Data transfer device, method, and program

Abstract

A data transfer device according to one embodiment includes: a data management unit that acquires first data from a first environment and acquires second data from a second environment; a pairing unit that performs pairing of related pieces of data among the respective pieces of data constituting the first data and pairing of related pieces of data among the respective pieces of data constituting the second data; and a data classification unit for generating a plurality of first data clusters from clustering the results of the pairing of the respective pieces of data constituting the first data, and generating a plurality of second data clusters from clustering the results of the pairing of the respective pieces of data constituting the second data; and a transition determination unit for generating a plurality of third data clusters by combining and clustering the plurality of first data clusters and the plurality of second data clusters, and extracting the third data cluster including at least one of the first data clusters.

Description

Data transfer device, method and program

[0001] SUMMARY OF THE INVENTION An embodiment of the present invention relates to a data transfer device, method, and program.

[0002] One method for responding to network failures involves classifying a huge amount of alarm information and log information collected from the network using a clustering technique, comparing the information collected from the network with rules created from past experience, and using artificial intelligence (AI) to identify and respond to the failure.In the above-mentioned network failure response work, in order to achieve accurate detection of network failures using AI and rapid failure recovery, it is necessary to collect a large amount of failure patterns and response methods in advance and have the AI ​​learn from the large amount of collected data.

[0003] A method of using past data as learning data, i.e., transfer, to allow AI to learn from large amounts of data is known (see, for example, Non-Patent Document 1). This method combines and classifies data acquired from a network of a target environment (sometimes called a production environment) and networks of existing similar environments by processing them using hierarchical clustering.

[0004] Di LI, Haruhisa NOZUE, Norio YAMAMOTO, Kenichi TAYAMA, "Study on data clustering efficiency for the shortage of training data for network failure response AI," The Institution of Electronics, Information and Communication Engineers Society Conference, September 2021.

[0005] As described above, in order to use data from an existing environment as training data for the production environment, a mechanism is required to determine and recognize data as the same or similar if the meaning is the same or similar even if the data is expressed differently.

[0006] If the data being compared contains exactly the same words, it is easy to determine that the data are similar. However, when the data being compared is, for example, data in different languages, proper nouns, technical terms, synonyms, or compound words formed by combining multiple words (roots), which are expressed differently as character strings but have the same or similar meanings, it is difficult to properly determine whether the data is similar, and as a result, the data may be determined to be unusable for the training data.

[0007] For example, in cases such as (1) to (3) below, it is highly likely that similarity cannot be judged appropriately: (1) Words that have the same or similar meaning but are written in different languages ​​or spellings, such as Japanese, Katakana, and English (e.g., error, abnormality) (2) Words that have the same or similar meaning but are expressed in different ways, such as words that indicate the status of protocols at the same layer (e.g., adjacent, neighbor, and neighbor (or peer)) (3) Words that can be expressed as a single word or as multiple words separated from a single word, such as compound words (e.g., IP, IP address)

[0008] This invention was made in light of the above circumstances, and its purpose is to provide a data transfer device, method, and program that can appropriately extract necessary data from an existing environment.

[0009] A data transfer device according to one aspect of the present invention includes a data management unit that acquires first data from a first environment and acquires second data from a second environment; a pairing unit that pairs related data among each piece of data that constitutes the first data and related data among each piece of data that constitutes the second data; a data classification unit that generates a plurality of first data clusters from clustering the results of the pairing for each piece of data that constitutes the first data and generates a plurality of second data clusters from clustering the results of the pairing for each piece of data that constitutes the second data; and a transfer determination unit that generates a plurality of third data clusters by combining and clustering a plurality of the first data clusters and a plurality of the second data clusters and extracts the third data cluster including at least one of the first data clusters.

[0010] A data transfer method according to one aspect of the present invention is a method executed by a data transfer device, comprising: a data management unit of the data transfer device acquiring first data from a first environment and second data from a second environment; a pairing unit of the data transfer device pairing related data among each piece of data that constitutes the first data and pairing related data among each piece of data that constitutes the second data; a data classification unit of the data transfer device generating a plurality of first data clusters from clustering the results of the pairing for each piece of data that constitutes the first data, and generating a plurality of second data clusters from clustering the results of the pairing for each piece of data that constitutes the second data; and a transfer determination unit of the data transfer device generating a plurality of third data clusters by combining and clustering a plurality of the first data clusters and a plurality of the second data clusters, and extracting the third data cluster including at least one of the first data clusters.

[0011] According to the present invention, necessary data can be appropriately extracted from the existing environment.

[0012] FIG. 1 is a block diagram illustrating an example of the configuration of a data transfer device according to an embodiment. FIG. 2 is a flowchart illustrating an example of the operation of a data transfer process performed by a data transfer device according to an embodiment. FIG. 3 is a diagram illustrating an example of the operation of a data transfer process performed by a data transfer device according to an embodiment. FIG. 4 is a diagram illustrating an example of the operation of the word pairing unit 4 of a data transfer device 1 according to an embodiment. FIG. 5A is a diagram illustrating an example of a word list for a production environment before a word pairing process. FIG. 5B is a diagram illustrating an example of a word list for a production environment after a word pairing process. FIG. 6 is a diagram illustrating an example of a word list for an existing environment before and after a word pairing process. FIG. 7 is a diagram illustrating an example of a word match rate between the production environment and the existing environment before and after a word pairing process. FIG. 8 is a block diagram illustrating an example of the hardware configuration of a data transfer device 1 according to an embodiment of the present invention.

[0013] Hereinafter, a data transfer device, a data transfer method, and a computer program according to an embodiment of the present invention will be described with reference to the drawings. In the following embodiments, parts with the same numbers perform similar operations, and redundant explanations will be omitted.

[0014] 1 is a block diagram illustrating an example configuration of a data transfer device 1 according to an embodiment. The data transfer device 1 according to this embodiment extracts data that can be used to train an AI model (hereinafter referred to as a learning model) that outputs a method for dealing with network failures in a target network environment (hereinafter referred to as a production environment (first environment)).

[0015] Furthermore, the data transfer device 1 of this embodiment extracts data from an existing environment similar to the production environment (hereinafter referred to as the "existing environment (second environment)") that can be used for training the learning model from multiple data contained in the existing environment. By supplementing the data from the production environment with the extracted data from the existing environment, it is possible to obtain a sufficient amount of data necessary for training the learning model.

[0016] The data transfer device 1 comprises a control unit 2, an input unit 7, a storage unit 8, a communication unit 9, and an output unit 10. The control unit 2, input unit 7, storage unit 8, communication unit 9, and output unit 10 are each connected via a bus communication line BUS, and transmit and receive data between them. Note that the control unit 2, input unit 7, storage unit 8, communication unit 9, and output unit 10 may also be configured to transmit and receive data between them via wireless communication or the like.

[0017] The input unit 7 may include, for example, a user interface such as a mouse and keyboard for inputting changes to the numerical values ​​of various settings of the data transfer device 1, as well as a microphone, a touch panel, a camera, and various sensors.

[0018] The input unit 7 acquires multiple pieces of data necessary for the learning model to learn from the production environment and inputs (transmits) them to the control unit 2. Similarly, the input unit 7 acquires multiple pieces of corresponding data in the existing environment and inputs (transmits) them to the control unit 2.

[0019] For example, in this embodiment, since a network failure response task is assumed, the data necessary for the learning model to learn may be network log data expressed in text including natural language and symbols, etc. The function of the input unit 7 may be included in the data management unit 3 described later, in which case the data management unit 3 may be configured to acquire a data set for the production environment and a data set for the existing environment, and then transmit the data set for the production environment and the data set for the existing environment to the word pairing unit 4 described later.

[0020] The storage unit 8 includes, for example, a main storage unit and an auxiliary storage unit. The main storage unit may include, for example, a read-only memory (ROM) and a random-access memory (RAM). The ROM is a non-volatile memory used exclusively for reading data, and can store data and various setting values ​​used by the control unit 2 when performing various processes. The RAM is a volatile memory that temporarily stores data when the control unit 2 performs various processes, and can be used as a so-called work area. The main storage unit in this embodiment is, for example, a RAM, and is used as a memory.

[0021] The auxiliary storage unit is a non-transitory computer-readable storage medium of a computer centered around the control unit 2. The auxiliary storage unit is, for example, an EEPROM (registered trademark) (Electric Erasable Programmable Read-Only Memory), an HDD (Hard Disk Drive), or an SSD (Solid State Drive).

[0022] The auxiliary storage unit can store data used by the control unit 2 when performing various processes, data generated by the processes in the control unit 2, various setting values, etc. The auxiliary storage unit is a memory that stores various information, and stores information such as a production environment data set (first data), an existing environment data set (second data), a production environment data cluster (first data cluster), an existing environment data cluster (second data cluster), a reclassified data cluster (third data cluster), etc., which will be described later.

[0023] The communication unit 9 can transmit and receive various data, including data necessary for the learning model to learn (hereinafter referred to as supplementary data), between the data transfer device 1 and an external device (e.g., a learning model). The function of the communication unit 9 may also be included in the data management unit 3, in which case the data management unit 3 may be configured to transmit the supplementary data to the learning model.

[0024] The output unit 10 may include, for example, a display means such as a monitor that allows the processing results of the control unit 2 to be visually recognized, and an audio output means that allows the processing results to be audibly recognized by a speaker, etc. The output unit 10 may be configured to output image information and audio information to a display means and an audio output means that are externally connected.

[0025] The control unit 2 includes at least one of a CPU (Central Process Unit), MPU (Micro Processing Unit), GPU (Graphics Processing Unit), ASIC (Application Specific Integrated Circuit), and FPGA (Field-Programmable Gate Array), and can realize various functions of the data transfer device 1 using programs such as system software, application software, or firmware stored in the storage unit 8.

[0026] The control unit 2 includes a data management unit 3, a word pairing unit 4, a data classification unit 5, and a transfer determination unit 6. The data management unit 3 may include the functions of the input unit 7 as described above, and acquires data of the production environment and data of the existing environment, and transmits the data sets of the production environment and the data sets of the existing environment to the word pairing unit 4.

[0027] The data management unit 3 further has the function of managing the data of the entire data transfer device 1. That is, the data management unit 3 manages various information stored in the storage unit 8, and may also have the function of the communication unit 9 as described above, and transmits supplementary data to the learning model.

[0028] The word pairing unit 4 performs a word pairing process that links words in a data set in the production environment and a data set in the existing environment. The pairing process is a process of identifying words that exist in the existing environment and are similar to words in the production environment, and linking related words together. In the pairing process, for example, the word pairing unit 4 performs a morphological analysis on the data set in the production environment and the data set in the existing environment.

[0029] More specifically, the word pairing unit 4 converts the data set for the production environment and the data set for the existing environment into sets of words with morphemes as the smallest unit, and generates word lists showing these sets of words and the sets of words before conversion as word lists for the production environment and word lists for the existing environment.

[0030] Next, the word pairing unit 4 performs a unique extraction process on the generated word lists for each environment, which is a process of removing duplicate words, and generates unique word lists indicating the set of words after this process as a unique word list for the production environment and a unique word list for the existing environment. The generation of unique word lists corresponds to the regeneration of the above word lists. The unique extraction process is also described in the above-mentioned Non-Patent Document 1.

[0031] The word pairing unit 4 uses the unique word lists for each environment generated as described above to perform a pairing process that links related words managed in the unique word list for the production environment, and generates pairing word lists indicating the results of this pairing process as a pairing word list for the production environment and a pairing word list for the existing environment.

[0032] A first example of a pairing process is a process of classifying words with the same or similar meanings in different languages ​​into one group. A second example of a pairing process is a process of classifying compound words, which are composed of two or more words combined, into one group.

[0033] The word pairing unit 4 transmits the generated paired word list to the data management unit 3. The data management unit 3 transmits the received paired word list for the production environment to the data classification unit 5 and the transfer determination unit 6, and transmits the received paired word list for the existing environment to the data classification unit 5.

[0034] The data classifying unit 5 classifies the data set of the production environment indicated in the pairing word list for the production environment and the data set of the existing environment indicated in the pairing word list for the existing environment (first clustering process). That is, the data classifying unit 5 performs clustering on the data set of the production environment to generate multiple production environment data clusters (first data clusters). The data classifying unit 5 also performs clustering on the data set of the existing environment to generate multiple existing environment data clusters (second data clusters). The data classifying unit 5 transmits the multiple production environment data clusters and the multiple existing environment data clusters to the transfer determination unit 6.

[0035] For example, the data classification unit 5 performs hierarchical clustering on each of the data sets of the production environment and the existing environment using a method such as the shortest distance method, the longest distance method, the group average method, or Ward's method. Note that the clustering method is not limited to the above, and for example, the data classification unit 5 may be configured to perform non-hierarchical clustering on each of the data sets of the production environment and the existing environment using a method such as the k-means method.

[0036] The transfer determination unit 6 performs clustering on a plurality of data clusters that combine the plurality of production environment data clusters generated by the first clustering process with the plurality of existing environment data clusters (second clustering process). The transfer determination unit 6 generates a plurality of reclassified data clusters (third data clusters) by combining the plurality of production environment data clusters with the plurality of existing environment data clusters and performing clustering.

[0037] The transfer determination unit 6 performs hierarchical clustering on multiple data clusters that combine multiple production environment data clusters and multiple existing environment data clusters using a method such as the shortest distance method, the longest distance method, the group average method, or Ward's method. Note that the clustering method is not limited to the above, and for example, the transfer determination unit 6 may be configured to perform non-hierarchical clustering on multiple data clusters that combine multiple production environment data clusters and multiple existing environment data clusters using a method such as the k-means method.

[0038] The transfer determination unit 6 extracts reclassified data clusters including at least one production environment data cluster from the generated reclassified data clusters. The transfer determination unit 6 transmits data on the existing environment in the existing environment data cluster included in the extracted reclassified data cluster to the data management unit 3. The data management unit 3 outputs the data on the existing environment included in the extracted reclassified data cluster as data to be supplemented to the learning model.

[0039] Next, a description will be given of the data transfer process including clustering and data extraction performed by the data transfer device 1 of this embodiment. Figure 2 is a flowchart for explaining an example of the operation of the data transfer process performed by the data transfer device 1 according to one embodiment. Figure 3 is a diagram for explaining an example of the operation of the data transfer process performed by the data transfer device 1 according to one embodiment.

[0040] In this embodiment, for example, a start command based on a user operation or a start command from a higher-level control device can be used as a trigger to start the process.

[0041] The data management unit 3 identifies a learning model to be trained (step S1). In this embodiment, the data transfer device 1 identifies a learning model to be used for network failure response work as a target to be trained.

[0042] Furthermore, the data management unit 3 selects the type of data necessary for the learning model to learn and sets the type of data as an initial value (step S2). For example, the data management unit 3 selects network log data as the type of data necessary for the learning model to learn and sets the network log data as an initial value.

[0043] The data management unit 3 acquires data from the production environment based on the set initial values ​​(step S3). The data management unit 3 acquires data from the existing environment based on the set initial values ​​(step S4). Note that the order of steps S3 and S4 may be reversed, or they may be acquired simultaneously.

[0044] The word pairing unit 4 performs a word pairing process based on the data set of the production environment and the data set of the existing environment (step S5).

[0045] 4 is a diagram illustrating an example of the operation of the word pairing unit 4 of the data transfer device 1 according to an embodiment. In step S5, the word pairing unit 4 converts the data set of the production environment and the data set of the existing environment into sets of words with morphemes as the smallest unit, and generates word lists indicating these words and the sets of words before conversion as a word list for the production environment and a word list for the existing environment (S51).

[0046] The word pairing unit 4 performs a unique extraction process, which is a process of removing duplicate words, on the generated word lists for each environment, and generates unique word lists showing the set of words after this process as a unique word list for the production environment and a unique word list for the existing environment (S52).

[0047] The word pairing unit 4 uses the generated unique word lists for each environment to perform a pairing process that links related words managed in the unique word list for the production environment, and generates paired word lists indicating the results of this pairing process as a paired word list for the production environment and a paired word list for the existing environment (S53). The word pairing unit 4 transmits the generated paired word lists to the data management unit 3, and then ends the process of S5.

[0048] 5A is a diagram illustrating an example of a word list for a production environment before the word pairing process. FIG. 5B is a diagram illustrating an example of a word list for a production environment after the word pairing process. The examples illustrated in FIGS. 5A and 5B show that the word pairing process on the word list for the production environment results in word pairing results including (1) "device module" | module, (2) "state" | state, (3) "bgp|ospf|"bgp fsm"", (4) "neighbor|neighbor|peer|peer", (5) "number|id", (6) "ip|ip address|"IP address"", (7) "index" | index, (8) "status" | status, and (9) "down|down".

[0049] 6 is a diagram illustrating an example of a word list for an existing environment before and after the word pairing process. The example illustrated in FIG. 6 shows that the word pairing process on the word list for the existing environment results in word pairing results including (1) “config”, (2) “bps|kbps|gbps”, (3) “device module”|module”, (4) “state”, (5) “bgp|ospf|“bgp fsm””, (6) “neighbor|neighbor|peer”, and (7) “error|error|abnormal”.

[0050] As a result, it can be seen that (1) "device module"|module, (2) "state|state", (3) "bgp|ospf|"bgp fsm"", and (4) "adjacent|neighbor|peer|peer" are word pairing results that are common between the production environment and the existing environment, and are data that can be used as supplementary data from the existing environment to the production environment.

[0051] 7 is a diagram showing an example of the word matching rate between the production environment and the existing environment before and after the word pairing process. The example shown in Fig. 7 shows the calculation results of the matching rate between a set of words in the production environment and a set of words in the existing environment before and after the word pairing process when using the Jaccrd coefficient, the Simpson coefficient, and the Dice coefficient, respectively.

[0052] 7, regardless of which of the three types of coefficients described above is used, the match rate after the word pairing process is higher than the match rate before the word pairing process. In other words, by performing the word pairing process, the amount of data in the existing environment that can be used in the production environment has increased.

[0053] After step S5, the data classification unit 5 performs clustering on the data set of the production environment to generate multiple production environment data clusters (step S6). Furthermore, the data classification unit 5 performs clustering on the data set of the existing environment to generate multiple existing environment data clusters (step S7). Note that the order of steps S6 and S7 may be reversed, or they may be performed simultaneously.

[0054] The transfer determination unit 6 performs clustering by combining the multiple production environment data clusters generated in step S6 and the multiple existing environment data clusters generated in step S7, thereby generating multiple reclassified data clusters including at least one production environment data cluster (step S8). This clustering in step S8 can also be called inter-environment clustering.

[0055] The transfer determination unit 6 extracts data of the existing environment from the reclassified data clusters including at least one production environment data cluster from among the plurality of reclassified data clusters in step S8 (step S9).

[0056] The data management unit 3 outputs the extracted data of the existing environment to the learning model as supplementary data necessary for learning by the learning model, and ends the process (step S10).

[0057] By the data transfer device 1 repeatedly performing the above steps S1 to S10 in multiple different existing environments, the learning model can obtain the production environment data necessary for learning and supplementary data for multiple different existing environments.

[0058] According to the data transfer device 1 of the embodiment, by performing the data clustering process twice, the number of data classification / extraction targets is reduced, and efficient processing is realized. Furthermore, by performing clustering processing on data within the same environment in the first clustering process by the data transfer device 1, the number of targets for the second clustering process is reduced, and it is possible to reduce the amount of calculation required for processing while maintaining the accuracy of data classification.

[0059] In other words, according to this embodiment, when transferring data between the production environment and the existing environment, the amount of data available for the production environment is increased by pairing words that have the same or similar meaning but different forms of expression in the existing environment data that cannot be used as production environment data as is. Thus, a data transfer device, data transfer method, and computer program can be provided that can appropriately extract the data necessary for the production environment.

[0060] In the above embodiment, the data management unit 3, word pairing unit 4, data classification unit 5, transfer determination unit 6, and storage unit 8 may be mounted on different devices. In this case, the multiple devices included in the data transfer device 1 may share various information stored in the storage unit 8.

[0061] 8 is a block diagram showing an example of the hardware configuration of a data transfer device according to one embodiment of the present invention. In the example shown in FIG. 8, the data transfer device 1 according to the above embodiment is configured, for example, as a server computer or personal computer, and has a hardware processor 111A such as a CPU. Program memory 111B, data memory 112, input / output interface 113, and communication interface 114 are connected to this hardware processor 111A via a bus 115.

[0062] The communication interface 114 includes, for example, one or more wireless communication interface units, and enables transmission and reception of information to and from the communication network NW. As the wireless interface, for example, an interface that adopts a low-power wireless data communication standard such as a wireless LAN (Local Area Network) is used.

[0063] In the example shown in FIG. 8, an input device 200 and an output device 300 that are attached to the data transfer device 1 and used by users and the like are connected to the input / output interface 113 .

[0064] The input / output interface 113 takes in operation data input by a user or the like via an input device 200 such as a keyboard, touch panel, touchpad, mouse, or the like, and outputs and displays the output data to an output device 300, which may include a display device using liquid crystal or organic electroluminescence (EL) or the like. Note that the input device 200 and output device 300 may be devices built into the video processing device 100, such as the input unit 7 and output unit 10 shown in FIG. 1, or may be input and output devices of another computer or information terminal that can communicate with the data transfer device 1 via the network NW.

[0065] The program memory 111B is a non-transitory tangible storage medium that is a combination of a non-volatile memory such as an HDD or SSD that can be written to and read at any time, and a non-volatile memory such as a ROM, and stores the programs necessary to execute various control processes, etc., according to one embodiment.

[0066] The data memory 112 is a tangible storage medium, for example, a combination of the above-mentioned nonvolatile memory and a volatile memory such as RAM, and is used to store various data acquired and created during various processing steps.

[0067] The data transfer device 1 according to one embodiment of the present invention can be configured as an information processing device having a software-implemented processing function unit. This processing function unit corresponds to each unit of the data transfer device 1 shown in FIG.

[0068] The storage device used as work memory or the like by the data transfer device 1 can be configured using the data memory 112 shown in Fig. 8. However, these configured storage areas are not essential components within the data transfer device 1, and may be, for example, areas provided in an external storage medium such as a USB (Universal Serial Bus) memory, or in a storage device such as a database server located in the cloud.

[0069] The processing function units in each of the above-mentioned units can be realized by reading and executing a program stored in the program memory 111B by the hardware processor 111A. Note that some or all of these processing function units may be realized in various other forms, including integrated circuits such as application specific integrated circuits (ASICs or FPGAs).

[0070] The methods described in each embodiment may be stored as a program (software means) that can be executed by a computer on a recording medium such as a magnetic disk (e.g., a floppy disk, a hard disk, etc.), an optical disk (e.g., a CD-ROM, a DVD, an MO, etc.), or a semiconductor memory (e.g., a ROM, a RAM, a flash memory, etc.), or may be transmitted and distributed via a communication medium. The program stored on the medium also includes a configuration program that configures the software means (including not only execution programs but also tables and data structures) that the computer executes. The computer that realizes this device reads the program stored on the recording medium and, in some cases, configures the software means using the configuration program, and executes the above-described processing by having the operation controlled by this software means. The term "recording medium" as used herein is not limited to a storage medium for distribution, but also includes a storage medium such as a magnetic disk or semiconductor memory installed inside the computer or in a device connected via a network.

[0071] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected elements from the disclosed elements. For example, if the problem can be solved and the desired effect can be obtained even if some elements are deleted from all elements shown in the embodiments, the configuration from which these elements are deleted can be extracted as an invention.

[0072] DESCRIPTION OF SYMBOLS 1... Data transfer device 2... Control unit 3... Data management unit 4... Word pairing unit 5... Data classification unit 6... Transfer determination unit 7... Input unit 8... Storage unit 9... Communication unit 10... Output unit

Claims

1. A data transfer device comprising: a data management unit that acquires first data from a first environment and second data from a second environment; a pairing unit that performs pairing of related data in each piece of data that constitutes the first data and pairing of related data in each piece of data that constitutes the second data; a data classification unit that generates a plurality of first data clusters from clustering of results of the pairing of each piece of data that constitutes the first data, and generates a plurality of second data clusters from clustering of results of the pairing of each piece of data that constitutes the second data; and a transfer determination unit that generates a plurality of third data clusters by combining and clustering a plurality of the first data clusters and a plurality of the second data clusters, and extracts the third data cluster including at least one of the first data clusters.

2. The data transfer device according to claim 1, wherein the first data and the second data are data represented by text including words, and the pairing unit performs pairing of related words among the words constituting the first data and pairing of related words among the words constituting the second data.

3. The data transfer device according to claim 1, wherein the pairing unit performs pairing of data that have the same or similar meaning but different expressions in each piece of data that constitutes the first data, and pairing of data that have the same or similar meaning but different expressions in each piece of data that constitutes the second data.

4. The data transfer device according to claim 1, wherein the data to be paired by said pairing unit includes any one of the words of a compound word formed by combining a plurality of words.

5. A method executed by a data transfer device, comprising: a data management unit of the data transfer device acquiring first data from a first environment and acquiring second data from a second environment; a pairing unit of the data transfer device pairing related data in each piece of data constituting the first data and pairing related data in each piece of data constituting the second data; a data classification unit of the data transfer device generating a plurality of first data clusters from clustering a result of the pairing of each piece of data constituting the first data, and generating a plurality of second data clusters from clustering a result of the pairing of each piece of data constituting the second data; and a transfer determination unit of the data transfer device generating a plurality of third data clusters by combining and clustering a plurality of the first data clusters and a plurality of the second data clusters, and extracting the third data cluster including at least one of the first data clusters.

6. A data transfer method as described in claim 5, wherein the first data and the second data are data represented by text including words, and the pairing unit performs pairing of related words among the words constituting the first data and pairing of related words among the words constituting the second data.

7. A data transfer method as described in claim 5, wherein the pairing unit performs pairing of data that have the same or similar meaning but different expressions in each piece of data constituting the first data, and pairing of data that have the same or similar meaning but different expressions in each piece of data constituting the second data.

8. A computer program causing a computer to function as the data transfer device according to any one of claims 1 to 4.