Data processing system distribution device, data processing system distribution method, and data processing system distribution program

Abstract

The attribute information acquisition unit (110) acquires attribute information of target data to be subjected to data processing. The affinity score calculation unit (120) refers to processing method information including attribute information of each of a plurality of processing methods, and calculates an affinity score of the processing method for the target data for each processing method based on the attribute information of the processing method and the attribute information of the target data. The recommendation information output unit (130) selects a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputs recommendation information indicating the recommendation method.

Description

[Technical field]

[0001] The present disclosure relates to a technique for recommending a data processing method. [Background technology]

[0002] Patent Document 1 discloses a system for distributing processed data. This system independently manages source data and the processing method for processing the source data, and transmits a combination of the processed data and the processing method in response to a request for data processing. Patent Document 1 describes a method of recommending a processing method by advertising a sample of the processed data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2010-193506 A Summary of the Invention [Problem to be solved by the invention]

[0004] The system of Patent Document 1 aims to distribute dedicated processing methods for specific data. Therefore, the system of Patent Document 1 cannot recommend a processing method for processing unspecified data.

[0005] An object of the present disclosure is to make it possible to recommend a processing method for processing unspecified data. [Means for solving the problem]

[0006] The data processing method distribution device of the present disclosure includes: an attribute information acquisition unit for acquiring attribute information of target data to be processed; an affinity score calculation unit that refers to processing method information including attribute information of each of a plurality of processing methods, and calculates an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; a recommendation information output unit that selects a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputs recommendation information indicating the recommended method; Equipped with. Effect of the Invention

[0007] According to the present disclosure, it is possible to recommend a processing method for processing unspecified data. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a configuration diagram of a data processing method distribution device 100 according to a first embodiment. [Diagram 2] FIG. 2 is a functional configuration diagram of a data processing method distribution device 100 according to the first embodiment. [Diagram 3] 3 is a flowchart of a data processing method distribution method in the first embodiment. [Figure 4] FIG. 11 shows an example of attribute information 199 of target data in the first embodiment. [Diagram 5] FIG. 11 shows an example of attribute information 192 of a processing method according to the first embodiment. [Figure 6] FIG. 11 is a diagram showing an example of a flowchart of step S120 in the first embodiment. [Figure 7] FIG. 13 shows an example of a data format classification table 193 in the first embodiment. [Figure 8] FIG. 11 is a diagram showing an example of a flowchart of step S121 in the first embodiment. [Figure 9] FIG. 4 shows an example of an affinity score in the first embodiment. [Figure 10] FIG. 11 is a diagram showing an example of a flowchart of step S123 in the first embodiment. [Figure 11]FIG. 4 shows an example of a combination of columns in table data according to the first embodiment. [Figure 12] FIG. 11 is a configuration diagram of a data processing method distribution device 100 according to a second embodiment. [Figure 13] FIG. 11 is a functional configuration diagram of a data processing method distribution device 100 according to a second embodiment. [Figure 14] 13 is a flowchart of a data processing system distribution method in the second embodiment. [Figure 15] FIG. 11 is a configuration diagram of a data processing method distribution device 100 according to a third embodiment. [Figure 16] FIG. 11 is a functional block diagram of a data processing method distribution device 100 according to a third embodiment. [Figure 17] 13 is a flowchart of a data processing system distribution method in the third embodiment. [Figure 18] FIG. 11 is a diagram showing an example of a flowchart of step S310 in the third embodiment. [Figure 19] FIG. 13 is a diagram showing an example of analysis of a data body in the third embodiment. [Figure 20] FIG. 1 is a hardware configuration diagram of a data processing method distribution device 100 according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] In the embodiments and drawings, the same or corresponding elements are denoted by the same reference numerals. Descriptions of elements denoted by the same reference numerals as those of the elements described above will be omitted or simplified as appropriate. Arrows in the drawings mainly indicate data flow or processing flow.

[0010] Embodiment 1 The data processing method distribution device 100 will be described with reference to FIGS.

[0011] ***Configuration Description*** The configuration of a data processing method distribution device 100 will be described with reference to FIG. The data processing method distribution device 100 is a computer equipped with hardware such as a processor 101, a memory 102, an auxiliary storage device 103, and an input / output interface 104. These pieces of hardware are connected to each other via signal lines.

[0012] The processor 101 is an IC that performs arithmetic processing and controls other hardware. For example, the processor 101 is a CPU. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit.

[0013] The memory 102 is a volatile or non-volatile storage device. The memory 102 is also called a primary storage device or a main memory. For example, the memory 102 is a RAM. Data stored in the memory 102 is saved in the secondary storage device 103 as necessary. RAM is an abbreviation for Random Access Memory.

[0014] The auxiliary storage device 103 is a non-volatile storage device. For example, the auxiliary storage device 103 is a ROM, a HDD, a flash memory, or a combination of these. Data stored in the auxiliary storage device 103 is loaded into the memory 102 as needed. ROM is an abbreviation for Read Only Memory. HDD is an abbreviation for Hard Disk Drive.

[0015] The input / output interface 104 is a port to which an input device and an output device are connected. For example, the input / output interface 104 is a USB terminal, the input device is a keyboard, a mouse, and a communication device, and the output device is a display and a communication device. Input and output of the data processing method distribution device 100 is performed using the input / output interface 104. USB is an abbreviation for Universal Serial Bus.

[0016] The data processing method distribution device 100 comprises elements such as an attribute information acquisition unit 110, an affinity score calculation unit 120, and a recommendation information output unit 130. These elements are realized by software. The attribute information acquiring unit 110 includes an element called a data information accepting unit 111 . The recommendation information output unit 130 includes an element called a recommendation scheme output unit 131 .

[0017] The auxiliary storage device 103 stores a data processing method distribution program for causing a computer to function as an attribute information acquisition unit 110, an affinity score calculation unit 120, and a recommendation information output unit 130. The data processing method distribution program is loaded into the memory 102 and executed by the processor 101. The auxiliary storage device 103 further stores an OS. At least a part of the OS is loaded into the memory 102 and executed by the processor 101. The processor 101 executes the data processing method distribution program while executing the OS. OS is an abbreviation for Operating System.

[0018] Input and output data of the data processing method distribution program is stored in the storage unit 190. The auxiliary storage device 103 functions as the storage unit 190. However, a storage device such as the memory 102, a register in the processor 101, or a cache memory in the processor 101 may function as the storage unit 190 instead of the auxiliary storage device 103 or together with the auxiliary storage device 103.

[0019] The data processing method distribution program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or a flash memory.

[0020] FIG. 2 shows the functional configuration of the data processing method distribution device 100. The processing method information 191 is stored in advance in the storage unit 190. The contents of the processing method information 191 will be described later.

[0021] ***Explanation of Operation*** The operation procedure of the data processing system distribution device 100 corresponds to a data processing system distribution method. Also, the operation procedure of the data processing system distribution device 100 corresponds to a processing procedure by a data processing system distribution program.

[0022] The data processing method and distribution method will be explained based on FIG. The data processing method distribution method is a method for recommending to a user a processing method suitable for processing target data.

[0023] In step S110, the attribute information acquisition unit 110 acquires the attribute information 199 of the target data.

[0024] Specifically, a user inputs attribute information 199 of the target data to data processing method distribution device 100. Then, data information receiving unit 111 receives attribute information 199 that has been input.

[0025] The data information receiving unit 111 may provide assistance for inputting the attribute information 199 of the target data. For example, an attribute information list is stored in advance in the memory unit 190. The attribute information list indicates attribute information of each of one or more data. The attribute information of each data is collected in advance by a different method. The data information receiving unit 111 displays the attribute information list on a display. The user designates any of the attribute information in the displayed attribute information list as attribute information 199 of the target data. The data information receiving unit 111 extracts the designated attribute information from the attribute information list.

[0026] The target data is data that is the target of data processing. In other words, the target data is the data that the user wants to process.

[0027] The attribute information 199 of the target data indicates various attributes of the target data.

[0028] FIG. 4 shows an example of attribute information 199 of the target data. An example of the target data is time-series data of power measured by a terminal device. The attribute information 199 indicates the data name, data content, data format, and data structure of the target data. The data name column indicates the name of the target data. The data content column indicates the content of the target data. The data format column indicates the data format of the target data. For example, the data format of the target data indicates the major classification and minor classification of the target data. The major classification and minor classification will be described later. The data structure column shows the data structure of the target data. Specifically, it shows the multiple data elements that make up the target data. If the data format of the target data is a table, the number, column name, and data type of each column in the table are shown.

[0029] Returning to FIG. 3, the description will continue from step S120. In step S120, the affinity score calculation unit 120 refers to the processing method information 191. The processing method information 191 includes attribute information 192 for each of a plurality of processing methods.

[0030] Specifically, the affinity score calculation unit 120 reads attribute information 192 of each processing method from the processing method information 191. All of the attribute information 192 may be read at once, or when there are a large number of processing methods, the attribute information 192 may be read in a small number of cases at a time.

[0031] The processing method is the method of processing data. Specifically, the processing method is a program or algorithm for processing data. The program and algorithm can be used for general purposes without being limited to specific data. Examples of data processing are data analysis and data shaping.

[0032] The processing method attribute information 192 indicates various attributes of the processing method. The attribute information 192 may be input by a user and registered in the processing method information 191, or may be generated by analyzing annotations in a program (processing method) and a program setting file, etc., and registered in the processing method information 191.

[0033] FIG. 5 shows an example of attribute information 192 of the processing method. An example of a processing method is a program that detects state transitions in time-series data. The attribute information 192 indicates, for the processing method, the processing name, the processing content, the input data format, the input data structure, the output data format, the output data structure, and the form of provision. The processing name column indicates the name of the processing method. The processing content column indicates the content of data processing according to the processing method. The input data format column indicates the data format of the input data for the processing method. The input data for the processing method is subjected to data processing according to the processing method. The input data structure column shows the data structure of the input data for the processing method. The output data format column indicates the data format of the output data of the processing method. The output data of the processing method is obtained by applying data processing according to the processing method to the input data. The output data structure column indicates the data structure of the output data of the processing method. The column of "Form of provision" indicates the form in which the data processing is provided by the processing method. Specifically, the form of provision is a program or an algorithm.

[0034] The data formats of the input data and the output data are expressed, for example, by major and minor categories. Examples of broad categories are tables, semi-structured, images, audio, video and text. A minor category is expressed at a finer level of granularity than a major category. For example, a minor category corresponds to a file extension. When the major category is a table, examples of extensions that represent a minor category are CSV and TSV. CSV is an abbreviation for comma-separated values. TSV is an abbreviation for tab-separated values.

[0035] The data structure of each of the input data and output data is described based on the data format. The data structure is represented by the multiple data elements that make up the data. If the data format is a table, the data structure is represented by the number, column name, and data format for each column of the table.

[0036] If the processing method can process a plurality of patterns of input data, the input data format field can indicate a plurality of data formats, and the input data structure field can indicate a plurality of data structures.

[0037] The data format column in the attribute information 199 of the target data indicates information similar to the information in the input data format column in the attribute information 192 of the processing method. The data structure column in the attribute information 199 of the target data indicates information similar to the information in the input data structure column in the attribute information 192 of the processing method.

[0038] Returning to FIG. 3, the description of step S120 will continue. The affinity score calculation unit 120 calculates, for each processing method, an affinity score of the processing method for the target data based on attribute information 192 of the processing method and attribute information 199 of the target data.

[0039] The affinity score is a numerical representation of the probability that the processing method can be effectively applied to the target data. The affinity score may be a numerical representation of an index or degree similar to that probability.

[0040] The affinity score is calculated based on attribute information 192 of the processing method and attribute information 199 of the target data as follows. The affinity score calculation unit 120 calculates an affinity score for each processing method based on the data format of the input data of the processing method and the data format of the target data. In particular, the affinity score calculation unit 120 calculates an affinity score for each processing method based on a comparison result between the major categories of the input data of the processing method and the target data, and a comparison result between the minor categories of the input data of the processing method and the target data. The affinity score calculation unit 120 calculates an affinity score for each processing method based on the data structure of the input data of the processing method and the data structure of the target data. In particular, the affinity score calculation unit 120 calculates the affinity score for each processing method based on multiple data elements of the input data of the processing method and multiple data elements of the target data.

[0041] The affinity score calculation unit 120 may calculate the affinity score based on both the data format and the data structure, or may calculate the affinity score based on either the data format or the data structure. The affinity score calculation section 120 may calculate the affinity score by taking into consideration other attribute elements of the attribute information.

[0042] The calculated affinity score may be stored as history data. In the history data, the affinity score is associated with the target data and the processing method. When an affinity score (the affinity score) for the same target data using the same processing method is calculated, the affinity score calculation unit 120 may obtain the affinity score from the history data instead of calculating the affinity score.

[0043] Step S120 will be described in detail with reference to FIG. Steps S121 to S125 are executed for each processing method.

[0044] In step S121, the affinity score calculation unit 120 calculates an affinity score related to the data format based on the data format of the input data of the processing method and the data format of the target data.

[0045] The affinity score for a data type is calculated as follows: The affinity score calculation unit 120 compares the data format of the input data of the processing method with the data format of the target data, and calculates an affinity score for the data format based on the degree of agreement between the data format of the input data of the processing method and the data format of the target data.

[0046] To calculate the affinity score for the data format, for example, the data format classification table 193 is used. The data type classification table 193 is a table showing the relationship between major classifications and minor classifications, and is stored in the storage unit 190 in advance.

[0047] FIG. 7 shows an example of the data type classification table 193. Data formats are classified into major and minor categories. The major categories are based on the nature of the data. The major category "table data" may be read as "structured data." The minor categories are classified based on the file extension. The extensions shown in the minor category column are examples. Extensions not shown here may be added to the minor categories. Also, if there is information other than the extension that can specify the data format, that information may be used instead of or together with the extension.

[0048] Step S121 will be described in detail with reference to FIG. In step S1211, the affinity score calculation unit 120 compares the major classification of the input data of the processing method with the major classification of the target data.

[0049] In step S1212, the affinity score calculation unit 120 determines whether the major classification of the input data of the processing method matches the major classification of the target data, based on the comparison result in step S1211. If the major classification of the input data of the processing method matches the major classification of the target data, the process proceeds to step S1213. If the major classification of the input data of the processing method does not match the major classification of the target data, the process proceeds to step S1217.

[0050] In step S1213, the affinity score calculation unit 120 compares the subcategory of the input data of the processing method with the subcategory of the target data.

[0051] In step S1214, the affinity score calculation unit 120 determines whether the subcategory of the input data of the processing method includes the subcategory of the target data, based on the comparison result in step S1213.

[0052] When a processing method supports multiple data formats, the data formats of the input data of the processing method may include multiple sub-categories. Therefore, subcategory inclusion is determined rather than subcategory match.

[0053] If the subcategory of the input data of the processing method includes the subcategory of the target data, the process proceeds to step S1215. If the subcategory of the input data of the processing method does not include the subcategory of the target data, the process proceeds to step S1216.

[0054] In step S1215, the affinity score calculation unit 120 sets the affinity score for the data format to a high score. The high score is a predetermined score and is higher than the low score. For example, the high score is 10 points. The high score corresponds to the highest score out of the three levels of scores.

[0055] In step S1216, the affinity score calculation unit 120 sets the affinity score for the data format to a low score. The low score is a predetermined score that is higher than no score and lower than a high score. For example, the low score is 5 points. The high score corresponds to the middle score of the three-level score. In this case, it may be possible to apply the processing method by converting the data format of the target data. Therefore, the affinity score for the data format is set to a low score rather than zero.

[0056] In step S1217, the affinity score calculation unit 120 sets the affinity score for the data format to zero. Zero score is 0 points. Zero score corresponds to the lowest score out of the three levels of scores. However, when the major classification of the input data of the processing method is similar to the major classification of the target data, the affinity score calculation unit 120 may set the affinity score for the data format to a score according to the similarity of the major classification, rather than to no score. The similarity relationship of the major classifications is determined in advance and managed, for example, in a table format. For example, a data format whose major classification is a table is partially similar to a data format whose major classification is semi-structured.

[0057] FIG. 9 shows examples of affinity scores for data formats. In combination A, the major classification of the input data of the processing method matches the major classification of the target data, and the minor classification of the input data of the processing method encompasses the minor classification of the target data. Therefore, the affinity score for the data format is high. In combination B, the major classification of the input data of the processing method matches the major classification of the target data, and the minor classification of the input data of the processing method does not include the minor classification of the target data. Therefore, the affinity score for the data format is low. In combination C, the major classification of the input data of the processing method does not match the major classification of the target data. Therefore, the affinity score for the data format is zero.

[0058] Returning to FIG. 6, the description will continue from step S122. In step S122, the affinity score calculation unit 120 determines whether the affinity score related to the data format is equal to or greater than a threshold value.

[0059] This determination is made to determine whether it is possible to compare the data structure of the input data for the processing method with the data structure of the target data.

[0060] For example, the threshold value is a low score, in which case an affinity score for a data type is equal to or greater than the threshold value if the affinity score for the data type is a high score or a low score.

[0061] If the affinity score for the data format is greater than or equal to the threshold, the process proceeds to step S123. If the affinity score for the data type is less than the threshold, processing proceeds to step S124.

[0062] In step S123, the affinity score calculation unit 120 calculates an affinity score related to the data structure based on the data structure of the input data of the processing method and the data structure of the target data.

[0063] The affinity score for a data structure is calculated as follows: The affinity score calculation unit 120 compares the data structure of the input data of the processing method with the data structure of the target data, and calculates an affinity score for the data structure based on the degree of agreement between the data structure of the input data of the processing method and the data structure of the target data.

[0064] The data structure differs for each data format, so the affinity score for the data structure is calculated according to a procedure defined for each data format. A procedure for calculating an affinity score for a data structure is prepared, for example, for each major classification of data formats. If the data format is tabular data, the column name and data type of each column contributes to the affinity score for the data structure. If there are other columns, the information in the other columns also contributes to the affinity score for the data structure. When the data format is semi-structured data, the element name (key name) and the data type of the value (value) for each element separated by tags or symbols contribute to the affinity score for the data structure. By utilizing these relationships, semi-structured data can be treated like table data. If the data format is unstructured data, there is no element that contributes to the affinity score regarding the data structure, so step S123 may be skipped. Examples of unstructured data are text data, image data, audio data, and video data.

[0065] Based on FIG. 10, the details of step S123 in the case where the data format of the input data for the processing method and the data format of the target data are table data will be described.

[0066] Steps S1231 to S1233 are executed for each column of input data of the processing method.

[0067] In step S1231, the affinity score calculation unit 120 compares the string of input data of the processing method with each string of the target data.

[0068] Specifically, the affinity score calculation unit 120 compares the columns of the input data of the processing method with each column of the target data for each column name and data type.

[0069] Explains column name comparison. A thesaurus mechanism can be used to compare column names. The column name thesaurus shows, for each word used in a column name, words similar to the word and words in an inclusive relationship with the word. The column name thesaurus is stored in advance in the storage unit 190. The affinity score calculation unit 120 extracts a group of column names for each of the columns of the input data of the processing method and the columns of the target data from the column name thesaurus. The group of column names consists of the target column name, column names similar to the target column name (similar column names), and column names in an inclusive relationship with the target column name (inclusive column names). Then, the affinity score calculation unit 120 compares the column names of the input data of the processing method with the column names of the target data.

[0070] Explain data type comparison. A data type relationship table can be used to compare data types. The data type relationship table shows data types that are in an inclusive relationship for each data type. For example, the data type "numeric" includes data types such as "integer" and "real number". However, the data type "integer" and the data type "real number" do not include the data type "numeric". The data type relationship table is stored in advance in storage unit 190.

[0071] In step S1232, the affinity score calculation unit 120 calculates, for each column of the target data, an affinity score of the input data column of the processing method for the column of the target data, based on the comparison result of step S1231.

[0072] Specifically, the affinity score calculation unit 120 calculates the affinity score of the input data column of the processing method for each column of the target data based on the comparison result of the column names and the comparison result of the data types. For example, the affinity score calculation unit 120 calculates an affinity score based on the comparison result of column names and an affinity score based on the comparison result of data types. Then, the affinity score calculation unit 120 performs an operation such as sum or product on the affinity score based on the comparison result of column names and the affinity score based on the comparison result of data types to calculate the affinity score of the input data column of the processing method for the target data column.

[0073] The affinity score based on the comparison of column names is explained. The affinity score calculation unit 120 calculates the similarity and inclusion of the column name of the processing method to the column name of the target data for each column of the target data, and calculates the affinity score based on the similarity and inclusion. The higher the similarity, the higher the affinity score. Also, the higher the inclusion, the higher the affinity score. An example of similarity will be described. Assume that the column name of the input data for the processing method is "power value." In this case, if the column name of the target data is "current value," the similarity is high. On the other hand, if the column name of the target data is "age," the similarity is low. An example of inclusion is explained. Assume that the column name of the input data of the processing method is "measurement value". "Measurement value" includes "current value" in terms of meaning. In this case, if the column name of the target data is "current value", the inclusion degree is high.

[0074] Affinity scores based on the results of data type comparisons are described below. If the data type of the input data of the processing method encompasses the data type of the target data, the affinity score is high.

[0075] In step S1233, the affinity score calculation unit 120 selects the maximum affinity score from the affinity scores of the columns of the input data of the processing method for each column of the target data. This allows the column of the target data that corresponds to the highest affinity score to be combined with the column of the input data of the processing method.

[0076] The input data column of the processing method is best matched with the column of the target data that corresponds to the highest affinity score. In other words, the column of the target data that most closely matches the column of the input data for the processing method is determined.

[0077] The affinity score calculation unit 120 may prevent one string of target data from being combined with two or more strings of input data of the processing method. In other words, the affinity score calculation unit 120 may be designed so that one string of the target data is combined with one string of the input data of the processing method. For example, the affinity score calculation unit 120 selects the maximum affinity score from the affinity scores of columns of the input data of the processing method for each column of the target data, excluding columns from the target data that are combined with any columns of the input data of the processing method.

[0078] In steps S1231 to S1233, an affinity score is calculated for each pair of a column of input data of the processing method and a column of target data.

[0079] FIG. 11 shows an example of a combination of input data columns and target data columns for a processing method. In the case of FIG. 11, column 1 of the input data for the processing method best matches column 2 of the target data, and an affinity score for the pair of column 1 of the input data for the processing method and column 2 of the target data is calculated. In addition, column 2 of the input data for the processing method best matches column 1 of the target data, and an affinity score for the pair of column 2 of the input data for the processing method and column 1 of the target data is calculated. In addition, column 3 of the input data for the processing method best matches column 3 of the target data, and an affinity score for the pair of column 3 of the input data for the processing method and column 3 of the target data is calculated.

[0080] Returning to FIG. 10, step S1234 will be described. In step S1234, the affinity score calculation unit 120 calculates an affinity score for the data structure by using the affinity score of each pair of the input data string and the target data string of the processing method.

[0081] For example, the affinity score is calculated by performing an operation such as sum or product on the affinity scores of each pair of the input data column of the processing method and the target data column.

[0082] This makes it possible to calculate an affinity score based on the results of comparing all columns of the input data of the processing method with the target data as an affinity score for the data structure.

[0083] Returning to FIG. 6, the description will continue from step S124. In step S124, the affinity score calculation unit 120 calculates affinity scores for other attribute elements based on the attribute information 192 about the input data of the processing method and the attribute information 199 of the target data. The other attribute elements are the remaining attribute elements excluding the data format and the data structure, such as the data acquisition status, the data acquisition purpose, and the data quality.

[0084] Specifically, the affinity score calculation unit 120 compares other attribute elements of the input data of the processing method with other attribute elements of the target data, and calculates an affinity score for the other attribute elements based on the comparison result.

[0085] The affinity score calculation section 120 may use characteristic attribute elements based on the data format among the other attribute elements to calculate affinity scores related to the other attribute elements. For example, affinity scores for other attribute elements are calculated as follows: When the input data and target data for the processing method are text data, the affinity score calculation unit 120 calculates an affinity score for other attribute elements based on attribute elements such as language type and character code. When the input data and the target data of the processing method are image data, the affinity score calculation unit 120 calculates affinity scores for other attribute elements based on attribute elements such as the number of pixels, the type of data format, and the type of subject. Examples of the type of data format are illustrations and photographs, etc. Examples of the type of subject are humans and objects, etc. When the input data and target data of the processing method are voice data, the affinity score calculation unit 120 calculates affinity scores for other attribute elements based on attribute elements such as sampling rate and voice type. Examples of voice types include human voice and music.

[0086] In step S125, the affinity score calculation unit 120 calculates a final affinity score using the affinity score related to the data format, the affinity score related to the data structure, and the affinity scores related to other attribute elements.

[0087] Specifically, the affinity score calculation unit 120 performs a specific calculation on the affinity score related to the data format, the affinity score related to the data structure, and the affinity score related to other attribute elements to calculate one affinity score. The calculated one affinity score is the final affinity score. Examples of specific operations are a sum, a product, an average, a weighted average, or combinations thereof.

[0088] Returning to FIG. 3, the explanation will be continued. In step S120, an affinity score for the target data is calculated for each processing method.

[0089] In step S130, the recommendation information output unit 130 selects a processing method to be a recommended method from the multiple processing methods based on the affinity scores of each of the multiple processing methods. The recommendation method is a processing method recommended for data processing of the target data.

[0090] For example, the recommendation method output unit 131 selects the processing method having the highest affinity score for the target data as the recommendation method.

[0091] The explanation of step S130 will be continued. The recommendation information output unit 130 outputs recommendation information indicating the recommendation method. For example, the recommendation method output unit 131 displays the recommendation information on a display.

[0092] The recommendation information output unit 130 may output a program for data processing according to a recommendation method as recommendation information. For example, the recommendation scheme output unit 131 stores the recommendation information in a storage medium. After step S130, the process ends.

[0093] ***Features of the First Embodiment*** The data processing method distribution device 100 compares the attribute information of the target data that the user wishes to process with each processing method distributed by this device to calculate an affinity score. The affinity score represents the probability or degree to which the processing method can be effectively applied to the target data. The data processing method distribution device 100 recommends a processing method that has not been used for the target data, if the processing method has a high affinity score for the target data.

[0094] ***Advantages of the First Embodiment*** The first embodiment has an advantage not available in the conventional technology in that it is possible to recommend a processing method that has not been used in the past for the processing target data but is likely to be effectively applicable to the processing target data. This allows users to use many data processing methods via the data processing method distribution device 100. As a result, users can effectively utilize data.

[0095] In the first embodiment, the attributes of each processing method are compared for the data to be processed. This makes it possible to recommend even processing methods that have not been used in the past.

[0096] In the first embodiment, the data format and data structure are compared for each combination of data to be processed and the processing method, thereby improving the accuracy of recommendations.

[0097] Embodiment 2 The embodiment for recommending two or more processing methods that can be applied in a chain will be described below with reference to Figs. 12 to 14, mainly with respect to the points that differ from the first embodiment.

[0098] ***Configuration Description*** The configuration of the data processing method distribution device 100 will be described with reference to FIG. The recommendation information output unit 130 further includes an element called a chain-based output unit 132 .

[0099] FIG. 13 shows the functional configuration of the data processing method distribution device 100.

[0100] ***Explanation of Operation*** The data processing method and distribution method will be described with reference to FIG. In step S210, the attribute information acquisition unit 110 acquires the attribute information 199 of the target data. Step S210 is the same as step S110 in the first embodiment.

[0101] In step S220, the affinity score calculation unit 120 calculates an affinity score for the target data for each processing method. Step S220 is the same as step S120 in the first embodiment.

[0102] In step S230, the recommendation information output unit 130 outputs recommendation information of processing methods for the target data, based on the affinity score of each processing method. Step S230 is the same as step S130 in the first embodiment.

[0103] In step S240, the affinity score calculation unit 120 determines whether to consecutively recommend processing methods.

[0104] The chain recommendation of processing methods means recommending a processing method (next recommendation method) suitable for data processing of the output data (next target data) of the recommendation method.

[0105] That is, the affinity score calculation unit 120 determines whether to recommend a secondary processing method (next recommendation method) for further secondary processing of the processed data (output data) generated by the recommendation method in the previous step S230. When a secondary processing method, a tertiary processing method, . . . and repeated processing methods are recommended, steps S210 to S240 are repeatedly executed.

[0106] For example, the affinity score calculation unit 120 makes the determination as follows. The affinity score calculation unit 120 makes a determination according to an input from a user. If the number of times steps S210 to S240 are repeated has not reached the upper limit, the affinity score calculation unit 120 determines that processing methods should be consecutively recommended. The affinity score calculation unit 120 makes the determination based on the affinity score of the recommendation method in the immediately preceding step S230.

[0107] If it is determined that the processing method should be consecutively recommended, the output data of the recommended method in the immediately preceding step S230 is set as the next target data, and the process proceeds to step S210. In step S210, the attribute information acquiring unit 110 acquires attribute information 192 on the output data of the recommendation method from the processing method information 191 as attribute information 199 of the next target data. In step S220, the affinity score calculation unit 120 calculates, for each processing method, the affinity score of the processing method for the next target data as a new affinity score based on the attribute information 192 of the processing method and the attribute information 199 of the next target data. In step S230, the recommendation information output unit 130 selects a processing method to be the next recommended method from the multiple processing methods based on the new affinity scores of each of the multiple processing methods, and outputs recommendation information indicating the next recommended method.

[0108] If it is determined that the processing method is not to be consecutively recommended, the process ends.

[0109] ***Effects of the second embodiment*** According to the second embodiment, it is possible to recommend a plurality of processing methods that can be applied in a chain manner to the processing target data, rather than a single processing method. For example, the data processing method distribution device 100 can recommend a plurality of processing methods in a combination such as applying a primary processing method for shaping data and then applying a secondary processing method for analyzing data to certain processing target data. This increases the variety of processing methods available to users, enabling them to carry out advanced data processing.

[0110] Embodiment 3 The embodiment in which attribute information 199 obtained by analyzing the data body of the target data is used will be described with reference to Figs. 15 to 19, focusing mainly on the points different from the first embodiment.

[0111] ***Configuration Description*** The configuration of the data processing method distribution device 100 will be described with reference to FIG. The attribute information acquisition unit 110 further includes an element called a data information analysis unit 112 .

[0112] FIG. 16 shows the functional configuration of the data processing method distribution device 100.

[0113] ***Explanation of Operation*** The data processing method and distribution method will be described with reference to FIG. In step S310, the attribute information acquisition unit 110 acquires the attribute information 199 of the target data.

[0114] The attribute information 199 of the target data is acquired as follows. The attribute information acquisition unit 110 accepts at least a part of the target data as a data body, and analyzes the data body to generate attribute information 199 of the target data.

[0115] Step S310 will be described in detail with reference to FIG. In step S311, the user inputs to the data processing system distribution device 100 at least one of the data body of the target data and the attribute information of the target data. When the data body of the target data is input, the data information receiving unit 111 receives the input data body. When attribute information of the target data is input, the data information receiving unit 111 receives the input attribute information.

[0116] The data body of the target data is the content managed as the target data. The data body of the target data may be the entire target data, or may be a portion of the data (sample data) extracted from the target data.

[0117] In step S312, the data information accepting section 111 determines whether the data body has been accepted. If the data body is accepted, the process proceeds to step S313. If the data body is not accepted, the process ends.

[0118] In step S313, the data information analysis unit 112 analyzes the data body and generates attribute information.

[0119] The data body is parsed as follows: The data information analysis unit 112 determines the data format of the data body. For example, when an electronic file is received as the data body, the data information analysis unit 112 refers to the extension of the electronic file to identify the data format. The data information analysis unit 112 determines the data structure of the data body as follows. When the data format of the data body is structured data (or semi-structured data), the data information analysis unit 112 identifies the data structure of the data body. An example of structured data is table data. Specifically, the data information analysis unit 112 parses the data body in accordance with the data format of the data body, thereby identifying the data structure of the data body.

[0120] An example of analysis of the data body will be described with reference to FIG. The file name of the data body is "Power Measurement Values.csv". First, the data information analysis unit 112 refers to the file name extension "csv" of the data body and determines that the data format of the data body is a CSV file. Furthermore, the data information analysis unit 112 determines the major classification of the data body as table data, and determines the minor classification of the data body as CSV. Next, the data information analysis unit 112 parses the data body according to the CSV format. Then, the data information analysis unit 112 determines the column name and data type for each column of the data body. Column names can be identified by referencing the header row in the body of the data file. The data type can be determined by performing type determination on the values ​​of each column in the data body file. If multiple data types can be determined, the data information analyzer 112 selects the most restrictive data type. For example, if a column has values ​​such as 1, 2, ..., the data type is specified as numeric rather than string. Numeric data can always be converted to string data, but string data cannot always be converted to numeric data. Therefore, numbers are a more specific data type than strings.

[0121] Returning to FIG. 18, the explanation will be continued. After step S313, the process ends.

[0122] If only the data body is accepted in step S311, the attribute information generated in step S313 is used as attribute information 199 of the target data. When the data body and attribute information are accepted in step S311, a set of the attribute information accepted in step S311 and the attribute information generated in step S313 is used as attribute information 199 of the target data. If only the attribute information is accepted in step S311, the attribute information accepted in step S311 is used as the attribute information 199 of the target data.

[0123] Returning to FIG. 17, steps S320 and S330 will be described. Step S320 is the same as step S120 in the first embodiment. Step S330 is the same as step S130 in the first embodiment.

[0124] ***Effects of the Third Embodiment*** In the third embodiment, attribute information of the processing target data is acquired by receiving and analyzing the data body of the processing target data. This eliminates the need for the user to input all of the attribute information for the data to be processed. This also has the effect of saving the user the trouble of searching for a processing method. The data body input by the user may be a portion of the entire data, such as sample data.

[0125] ***Additional Note to the Third Embodiment*** The third embodiment may be implemented in combination with the second embodiment. That is, in the third embodiment, the data processing method distribution device 100 may recommend two or more processing methods that can be applied in a chain manner, as in the second embodiment.

[0126] ***Supplementary explanation of implementation form*** The hardware configuration of the data processing method distribution device 100 will be described with reference to FIG. The data processing method distribution device 100 includes a processing circuit 109 . The processing circuit 109 is hardware that realizes the attribute information acquisition unit 110, the affinity score calculation unit 120, and the recommendation information output unit . The processing circuitry 109 may be dedicated hardware, or may be a processor 101 that executes a program stored in the memory 102 .

[0127] When the processing circuitry 109 is dedicated hardware, the processing circuitry 109 may be, for example, a single circuit, a multiple circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof. ASIC is an abbreviation for Application Specific Integrated Circuit. FPGA is an abbreviation for Field Programmable Gate Array.

[0128] The data processing method distribution device 100 may include a plurality of processing circuits replacing the processing circuit 109 .

[0129] In the processing circuit 109, some functions may be realized by dedicated hardware, and the remaining functions may be realized by software or firmware.

[0130] In this way, the functions of the data processing method distribution device 100 can be realized by hardware, software, firmware, or a combination of these.

[0131] Each embodiment is an example of a preferred embodiment, and is not intended to limit the technical scope of the present disclosure. Each embodiment may be implemented in part or in combination with other embodiments. The procedures described using flow charts, etc. may be modified as appropriate.

[0132] The data processing method distribution device 100 may be realized by a plurality of devices (computers). In other words, the functions of the data processing method distribution device 100 may be distributed to multiple devices (computers), and the multiple devices (computers) may cooperate with each other through a network or the like to realize the functions of the data processing method distribution device 100. A cloud environment may also be used.

[0133] The "part" of each element of the data processing method distribution device 100 may be read as a "process," a "step," a "circuit," or a "circuitry." [Explanation of symbols]

[0134] 100 Data processing method distribution device, 101 Processor, 102 Memory, 103 Auxiliary storage device, 104 Input / output interface, 109 Processing circuit, 110 Attribute information acquisition unit, 111 Data information reception unit, 112 Data information analysis unit, 120 Affinity score calculation unit, 130 Recommendation information output unit, 131 Recommendation method output unit, 132 Chaining method output unit, 190 Memory unit, 191 Processing method information, 192 Attribute information, 193 Data format classification table, 199 Attribute information.

Claims

1. an attribute information acquisition unit for acquiring attribute information of target data to be processed; an affinity score calculation unit that refers to processing method information including attribute information of each of a plurality of processing methods, and calculates an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; a recommendation information output unit that selects a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputs recommendation information indicating the recommended method; A data processing distribution device comprising: The attribute information of the target data indicates a major classification and a minor classification of the target data as a data format of the target data, The attribute information for each processing method indicates a major classification of the input data and a minor classification of the input data as a data format of the input data for the processing method, The affinity score calculation unit calculates the affinity score for each processing method based on a comparison result between the major categories of the input data and the target data and a comparison result between the minor categories of the input data and the target data. Data processing distribution device.

2. an attribute information acquisition unit for acquiring attribute information of target data to be processed; an affinity score calculation unit that refers to processing method information including attribute information of each of a plurality of processing methods, and calculates an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; a recommendation information output unit that selects a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputs recommendation information indicating the recommended method; A data processing distribution device comprising: The attribute information of the target data indicates a data structure of the target data, The attribute information for each processing method indicates a data structure of input data for the processing method, The affinity score calculation unit calculates the affinity score for each processing method based on the data structure of the input data and the data structure of the target data. Data processing distribution device.

3. The attribute information of the target data indicates a plurality of data elements constituting the target data as the data structure of the target data, The attribute information for each processing method indicates a plurality of data elements constituting the input data as the data structure of the input data of the processing method, The affinity score calculation unit calculates the affinity score for each processing method based on the multiple data elements of the input data and the multiple data elements of the target data.

3. A data processing system distribution device according to claim 2.

4. an attribute information acquisition unit for acquiring attribute information of target data to be processed; an affinity score calculation unit that refers to processing method information including attribute information of each of a plurality of processing methods, and calculates an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; a recommendation information output unit that selects a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputs recommendation information indicating the recommended method; A data processing distribution device comprising: The attribute information for each processing method includes attribute information of output data of the processing method, The attribute information acquisition unit acquires the attribute information of the output data of the recommendation method from the processing method information as attribute information of the next target data; the affinity score calculation unit calculates, for each processing method, an affinity score of the processing method for the next target data as a new affinity score based on the attribute information of the processing method and the attribute information of the next target data; The recommendation information output unit selects a processing method to be a next recommended method from the plurality of processing methods based on the new affinity score for each of the plurality of processing methods, and outputs recommendation information indicating the next recommended method. Data processing distribution device.

5. A data processing distribution device, Obtain attribute information of the data to be processed, Refer to processing method information including attribute information of each of a plurality of processing methods, and calculate an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; A processing method to be a recommended method for the data processing of the target data is selected from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and recommendation information indicating the recommended method is output. This is a data processing and distribution method. The attribute information of the target data indicates a major classification and a minor classification of the target data as a data format of the target data, The attribute information for each processing method indicates a major classification of the input data and a minor classification of the input data as a data format of the input data for the processing method, The data processing method distribution device calculates the affinity score for each processing method based on a comparison result between the major categories of the input data and the target data and a comparison result between the minor categories of the input data and the target data. Data processing and distribution method.

6. A data processing distribution device, Obtain attribute information of the data to be processed, Refer to processing method information including attribute information of each of a plurality of processing methods, and calculate an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; A processing method to be a recommended method for the data processing of the target data is selected from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and recommendation information indicating the recommended method is output. This is a data processing and distribution method. The attribute information of the target data indicates a data structure of the target data, The attribute information for each processing method indicates a data structure of input data for the processing method, The data processing method distribution device calculates the affinity score for each processing method based on the data structure of the input data and the data structure of the target data. Data processing and distribution method.

7. A data processing distribution device, Obtain attribute information of the data to be processed, Refer to processing method information including attribute information of each of a plurality of processing methods, and calculate an affinity score of the processing method for the target data based on the attribute information of the processing method and the attribute information of the target data for each processing method; A processing method to be a recommended method for the data processing of the target data is selected from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and recommendation information indicating the recommended method is output. This is a data processing and distribution method. The attribute information for each processing method includes attribute information of output data of the processing method, The data processing method distribution device includes: acquiring the attribute information of the output data of the recommendation method from the processing method information as attribute information of the next target data; For each processing method, calculate an affinity score of the processing method for the next target data as a new affinity score based on the attribute information of the processing method and the attribute information of the next target data; Selecting a processing method to be a next recommended method from the plurality of processing methods based on the new affinity score for each of the plurality of processing methods, and outputting recommendation information indicating the next recommended method. Data processing and distribution method.

8. An attribute information acquisition process for acquiring attribute information of the target data to be processed; an affinity score calculation process for calculating an affinity score of a processing method for the target data based on the attribute information of the processing method and the attribute information of the target data, by referring to processing method information including attribute information of each of a plurality of processing methods; a recommendation information output process of selecting a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputting recommendation information indicating the recommended method; A data processing distribution program for causing a computer to execute the above. The attribute information of the target data indicates a major classification and a minor classification of the target data as a data format of the target data, The attribute information for each processing method indicates a major classification of the input data and a minor classification of the input data as a data format of the input data for the processing method, The affinity score calculation process calculates the affinity score for each processing method based on a comparison result between the major categories of the input data and the target data and a comparison result between the minor categories of the input data and the target data. Data processing method distribution program.

9. An attribute information acquisition process for acquiring attribute information of the target data to be processed; an affinity score calculation process for calculating an affinity score of a processing method for the target data based on the attribute information of the processing method and the attribute information of the target data, by referring to processing method information including attribute information of each of a plurality of processing methods; a recommendation information output process of selecting a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputting recommendation information indicating the recommended method; A data processing distribution program for causing a computer to execute the above. The attribute information of the target data indicates a data structure of the target data, The attribute information for each processing method indicates a data structure of input data for the processing method, The affinity score calculation process calculates the affinity score for each processing method based on the data structure of the input data and the data structure of the target data. Data processing method distribution program.

10. An attribute information acquisition process for acquiring attribute information of the target data to be processed; an affinity score calculation process for calculating an affinity score of a processing method for the target data based on the attribute information of the processing method and the attribute information of the target data, by referring to processing method information including attribute information of each of a plurality of processing methods; a recommendation information output process of selecting a processing method to be a recommended method for the data processing of the target data from the plurality of processing methods based on the affinity score of each of the plurality of processing methods, and outputting recommendation information indicating the recommended method; A data processing distribution program for causing a computer to execute the above. The attribute information for each processing method includes attribute information of output data of the processing method, The attribute information acquisition process acquires, from the processing method information, the attribute information of the output data of the recommendation method as attribute information of a next target data; The affinity score calculation process calculates, for each processing method, an affinity score of the processing method for the next target data as a new affinity score based on the attribute information of the processing method and the attribute information of the next target data; The recommendation information output process selects a processing method to be a next recommended method from the plurality of processing methods based on the new affinity score for each of the plurality of processing methods, and outputs recommendation information indicating the next recommended method. Data processing method distribution program.

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