength of a second association relationship between the first dataset and the second dataset using the difference.

In an embodiment, the second association relationship is an item-based association relationship, and analyzing the second association relationship includes a degree of matching between the items included in the first dataset and the items included in the second dataset. and calculating the strength of a second correlation between the first dataset and the second dataset by using the analyzed matching information.

In an embodiment, the method may further include analyzing the plurality of datasets, grouping datasets having the same subject, and visualizing objects pointing to the grouped datasets in the same graphic representation. .

The connecting with the connecting line may include visualizing the first object and the second object on a ring-shaped diagram. In the ring-shaped diagram, each object may be arranged at equal intervals.

In order to solve the above technical problem, a visualization modeling apparatus according to another embodiment of the present invention includes a storage unit for a plurality of datasets, extracts first and second data sets from the storage unit, and extracts the extracted second data from the storage unit. A correlation analysis unit that analyzes the first correlation and the second correlation between the first dataset and the second dataset, and visualizes the first object pointing to the first dataset and the second object pointing to the second dataset However, as an expression of the first association relationship, the first object and the second object are connected by a connecting line, the height of the connecting line is determined based on the strength of the first association relationship, and the strength of the second association relationship is It may include a data set visualization unit for visualizing by determining the color or thickness of the connecting line based on the.

In order to solve the above technical problem, a computing device according to another embodiment of the present invention includes one or more processors, a memory for loading a program executed by the processor, and a storage in which the program is stored. , the program analyzes a plurality of datasets, selects a first dataset and a second dataset having a first correlation, and separates a first object pointing to the first dataset and the second dataset Visualizing a pointing second object, connecting the first object and the second object with a connecting line as an expression of the first relation, and determining the height of the connecting line based on the strength of the first relation Visualizing, analyzing a second correlation between the first dataset and the second dataset, and visualizing by determining a color or thickness of the connecting line based on the strength of the second correlation It may include instructions to perform.

In a computer-readable non-transitory storage medium including instructions according to another embodiment of the present invention for solving the above technical problem, the instructions cause the processor to analyze a plurality of data sets when executed by the processor to select a first dataset and a second dataset having a first correlation relationship, and visualize a first object indicating the first dataset and a second object indicating the second dataset, and 1 Connecting the first object and the second object with a connecting line as an expression of a relation, determining and visualizing the height of the connecting line based on the strength of the first relation, and the first dataset and analyzing a second correlation between the second data set and the second data set, and determining and visualizing the color or thickness of the connecting line based on the strength of the second correlation.

1 is a diagram illustrating a block diagram of a visualization modeling apparatus according to an embodiment of the present invention.
2 is a flowchart of a method for visualizing a dataset relationship according to another embodiment of the present invention.
FIG. 3 is a view for explaining in detail step S300 of FIG. 2 .
FIG. 4 is a view for explaining in detail step S400 of FIG. 2 .
FIG. 5 is a view for explaining in detail step S500 of FIG. 2 .
6 is a diagram illustrating a diagram in which relationships between datasets are visualized.
7 is a diagram illustrating detailed information on the relationship between datasets.
8 is an exemplary hardware configuration diagram that may implement a computing device in various embodiments.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the technical spirit of the present invention is not limited to the following embodiments, but may be implemented in various different forms, and only the following embodiments complete the technical spirit of the present invention, and in the technical field to which the present invention belongs It is provided to fully inform those of ordinary skill in the art of the scope of the present invention, and the technical spirit of the present invention is only defined by the scope of the claims.

In adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals as much as possible even though they are indicated on different drawings. In addition, in describing the present invention, if it is determined that a detailed description of a related known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular. The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase.

In addition, in describing the components of the present invention, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only for distinguishing the elements from other elements, and the essence, order, or order of the elements are not limited by the terms. When it is described that a component is “connected”, “coupled” or “connected” to another component, the component may be directly connected or connected to the other component, but another component is formed between each component. It should be understood that elements may also be “connected,” “coupled,” or “connected.”

As used herein, "comprises" and/or "comprising" refers to the presence of one or more other components, steps, operations and/or elements mentioned. or addition is not excluded.

In the present specification, a data set may be an aggregate of data that can be used in a computer device.

Hereinafter, some embodiments of the present invention will be described with reference to the drawings.

1 is a diagram illustrating a block diagram of a visualization modeling apparatus according to an embodiment of the present invention.

Referring to FIG. 1 , the visualization modeling apparatus 10 includes a data collection unit 11 , an original data set storage unit 12 , a processed data set storage unit 13 , a data processing unit 14 , and a correlation analysis unit ( 15) and a data set visualization unit 16, and these components may be implemented as hardware or software, or may be implemented through a combination of hardware and software. The visualization modeling apparatus 10 may be implemented as a computing device including a memory and a processor, as will be described later.

The original dataset storage unit 12 is a storage means such as storage, and may store a plurality of original datasets that have not been processed.

The processed data set storage unit 13 is a storage means such as storage, and may store the processed data set in which the original data set is processed. In other words, the processed data set stored in the processing data set storage unit 13 may be different from the original dataset stored in the original dataset storage unit 12, at least one of item, time value, space value, and organization name. can In addition, some or all of the items of the original dataset may be included in the processed dataset.

The data collection unit 11 may collect necessary data (eg, a dataset) from an external device or database and store it in the original dataset storage unit 12 . The data collection unit 11 may collect big data including a plurality of datasets. The dataset may include a title of the dataset, one or more items, metadata, time values, and spatial values. Here, the title of the dataset is information for identifying the dataset, and may be a name given to the dataset. In addition, the item is a title for a field in which data is recorded, and can be precipitation, rainfall, fine dust concentration, ultrafine dust concentration, carbon dioxide concentration, ozone concentration, traffic volume, temperature, humidity, wind direction, wind speed, flow speed, hydrologic volume, etc. . In addition, the metadata is data for describing the dataset, and may include one or more of the name of the organization providing the dataset, the keyword of the dataset, the classification system of the dataset, and the description of the dataset. In addition, the time value may be a date and time when the dataset is created or a time range taken until the dataset is created. In addition, the spatial value may be information about a place where the data set is collected. The spatial value may include latitude and longitude coordinates, an administrative address, and the like.

The data processing unit 14 extracts only the items necessary for analysis from the dataset stored in the original dataset storage unit 12 , and stores the processed data set including only the extracted items in the processing data set storage unit 13 . can be saved The data processing unit 14 changes one or more of the time value, space value, organization name, and item name of the original dataset stored in the original dataset storage unit 12 into a standard format (or standard name), and the processing dataset storage unit (13) can be stored.

The correlation analysis unit 15 may analyze the correlation between the datasets stored in the processed dataset storage unit 13 , and store the analyzed strength of the correlation in the processed dataset storage unit 13 . As will be described later, the correlation analysis unit 15 analyzes the correlation of the datasets according to space, time, item or topic, and stores the analyzed result (ie, the strength of the correlation) in the processed dataset storage unit ( 13) can be saved.

The data set visualization unit 16 may visualize various relationships of data sets using graphic elements and display them as one chart. In an embodiment, the dataset visualization unit 16 selects a first dataset and a second dataset having a first correlation from among datasets, and then selects the first dataset indicating the first dataset, as will be described later. The object may be visualized, and the first object pointing to the second dataset may be visualized. In an embodiment, the dataset visualization unit 16 connects the first object and the second object with a connection line, and based on the strength of the first association between the first dataset and the second dataset, the height of the connection line can be determined and visualized. As another embodiment, a color or thickness of the connection line may be determined and visualized based on the strength of a second correlation between the first dataset and the second dataset.

When it is determined that the first dataset and the second dataset have the same subject as a result of the analysis by the correlation analysis unit 15, the dataset visualization unit 16 determines whether the first object and the second object are the same. Color may be visualized, and if it is determined that the first dataset and the second dataset are not the same subject, the first object and the second object may be visualized with different colors.

2 is a flowchart of a method for visualizing a dataset relationship according to another embodiment of the present invention.

Each step of the method illustrated in FIG. 2 may be performed by a computing device. In other words, each step of the method may be implemented with one or more instructions executed by a processor of a computing device. The first steps included in the method may be performed by the first computing device, and the second steps of the method may be performed by the second computing device. Hereinafter, the description will be continued assuming that each step of the method is performed by the visualization modeling apparatus 10 described with reference to FIG. 1 , but the subject of each step is merely an example, and the present invention is It is not limited by the description, and for the convenience of description, the operation subject of some steps included in the method may be omitted.

Referring to FIG. 2 , the data collection unit 11 collects big data including a plurality of datasets by interworking with an external server or device, and stores the plurality of datasets in the original dataset storage unit 12 . It can be saved (S100). In an embodiment, the data collection unit 11 may receive an address of an external server capable of collecting big data in advance, and may collect big data by accessing a server corresponding to the address. Also, after allocating an index of the dataset, the data collection unit 11 may store the dataset including the index in the original dataset storage unit 12 .

Subsequently, the data processing unit 14 may extract data to be analyzed from among the datasets stored in the original dataset storage unit 12 ( S200 ). The data processing unit 14 may extract the title, time value, spatial value, metadata, and items of the dataset from the dataset as analysis target data. That is, from among the data included in the dataset, only data used for analysis may be selected and extracted.

Next, the data processing unit 14 processes at least one of a time value, a space value, an organization name, and an item name among the extracted data analysis target data into a preset standard format or standard name, and processes the time value, space value, and organization name Alternatively, the processing data set including the item name processed and the analysis target data may be stored in the processing data set storage unit 13 ( S300 ). The operation S300 of processing the analysis target data and storing the processing data set will be described in more detail with reference to FIG. 3 .

Then, the correlation analysis unit 15 may analyze the correlation between the plurality of datasets stored in the processing data set storage unit 13 , and store the strength of the analyzed correlation in the processing dataset storage unit 13 . There is (S400). The correlation analysis unit 15 may analyze the correlation of the plurality of data sets based on one or more of a spatial value, a time value, an item, and a topic. The step S400 of analyzing the correlation between the datasets will be described in more detail with reference to FIG. 4 .

Next, the dataset visualization unit 16 can check the correlation between the datasets in the processed data set storage unit 13, and visualize the correlation between the datasets in one chart using various graphic elements. There is (S500). As will be described later, after selecting a first dataset and a second dataset having a first correlation from among datasets, a first object pointing to the first dataset is visualized, and a first object pointing to the second dataset is selected. Objects can be visualized. In an embodiment, the dataset visualization unit 16 connects the first object and the second object with a connection line, and based on the strength of the first association between the first dataset and the second dataset, the height of the connection line can be determined and visualized. As another embodiment, the dataset visualization unit 16 may determine and visualize the color or thickness of the connection line based on the strength of the second correlation between the first dataset and the second dataset. The step S500 of analyzing the correlation between data sets will be described in more detail with reference to FIG. 5 .

According to the present embodiment, by visualizing and displaying various correlations between datasets on one chart, the user can intuitively understand the correlations between datasets. Accordingly, users can easily understand the trend or pattern of the dataset, and also clearly understand the message contained in the dataset.

Hereinafter, with reference to FIG. 3 , step S300 of FIG. 2 will be described in detail.

Referring to FIG. 3 , the data processing unit 14 may identify one or more item names from the extracted analysis target data ( S305 ). Next, the data processing unit 14 may determine whether the identified item name is a preset standard item name (S310). The standard item name may be set in advance, and one or more names similar to the standard item name may be mapped in advance and stored. For example, "air dust concentration", "air dust concentration", "floating dust concentration", etc. may be mapped as words similar to the standard item name "fine dust concentration".

In response to the identified item name being not a standard item name, the data processing unit 14 may change the identified item name to a standard item name (S315). For example, when the identified item name is "airborne dust concentration" corresponding to a non-standard item name, "airborne dust concentration" may be changed to "fine dust concentration", which is a standard item name.

Next, the data processing unit 14 may identify the organization name from the metadata of the analysis target data ( S320 ). The data processing unit 14 may determine whether the identified organization name is a preset standard organization name (S325). The standard organization name may be set in advance, and one or more organization names similar to the standard organization name may be mapped in advance and stored. For example, a standard institution name of "Pusan National University" and "Pusan National University" may be mapped and recorded as a similar institution name.

In response to the identified organization name being not the standard organization name, the data processing unit 14 may change the organization name to a standard organization name corresponding to the identified organization name (S330). For example, if the identified institution name is "Pusan National University", which is a non-standard institution name, "Pusan National University" may be changed to "Pusan National University", which is a standard item name.

Next, the data processing unit 14 may identify a spatial value from the analysis target data (S335). The data processing unit 14 may determine whether the format of the identified spatial value is a standard format of a preset spatial value (S340). That is, the data processing unit 14 can determine whether or not the identified spatial value description format corresponds to a preset standard format for spatial values. For example, the standard format of the spatial value may be an administrative address format including a street name.

In response to the format of the identified spatial value being not the standard format of the spatial value, the data processing unit 14 may change the identified spatial value to the standard format (S345). For example, when the format of the identified spatial value is latitude and longitude coordinates, the data processing unit 14 may change the latitude and longitude coordinates into an administrative address format including a road name.

Next, the data processing unit 14 may identify a time value from the analysis target data (S350). The data processing unit 14 may determine whether the format of the identified time value corresponds to a standard format of a preset time value (S355). That is, the data processing unit 14 may determine whether the written format of the identified time value corresponds to a standard format of a preset time value. For example, the standard format of the time value has the order of year, month, and date, and may include a four-digit year, two-digit month, and two-digit date.

In response to that the format of the identified time value is not a standard format, the data processing unit 14 may change the identified time value into a standard format of the time value (S360). For example, a time value in the order of month, day, year, and year may be changed to a time value in the order of year, month, day.

Next, the data processing unit 14 may store the processing data set including the analysis target data having a standard format, a standard organization name, and a standard item in the processing data set storage unit 13 ( S365 ).

According to this embodiment, data to be analyzed are selected from among the data included in the dataset, and the data to be analyzed are changed to an easy-to-analyze form among the selected data, so that the relational analysis of the datasets to be described later can be performed more quickly and accurately. can

Hereinafter, with reference to FIG. 4 , step S400 of FIG. 2 will be described in detail.

Referring to FIG. 4 , the correlation analysis unit 15 extracts a plurality of data sets stored in the processed data set storage unit 13 , and compares and analyzes spatial values included in each of the data sets, The spatial correlation may be analyzed and the strength of the spatial correlation may be stored in the processing data set storage unit 13 (S405). That is, the correlation analysis unit 15 uses the first spatial value included in the first dataset and the second spatial value included in the second dataset, and the spatial correlation between the first dataset and the second dataset. can be calculated and stored. In an embodiment, the correlation analysis unit 15 may measure the distance between the first spatial value and the second spatial value, and calculate the strength of the spatial correlation according to a range to which the measured distance belongs. For example, when the distance between spatial values is included in the first distance range, the strength of the spatial correlation may be “upper”, and when the distance between spatial values is included in the second distance range, the strength of the spatial correlation is “medium”. ", and when the distance between spatial values is included in the third distance range, the strength of the spatial association may be "weak". Here, the closer to "upper", the closer the distance between spatial values is, and it may mean that the spatial correlation is strong.

Next, the correlation analysis unit 15 analyzes the temporal correlation of each dataset by using the time value included in each of the datasets, calculates the strength of the temporal correlation, and operates the processing data set storage unit 13 ) can be stored in (S410). That is, the correlation analysis unit 15 compares and analyzes the first time value included in the first dataset and the second time value included in the second dataset, and correlates the time between the first dataset and the second dataset. The strength of the relationship can be calculated and stored. In an embodiment, the correlation analysis unit 15 may calculate a time difference between the first time value and the second time value, and calculate the strength of the temporal correlation relationship according to a range to which the calculated time difference belongs. For example, when the difference between time values is included in the first time range, the strength of the temporal correlation may be “high”, and when the difference between the time values is included in the second time range, the strength of the temporal correlation is “medium” " may be, and when the difference between time values is included in the third time range, the strength of the temporal correlation may be "about". Here, the closer to “phase”, the shorter the difference between the time values and the stronger the temporal correlation.

Next, the correlation analysis unit 15 analyzes the degree of matching of the items included in each dataset, calculates the strength of the item correlation of each dataset, and calculates the strength of the calculated item correlation in the processed dataset. It can be stored in the storage unit 13 (S415). In one embodiment, the correlation analysis unit 15 compares and analyzes the items included in the first dataset and the items included in the second dataset, calculates the ratio or number of matching items, and calculates the calculated match Based on the number or ratio of items, the strength of the item correlation between the first dataset and the second dataset may be calculated and stored. For example, when the number or ratio of matching items is included in the first range, the strength of the item affinity may be “up”, and when the number or ratio of matching items are included in the second range, the strength of the item affinity is It may be “medium”, and when the number or ratio of matching items is included in the third range, the strength of the item association may be “weak”. Here, the closer to "top", the greater the number of matching items or the higher the ratio of matching items, and it may mean that the item correlation is strong.

Next, the correlation analysis unit 15 may identify the subject of each dataset, and store the identified subject of the dataset in the processed data set storage unit 13 ( S420 ). In an embodiment, the correlation analysis unit 15 identifies a first keyword and first metadata included in the first dataset, and includes keywords and metadata for each subject in which the first keyword and the first metadata are pre-stored. The subject of the dataset may be identified by analyzing which subject among them is matched. Keywords and metadata for each subject may be stored in advance in the visualization modeling apparatus 10 . The correlation analysis unit 15 calculates the degree of similarity between the identified first keyword and the keyword for each subject, calculates the similarity between the identified first meta data and the meta data for each subject, and then compares the meta data with the highest similarity with the meta data with the highest degree of similarity. A subject corresponding to the keyword may be identified as the subject of the dataset.

In an embodiment, the correlation analysis unit 15 identifies a second keyword and second metadata corresponding to the first topic, and determines a degree of similarity between the first keyword and the second keyword, and the first metadata and second metadata. 2 It is possible to calculate the similarity of the metadata and calculate the strength of the relational relation between the corresponding subjects by weighted summing the keyword similarity and the similarity of the metadata. The subject having the highest score among the subject association strengths calculated in this way may be identified as the subject of the dataset. The correlation analysis unit 15 may calculate the similarity between keywords and meta data using a morpheme matching rate, a word matching rate, and the like.

Through the process shown in FIG. 4 , spatial relevance, temporal relevance, item relevance, and subject relevance between a plurality of datasets may be analyzed.

Hereinafter, the step S500 of analyzing the correlation between the datasets will be described in more detail with reference to FIGS. 5 and 6 .

5 and 6 , the dataset visualization unit 16 may visualize and display an object pointing to the dataset stored in the processed dataset storage unit 13 ( S505 ). In an embodiment, the dataset visualization unit 16 displays a plurality of data sets identification information (eg, title) and an object corresponding to the identification information and pointing to the dataset (represented by grid lines in FIG. 6 ). Three can be visualized. As illustrated in FIG. 6 , an object indicating each dataset may be visualized as a grid line in a ring-shaped diagram. In FIG. 6 , identification information of the dataset is displayed on the outer region of the ring in correspondence with the grid lines. In addition, each object may be arranged at equal intervals in the ring-shaped diagram.

Next, the dataset visualization unit 16 selects datasets in which the spatial correlation strength is greater than or equal to a preset strength among the datasets, and visualizes the objects of the selected dataset as a connection line, but spatially correlates the height of the connection line It can be determined and visualized according to the strength of the relationship (S510). For example, the dataset visualization unit 16 visualizes the first object pointing to the first dataset and the second object pointing to the second dataset, and expressing the spatial correlation between the first dataset and the second dataset As a connection line, the first object and the second object are connected, and the height of the connection line can be determined and visualized based on the strength of the spatial relation. In an embodiment, the connecting line may be a parabola, and the height of the parabola may increase in proportion to the strength of the spatial correlation.

As illustrated in FIG. 6 , objects of a dataset having spatial correlation may be connected to each other by a connecting line. In addition, the strength of the spatial relation may be intuitively expressed according to the height of the connecting line. According to FIG. 6 , the object at point 61 and the object at point 62 are connected by a connecting line having the highest height, and accordingly, the object at point 61 and the object at point 62 have the strongest spatial association. can have a relationship.

Next, the dataset visualization unit 16 may identify the strength of the temporal correlation between the datasets connected by the connection line, determine the color of the connection line according to the strength of the temporal correlation, and visualize it ( S515 ). For example, when the strength of the temporal correlation relationship between the first dataset and the second dataset is “upper”, a color of a connecting line connecting the first object and the second object may be determined as the first color, and the time When the intensity of the association relationship is “medium”, the color of the connecting line may be determined as the second color, and when the intensity of the temporal association is “low”, the color of the connecting line may be determined as the third color.

Next, the dataset visualization unit 16 may identify the strength of the item correlation between datasets connected by the connection line, determine the thickness of the connection line according to the strength of the item correlation, and visualize it ( S520 ). For example, when the strength of the item correlation between the first dataset and the second dataset is “upper”, the thickness of the connecting line connecting the first object and the second object may be doubled, and the item association relationship When the strength of is "medium", the thickness of the connecting line may be determined as it is, and when the intensity of the item correlation is "low", the thickness of the connecting line may be determined in half. As illustrated in FIG. 6 , as the strength of the item connection relationship between the object corresponding to point 61 and the object corresponding to point 62 is strong, the connection line may be visualized to be relatively thick.

Next, the dataset visualization unit 16 may identify the subject of each dataset, identify a color corresponding to the subject, and then visualize the object with the identified color ( S525 ). According to FIG. 6 , the datasets corresponding to the first topic are visualized as “red”, the datasets corresponding to the second topic are visualized as “blue”, and the datasets corresponding to the third topic are visualized as “yellow”. It can be visualized and displayed. Also, the dataset visualization unit 16 may visualize objects of a dataset corresponding to the same subject as being adjacent to each other. In the ring-shaped diagram in FIG. 6 , objects of the dataset corresponding to the same subject are adjacent to each other.

According to the present embodiment, since various correlations are displayed as various visual elements on one chart, the user can intuitively understand the correlations of the dataset.

In another embodiment of the present invention, when a specific connection line is selected by the user, the data visualization unit 16 may display detailed information on the relationship between datasets connected by the connection line.

FIG. 7 is a diagram illustrating detailed information of a relationship displayed when the connection line 63 of FIG. 6 is selected.

In FIG. 6 , when the connecting line 63 is selected by the user, the data visualization unit 16 identifies a plurality of objects 61 and 62 to which the connecting line 63 is connected, and a dataset corresponding to the objects. can be identified. According to FIG. 6 , an object with reference number 61 may point to a dataset having the title of "Statistics service by river", and an object with reference number 62 may indicate "information on the occurrence of morning yellow dust by Korea Environment Corporation". The data visualization unit 16 is an association relationship including data (eg, a spatial value, a temporal value, a match item, etc.) that is a basis for calculating the strength of the association for the two identified datasets and the strength of the association. 7 may be extracted and displayed in the form of FIG. 7 .

As illustrated in FIG. 7 , when the connecting line 63 is selected, the spatial correlation relationship between the data set of “statistical service by river” and the data set of “Korea Environment Corporation morning yellow dust occurrence information” connected through the connecting line 63 strength, the strength of the temporal association. It is possible to provide the user with detailed information on the relationship between the datasets selected by the user by displaying the item correlation strength, the spatial value of the corresponding dataset, the temporal value, the matching items between datasets, and the subject of each dataset. there is.

In an embodiment, the data visualization unit 16 may display the detailed correlation information in the form of an upper layer of the diagram, or may display the detailed correlation information after removing the diagram from the screen.

As described above, by selecting the connection line in the diagram, the user can check detailed information of the correlation between data sets connected through the connection line.

Meanwhile, in the above-described embodiments, spatial correlation is visualized as a connecting line, temporal relation is visualized as a color of a connecting line, item correlation is visualized as a thickness of a connecting line, and the subject of a dataset is visualized as an object color was explained as However, it should be noted that the present invention is not limited thereto, and various relational relationships may be visualized using a graphic representation other than the above-described visualization representation. For example, the temporal relationship may be visualized as a connection between objects using a connection line, a color of an object, a thickness of a connection line, or another graphic representation. As another example, the spatial association may be visualized as a color of an object, a thickness of a connecting line, a color of a connecting line, or another graphic representation.

8 is an exemplary hardware configuration diagram that may implement a computing device in various embodiments.

The computing device 1000 according to the present embodiment includes one or more processors 1100 , a system bus 1600 , a communication interface 1200 , and a memory for loading a computer program 1500 executed by the processor 1100 . 1400 and a storage 1300 for storing the computer program 1500 may be included. In Fig. 8, only the components related to the embodiment are shown. Accordingly, those skilled in the art to which the embodiments of the present specification pertain can know that other general-purpose components other than those shown in FIG. 8 may be further included.

The processor 1100 controls the overall operation of each component of the computing device 1000 . The processor 1100 includes at least one of a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art. may be included. In addition, the processor 1100 may perform an operation on at least one application or program for executing the method/operation according to various embodiments. The computing device 1000 may include two or more processors.

The memory 1400 stores various data, commands, and/or information. The memory 1400 may load one or more programs 1500 from the storage 1300 to execute methods/operations according to various embodiments of the present specification. An example of the memory 1400 may be a RAM, but is not limited thereto.

The communication interface 1200 may communicate with an external communication device such as a mobile communication terminal, a personal computer, or a server using a network such as a mobile communication network or a wired Internet network. The communication interface 1200 may receive input information from a communication device.

The system bus 1600 provides a communication function between components of the computing device 1000 . The system bus 1600 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The storage 1300 may non-temporarily store one or more computer programs 1500 . The storage 1300 may include a non-volatile memory such as a flash memory, a hard disk, a removable disk, or any type of computer-readable recording medium well known in the art to which embodiments of the present specification pertain. . In addition, the storage 1300 may store the user's past transaction history, and may also store the past transaction history of the product.

The computer program 1500 may include one or more instructions in which methods/operations according to various embodiments of the present specification are implemented. When the computer program 1500 is loaded into the memory 1400 , the processor 1100 may execute the one or more instructions to perform methods/operations according to various embodiments of the present specification. The computer program 1500 may include instructions for the method described with reference to FIGS. 2 to 7 .

In an embodiment, the computer program 1500 analyzes a plurality of data sets and selects a first data set and a second data set having a first correlation, and a first object pointing to the first data set. and a second object pointing to the second dataset, and an operation of connecting the first object and the second object with a connecting line as an expression of the first relation, and based on the strength of the first relation An operation of determining and visualizing the height of the connection line, an operation of analyzing a second correlation relationship between the first dataset and the second dataset, and a color or thickness of the connection line based on the strength of the second correlation relationship may include instructions for performing an operation of determining and visualizing .

So far, various embodiments of the present invention and effects according to the embodiments have been described with reference to FIGS. 1 to 8 . Effects according to the technical spirit of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

The technical idea of the present invention described with reference to FIGS. 1 to 8 may be implemented as computer-readable codes on a computer-readable medium. The computer-readable recording medium may be, for example, a removable recording medium (CD, DVD, Blu-ray disk, USB storage device, removable hard disk) or a fixed recording medium (ROM, RAM, computer-equipped hard disk). can The computer program recorded on the computer-readable recording medium may be transmitted to another computing device through a network such as the Internet and installed in the other computing device, thereby being used in the other computing device.

In the above, even though all the components constituting the embodiment of the present invention are described as being combined or operating in combination, the technical spirit of the present invention is not necessarily limited to this embodiment. That is, within the scope of the object of the present invention, all the components may operate by selectively combining one or more.

Although acts are shown in a particular order in the drawings, it should not be understood that the acts must be performed in the specific order or sequential order shown, or that all illustrated acts must be performed to obtain a desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of the various components in the embodiments described above should not be construed as necessarily requiring such separation, and the described program components and systems may generally be integrated together into a single software product or packaged into multiple software products. It should be understood that there is

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those of ordinary skill in the art to which the present invention pertains can practice the present invention in other specific forms without changing the technical spirit or essential features. can understand that there is Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive. The protection scope of the present invention should be interpreted by the following claims, and all technical ideas within the equivalent range should be interpreted as being included in the scope of the technical ideas defined by the present invention.

Claims (19)

A method performed by a computer device, comprising:
analyzing a plurality of data sets to identify a first data set and a second data set having a first correlation relationship;
Visualizing a first object pointing to the first dataset and a second object pointing to the second dataset, and connecting the first object and the second object with a connecting line as an expression of the first association relationship;
determining and visualizing the height of the connecting line based on the strength of the first association relationship;
analyzing a second association relationship between the first dataset and the second dataset;
Visualizing by determining a color or thickness of the connecting line based on the strength of the second association relationship;
receiving a selection of the visualized connection line from a user; and
Displaying detailed association information including data of the first dataset and data of the second dataset as a basis for calculating the strength of the first association relationship and the strength of the second association relationship; ,
The connecting line is a parabola,
The height of the parabola increases in proportion to the strength of the first association relationship,
How to visualize dataset associations. delete According to claim 1,
The first association relationship is a space-based association relationship,
A distance between a spatial value included in the first dataset and a spatial value included in the second dataset is calculated, and a first correlation relationship between the first dataset and the second dataset is calculated using the calculated distance. Further comprising the step of calculating the strength of
How to visualize dataset associations. According to claim 1,
The thickness of the connecting line is that the thickness is proportional to the strength of the second association relationship,
How to visualize dataset associations. According to claim 1,
The second association is a time-based association,
The step of analyzing the second association relationship is
Calculate the difference between the time value included in the first dataset and the time value included in the second dataset, and use the difference to determine the strength of the second correlation between the first dataset and the second dataset comprising the step of calculating
How to visualize dataset associations. According to claim 1,
The second association is an item-based association,
The step of analyzing the second association relationship is
The degree of matching between the items included in the first dataset and the items included in the second dataset is analyzed, and a second correlation relationship between the first dataset and the second dataset is analyzed using the analyzed matching information. Comprising the step of calculating the strength of
How to visualize dataset associations. According to claim 1,
grouping the datasets having the same subject by analyzing the plurality of datasets; and
Further comprising the step of visualizing the objects pointing to the grouped datasets in the same graphical representation,
How to visualize dataset associations. 8. The method of claim 7,
The step of visualizing with the same graphic representation,
Extracting keywords and metadata from a dataset, and identifying a subject of the dataset using the keywords and metadata.
How to visualize dataset associations. 8. The method of claim 7,
The step of visualizing with the same graphic representation,
Visualizing a plurality of objects pointing to the grouped datasets to identify a subject of the grouped datasets and to have a color corresponding to the subject,
How to visualize dataset associations. According to claim 1,
The step of connecting with the connecting line is,
Comprising the step of visualizing the first object and the second object on a diagram in the form of a ring,
In the ring-shaped diagram, each object is arranged at equal intervals,
How to visualize dataset associations. According to claim 1,
collecting a dataset;
determining whether an item included in the collected data set is a standard item name; and
In response to determining that the item is not a standard item name, further comprising the step of changing an item included in the collected data set to the standard item,
How to visualize dataset associations. According to claim 1,
collecting a dataset;
determining whether a time value included in the collected data set is in a standard format; and
In response to determining that the time value included in the collected dataset is not in a standard format, changing the time value included in the collected dataset to the standard format,
How to visualize dataset associations. According to claim 1,
collecting a dataset;
determining whether a spatial value included in the collected data set is in a standard format; and
In response to determining that the spatial value included in the collected dataset is not in a standard format, changing the spatial value included in the collected dataset to the standard format,
How to visualize dataset associations. delete delete a storage unit for a plurality of data sets;
an association analysis unit extracting a first data set and a second data set from the storage unit, and analyzing a first association relation and a second association relation between the extracted first data set and the second data set; and
Visualize a first object pointing to the first dataset and a second object pointing to the second dataset, wherein the first object and the second object are parabolically connected as an expression of the first relation, and the second object is 1 The height of the parabola is determined so that the height of the parabola rises in proportion to the strength of the relation, and a data set visualization unit for visualizing the color or thickness of the parabola is determined based on the strength of the second relation. ,
When the visualized parabola is selected by the user, the association including the data of the first dataset and the data of the second dataset as a basis for calculating the strength of the first association and the strength of the second association displaying relationship details;
Visualization modeling device. 17. The method of claim 16,
The correlation analysis unit determines whether the first dataset and the second dataset are the same subject,
The dataset visualization unit visualizes the first object and the second object with the same color if the determination result is the same subject,
Visualization modeling device. one or more processors;
a memory for loading a program executed by the processor; and
Including a storage in which the program is stored,
The program is
analyzing a plurality of data sets and selecting a first data set and a second data set having a first correlation relationship;
Visualizing a first object pointing to the first dataset and a second object pointing to the second dataset, and parabolically connecting the first object and the second object as an expression of the first relation;
determining and visualizing the height of the parabola so that the height of the parabola increases in proportion to the strength of the first association relationship;
analyzing a second association relationship between the first dataset and the second dataset; and
determining and visualizing the color or thickness of the parabola based on the strength of the second association relationship;
receiving a selection from the user for the visualized parabola; and
performing an operation of displaying detailed association information including data of the first data set and data of the second data set as a basis for calculating the strength of the first association relationship and the strength of the second association relationship; including instructions for
computing device. A computer-readable non-transitory storage medium comprising instructions, comprising:
The instructions, when executed by a processor, cause the processor to:
analyzing a plurality of data sets and selecting a first data set and a second data set having a first correlation relationship;
Visualizing a first object pointing to the first dataset and a second object pointing to the second dataset, and parabolically connecting the first object and the second object as an expression of the first association relationship;
Determining and visualizing the height of the parabola so that the height of the parabola increases in proportion to the strength of the first association relationship;
analyzing a second association relationship between the first dataset and the second dataset;
Visualizing by determining a color or thickness of the parabola based on the strength of the second association relationship;
receiving a selection of the visualized parabola from a user; and
Displaying detailed association information including data of the first dataset and data of the second dataset as a basis for calculating the strength of the first association relationship and the strength of the second association relationship; to perform actions,
A computer-readable, non-transitory storage medium.

Images