JPH10232874A - Information processing know-how sharing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To share know-how with plural persons by providing a mechanism for storing know-how related to processing for extracting or analyzing data. SOLUTION: Only the data matched with the purpose of user are extracted from all the data stored in a full data storage device 1030 by a processing group 1100 and temporarily stored in an extracted data storage device 1040. In the case of extraction, data are extracted by preparing extraction conditions matched with the purpose of user while utilizing existent extraction conditions stored in an extraction condition storage device 1060. In a processing group 1200, the data extracted by the processing group 1100 are summed up and made into graph and the pattern, to which these data belong, is discriminated from the form of that graph and outputted. Next, the analyzed result is prepared. In a processing group 1500, the extraction conditions and graph/pattern know- how used for analysis are respectively stored in an extraction condition storage device 1070. At such a time, the data are collated with already stored conditions, deleted, worked or updated.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an information processing system and, more particularly, to a method for collecting and analyzing necessary data from an information source and for collecting the results of the collected analysis into a chart or a document. How to share with multiple people.

[0002]

2. Description of the Related Art The rapid development of computer technology has enabled various processing and detailed management of information. When a large amount of information is used in a computer system, such information is usually stored in a database system as data in a fixed format. A user rarely reads all of such a large amount of data, and often selects and reads only the data required by the user and uses it for analysis, aggregation, reporting, and the like. When the database system is a relational database, there is a method of selecting such data using a SELECT statement in the SQL language. The specification of the SQL language is standardized by JIS X3005.

[0003] In addition, the analysis work, the aggregation work,
Reporting work, etc. depends on the know-how owned by the operator
It may affect not only the work efficiency but also the result.

[0004] Therefore, it is said that a mechanism for presenting appropriate know-how to the user is required for such a task. As a known technique having a feature that know-how can be searched, there is a technique described in JP-A-8-63466. Although this technology is intended to be used for document creation, the core of the technology is the method of presenting know-how, and the user can preferentially search for and display the necessary know-how as desired and improve efficiency and efficiency. High quality of the results has been achieved.

[0005]

However, the SQL SE
The LECT sentence merely indicates the data extraction criteria, and does not include information that indirectly indicates the purpose of extraction or the purpose of who created it, and therefore cannot be said to be know-how.

[0006] Further, regarding the use of know-how described in Japanese Patent Application Laid-Open No. 8-63466, there is no mechanism for newly storing know-how, and only the use of know-how prepared in advance is used, so that improvement of know-how cannot be expected. .

An object of the present invention is to enable operations relating to data extraction and analysis to be used as know-how,
It is an object of the present invention to provide a method that can utilize know-how that reflects the experience of a skilled person.

[0008]

According to the present invention, there is provided a computer system having an input device, an output device, and a storage device, wherein the storage device includes an all data storage device, an extracted data storage device, and a shared data storage device. A method of sharing data extraction conditions and know-how with a plurality of persons using the computer system, comprising: (a) storing data in all data storage devices. From the data, extracted data that meets the extraction conditions created by the user, (b) the extracted data is processed and output based on the know-how created by the user, (c) the step (a) and (b) or only (b) is repeated, and (d) the extraction conditions stored in the extraction condition storage device based on the extraction conditions and know-how used in the steps (a) and (b). And know-how And updates the stored device to know.

The step (a) comprises the steps of: (a-1) searching for an existing extraction condition stored in the extraction condition storage device based on a key input by a user; Creating the desired extraction condition by editing the extracted extraction condition, and (a-3) extracting data matching the created extraction condition from all data storage devices and storing the data in the extracted data storage device. It is characterized by.

In the step (a-1), when the key input by the user is information relating to the creator of the extraction condition, (a-1-1) the information of the sharer information storage device is stored by the key. To obtain the ID number of the creator, and (a-1-
2) Searching the extraction condition stored in the extraction condition storage device using the ID number as a key.

In the step (b), (b-1) the existing know-how stored in the know-how storage device is searched based on a key input by the user, and (b-2) the obtained know-how is obtained by the search. A new know-how is created by editing the know-how, and (b-3) the extracted data is processed and output based on the created know-how.

Each extraction condition stored in the extraction condition storage device and the know-how stored in the know-how storage device include, as information, the number of times of reference used to create a new extraction condition or know-how. Also includes the creation date and time as information, in the step (d), the current date and time is read from the computer system, the reference frequency is calculated based on the reference count, the creation date and the current date and time, and based on the magnitude of the reference frequency. Or not to delete the extraction condition or the know-how.

Further, the know-how includes, as information, a graph axis and a parameter for graphing data, and a pattern dividing condition for patterning a data group, and performs processing of the extracted data on the graph axis. The method is characterized in that the analysis is performed based on the parameters and the pattern classification conditions, and the result is output, thereby assisting the user in analyzing the data.

[0014]

Embodiments of the present invention will be described below in detail.

FIG. 1 is an explanatory diagram showing the configuration of an embodiment of the present invention. In the present embodiment, a method will be described in which the present invention supports the task of an analyst creating a graph based on a large amount of data and analyzing its pattern tendency.

The apparatus used in this embodiment is an input device 101
0, output device 1020, all data storage device 1030, extracted data storage device 1040, sharer information storage device 105
0, an extraction condition storage device 1060, and a know-how storage device 1070. Using these devices, 1 in FIG.
The processing of the steps from 110 to 1520 is performed by a computer. It is assumed that the computer has a central processing unit, a memory, an input / output port, and an internal clock, and the operating system manages the date and time.

First, a general flow of the processing will be described. First, the processing group 1100 extracts only data suitable for the user's purpose from all data stored in all the data storage devices 1030 and temporarily stores the extracted data in the extracted data storage device 1040. When extracting, the extraction condition storage device 1060
Using the existing extraction conditions stored in, an extraction condition suitable for the user's purpose is created, and data is extracted. The processing group 1100 includes steps 1110 to 1150.

In the processing group 1200, the data extracted in the processing group 1100 is tabulated and graphed, and the pattern to which the pattern belongs is determined and output.
For selection of axes for graphing and classification criteria for patterning, an existing one is read from the know-how storage device 1070 and is changed and used so as to meet the user's purpose. The processing group 1200 includes steps 1210
It consists of step 1250.

In step 1300, the user selects whether or not to repeat, and if so, whether it is partial or full repetition. In the case of partial repetition, the process returns to the processing group 1200, and new graphing / patterning know-how is created to obtain a result. In the case of the whole repetition, the process returns to the processing group 1100, and the process is again performed from the data extraction.

Step 1400 is an analysis result creation step. The analysis result is created by the user repeating the processing up to step 1300 as necessary, and freely making a judgment from the obtained plurality of pattern results. However, this analysis result includes analyst information and the purpose of analysis in addition to the analysis result itself.

In the processing group 1500, the extraction conditions and the graphing / patterning know-how used for the analysis are stored in the extraction condition storage device 1060 and the know-how storage device 1070, respectively. At this time, deletion, processing, and updating, which will be described later, are performed in comparison with those already stored so that not only the data is stored as it is, but also better extraction conditions and know-how can be provided later. The processing group 1500 proceeds to step 15
10 and step 1520.

Next, the data structure will be described. The data stored in the all data storage device 1030 includes a plurality of item values and characters per data, and has a format as shown in FIG. 2, for example. The example shown in FIG. 2 represents a shopping situation of a customer in a certain store. In this example, data No. , Customer name, date, product, and amount are items. The data stored in the extracted data storage device 1040 has the same format as the data stored in the entire data storage device 1030. However, since the data is extracted from the data stored in all data storage devices, the set of data stored in the extracted data storage device is a subset of the set of data stored in all data storage devices. .

Analyst information is stored in the sharer information storage device 1050, and its format is as shown in FIG. Each analyst data includes an analyst ID, a name, an affiliation, and a task in charge.

The analyst ID is assigned to each analyst, and the same analyst ID is not assigned to different analysts.

The extraction condition storage device 1060 stores extraction condition data. Fig. 4 shows the data format of the extraction condition data.
The condition NO. , Analyst ID, analysis purpose, extraction condition, reference count, parent condition, and creation date and time. The parent condition refers to the condition No. of the extraction condition from which an existing extraction condition is read out when a certain extraction condition is to be created, and a part of the extraction condition is modified. Refers to In the case of a newly created extraction condition, there is no value in the parent condition.

The know-how storage device 1070 displays a graph
Patterning know-how (hereinafter simply referred to as know-how)
Is stored. The data format of the know-how has a structure as shown in FIG. , Analyst ID, analysis purpose, graph axis, parameter, pattern classification condition, number of references, parent know-how, and creation date and time. With parent know-how,
When attempting to create a certain know-how, the existing graphing / patterning know-how is read out, and when a part of the know-how is created, the know-how No. of the know-how of the original is obtained. Refers to For newly created know-how,
There is no value in parent know-how. The example shown in FIG. 5 has no parent know-how.

Next, the processing groups 1100, 1200, 150
The steps for configuring each of 0 will be described.
The processing group 1100 includes steps 1110 to 115
It consists of five steps of 0.

Step 1110 comprises the processing flow shown in FIG. In sub-step 1111, the input device 1010
, The user inputs whether to use an existing extraction condition. The process branches at sub-step 1112, and proceeds to sub-step 1113 if an existing extraction condition is used, or to sub-step 1114 otherwise. Sub-step 11
13, a flag u indicating whether to use the existing extraction condition
Is set to 1. In sub-step 1114, the flag
Set u to 0. In sub-step 1115, a key for searching for an existing extraction condition is input. As a key for searching the extraction condition, at least one of the name of the analyst, the affiliation of the analyst, the duties of the analyst, and the analysis purpose is specified.

Step 1120 comprises the processing flow shown in FIG. In sub-step 1121, sub-step 11
Branching is performed according to the value of the flag u set in step 13. When the value of u is 1, the process proceeds to sub-step 1122 to use an existing extraction condition.

When the value of the flag u is 0, the process returns to the main routine. In sub-step 1122, the key for the extraction condition search input in sub-step 1115 is checked and branched. If the key is only for analysis, the process jumps to sub-step 1124. If the key includes other types of keys, that is, the name of the analyst, the affiliation of the analyst, and the duties of the analyst, the sub-step 112 is performed.
Proceed to 3. In sub-step 1123, at least one type of data of the analyst's name, the analyst's affiliation, and the duties of the analyst already input in sub-step 1115 is stored as a key in sharer information storage device 1050. Search for existing analyst information. When a key is hit by the search, the value of the analyst ID in the analyst information is read into the computer. When there is a plurality of hit analyst information, all the analyst IDs are read. Sub-step 11
At 24, the extraction condition data stored in the extraction condition storage device 1060 is searched using at least one of the data of the analysis purpose already input in the sub-step 1115 or the analyst ID obtained in the sub-step 1123 as a key.

When a key is hit by the search, the value of the extraction condition of the extraction condition data is read into the computer. If there is more than one extraction condition data that hits, read all of them.

Step 1130 comprises the processing flow shown in FIG. In sub-step 1131, it is determined whether or not the read existing extraction condition exists, and the process branches. If there is no extraction condition that hits the search key in sub-step 1124, or if the existing extraction condition use flag u is 0 and the existing extraction condition is not read from the beginning, the process proceeds to sub-step 1132, where one or more existing extraction conditions are read. When the extraction condition is read, the process proceeds to sub-step 1133. In sub-step 1132, an extraction condition is created by a user directly inputting from the input device 1010. The extraction conditions created in this sub-step will be used in subsequent processing. In sub-step 1133, all of the extracted extraction conditions are displayed, and one of the extraction conditions that the user considers closest to the purpose is selected by specifying from input device 1010. In sub-step 1134, the user
Through 010, a new extraction condition is created by editing the extraction condition selected in the sub-step 1133, and is determined as an extraction condition to be used in later processing.

In step 1140, the data stored in all data storage devices 1030 is searched according to the extraction condition determined in step 1130, and only the data meeting the extraction condition is extracted. For example, data as shown in FIG. 2 is stored in all data storage devices 1030 and “date: 7 /
If extraction is performed under the extraction condition of “14 or later”, extracted data as shown in FIG. 9 is obtained. In step 1150, the extracted data extracted in step 1140 is temporarily stored in the extracted data storage device 1040. At this time, as shown in FIG. 90
01 is assigned and stored. Extracted data No. Is different from the extracted data No. for the extracted data extracted under different extraction conditions. Is assigned, and is not duplicated in a plurality of extracted data.

The processing group 1200 includes seven steps from step 1210 to step 1270.

Step 1210 comprises the processing flow shown in FIG. In sub-step 1211, the user selects from the input device 1010 which extracted data is to be used for analysis and determines the extracted data No. Enter If nothing is entered, the extracted data No. ,
Or the extracted data No. of the extracted data extracted immediately before.
Is used as a default value. In sub-step 1212, the user inputs from input device 1010 whether or not to use existing know-how. In the sub-step 1213, the process branches. If the existing know-how is not used, the sub-step 1214 is performed.
If not, go to sub-step 1215. At sub-step 1214, 0 is set to a flag n indicating whether to use existing know-how. Sub-step 121
At 5, the flag n is set to 1. Sub-step 12
In step 16, a key for searching for existing know-how is input. The purpose of analysis is specified as a key for searching for know-how.

In sub-step 1217, at least one key of the analysis purpose or the analyst ID used for the extraction condition search in sub-step 1124 is added as a key for know-how search.

In step 1220, sub-step 12
No. 11 of the extracted data No. Is read from the extracted data storage device 1040 into the computer.

Step 1230 comprises the processing flow shown in FIG. In sub-step 1231, the process branches depending on the value of the existing know-how use flag n. When the value of n is 1, the sub-step 12 is used to utilize the existing know-how.
Go to 32. When the value of the flag n is 0, the process returns to the main routine. In sub-step 1124, the know-how stored in know-how storage device 1060 is searched using the key of at least one of the analysis purpose and the analyst ID specified in step 1210. When a key is hit by the search, the graph axis, parameters, and pattern classification conditions in the data of the know-how are read into the computer. If there is more than one know-how, read them all.

Step 1240 comprises the processing flow shown in FIG. In sub-step 1241, it is determined whether or not the read know-how exists, and the process branches. If there is no know-how that hits the search key in sub-step 1232, and if the existing know-how use flag n is 0 and the existing extraction condition has not been read from the beginning, the process proceeds to sub-step 1242, where one or more existing know-how is If it has been read, the flow advances to substep 1243. In sub-step 1242, know-how is created by the user directly inputting from input device 1010. The know-how created in this sub-step is used in subsequent processing. In sub-step 1243, all of the read know-how is displayed, and one of the know-hows that the user considers closest to the purpose is selected by designating from input device 1010. In sub-step 1244, the user
Through step 10, new know-how is created by editing the know-how selected in the sub-step 1133, and determined as know-how to be used in later processing.

In steps 1250 and 1260, the extracted data read in step 1220 is graphed and patterned using the know-how determined in step 1240. For example, in the extracted data 9000 shown in FIG. 9, the graph axis is “date, amount meter”, the parameter is “product”, and the pattern classification condition is “A: maximum amount> 1000”.
Assuming that the data is graphed with know-how including information of “0, B: other”, a graph of the result is shown in FIG.
00. Further, when patterning is performed using this know-how, “product A → pattern A, product B → pattern A, product C → pattern B” is obtained.

In step 1270, the result of such a pattern is output to the output device 1020. The user sees this output, grasps trends in the data, and uses the data as analysis material. If it is desired to know the tendency of the data from another point of view, the repetition processing is designated in step 1300, and step 11
It is possible to return to step 10 or step 1210.

When the user determines that sufficient analytical material has been obtained, the user can input "end" in step 1300 to end the iterative process. After the end of the repetitive processing, the process proceeds to step 1400, and the user freely creates an analysis result.
At this time, the analyst ID and the purpose of analysis are input together with the analysis result, and are stored in the memory of the computer.

The processing group 1500 is composed of two steps, step 1510 and step 1520. In these steps, maintenance of extraction conditions and know-how is performed, and corresponds to post-processing after analysis and pre-processing for the next analysis.

Step 1510 comprises the processing flow shown in FIG. In sub-step 1511, step 14
In the extraction conditions used up to 00, condition No. , Analyst I
D, the purpose of analysis, the number of times of reference, the parent condition, and the date and time of creation are added to form a format such as 4000 shown in FIG. Condition N
o. Is assigned a number that does not overlap with other extraction conditions.

The data entered in step 1400 are substituted for the analyst ID and the analysis purpose. 1 is substituted for the reference count. When the extraction condition is created in step 1130 and edited using the existing extraction condition, the condition No. of the original extraction condition is used as the parent condition. Is assigned, and a new condition is created without using the existing extraction condition, and there is no parent condition. For the creation date and time, an inquiry command for the current date is issued to the operating system, and the return value is substituted.

In sub-step 1512, the extraction conditions in the data format prepared in sub-step 1511 are stored in extraction condition storage device 1060.

In sub-step 1513, the parent condition that was the base, that is, the number assigned to the parent condition item in sub-step 1511 is the condition No. , And the value of the number of times of reference is increased by one.

In sub-step 1514, conditions having this parent condition as a parent, that is, extraction conditions that are siblings of the extraction conditions stored in sub-step 1512, are retrieved and collected from extraction condition storage device 1060.

In sub-step 1515, each extraction condition collected in sub-step 1514 and sub-step 1512
Compare with the extraction conditions stored in step (1), and if there are similar ones, integrate them. Specifically, for example, if the extraction conditions are “date: 7/15 or later” and “date: 7/30 or later”, one of them is assumed to be similar and one of them is added. The value is a new value, the creation date is the new date and time, and the others inherit the selected value.

In sub-step 1516, the reference frequency is calculated for all the extraction conditions stored in the extraction condition storage device 1060, and if there is an extraction condition smaller than the value specified in advance, it is deleted. The reference frequency is calculated according to the following formula: reference frequency = number of references / (current date and time-creation date and time).

Step 1520 comprises the processing flow shown in FIG. In sub-step 1521, step 14
Know-how (graph axes, parameters,
The know-how No. , Analyst ID,
Information such as the purpose of analysis, the number of times of reference, parent know-how, and the date and time of creation is added to form a format such as 5000 shown in FIG. Know-how No. Is assigned a number that does not overlap with other know-how. Step 14 is used for the analyst ID and the analysis purpose, respectively.
Substitute the value entered in 00. 1 is substituted for the reference count. If the existing know-how is edited when the know-how is created in step 1240, the know-how No. of the original know-how is given to the parent know-how. Is substituted and the new know-how is created without using the existing know-how, there is no parent know-how. For the creation date and time, an inquiry command for the current date is issued to the operating system, and the return value is substituted.

In sub-step 1522, the know-how in the data format prepared in sub-step 1521 is stored in know-how storage device 1070.

In sub-step 1523, the know-how No. is assigned to the original parent know-how, that is, the number assigned to the item of parent know-how in sub-step 1521. Is accessed, and the value of the number of times of reference is increased by one.

In sub-step 1524, know-how having the same analysis purpose as the know-how stored in sub-step 1522 is retrieved from know-how storage device 1070 and collected.

In sub-step 1525, the reference frequency of each know-how collected in sub-step 1524 is calculated,
Only the upper-level know-how specified in advance is left, and the other know-how is deleted. The calculation of the reference frequency is the same as the calculation of the reference frequency of the extraction condition in sub-step 1516.

In sub-step 1527, the reference frequency is calculated for all the know-how stored in know-how storage device 1070, and if there is any know-how smaller than the value specified in advance, it is deleted.

The above is the description of the embodiment according to the present invention.
In this embodiment, as shown in FIG. 1, all data is described as being stored in one place in all data storage devices 1030, but the present invention is not limited to this.
All data is distributed and divided into multiple parts,
These may be connected to a computer via a network.

Further, in this embodiment, only the result after patterning is output, but the present invention is not limited to this, and data extraction, graphing / patterning may be performed as necessary. The transition can be output for each process and presented to the user as analysis support material.

Although the graphing and patterning criterion used in this embodiment is simple, using a more complicated criterion can be performed without any change in the method according to the present invention.

Further, in the present embodiment, a graphing standard and a patterning standard are used as know-how to assist the user in analyzing data. However, the present invention is not limited to this. As a standard, any method related to the analysis work may be used.

The method according to the present invention can be stored as a program in a storage medium readable and writable by a computer, such as a hard disk device or a floppy disk device, and can be executed by reading the program into a memory in the computer. .

[0062]

As described above, according to the information processing know-how sharing method of the present invention, there is provided a mechanism for storing know-how relating to data extraction and analysis processing.
It is possible to share know-how among multiple people, that is, one person's know-how can be used by another person.

The know-how itself has information such as the number of times of reference and the purpose of analysis, and can be used for updating or deleting existing know-how. Therefore, it is possible to leave high-quality know-how and to obtain a disorderly amount of know-how. It also has the effect of preventing enormous growth.

[Brief description of the drawings]

FIG. 1 is a diagram showing a configuration of an embodiment according to the present invention.

FIG. 2 is a diagram showing an example of data stored in all data storage devices.

FIG. 3 is a diagram showing a structure of data stored in a sharer information storage device.

FIG. 4 is a diagram showing a data structure of an extraction condition stored in an extraction condition storage device.

FIG. 5 is a diagram showing a data structure of know-how information stored in a know-how storage device.

FIG. 6 is a flowchart showing a processing flow of a data extraction method input routine.

FIG. 7 is a flowchart showing a processing flow of an existing extraction condition reading routine.

FIG. 8 is a flowchart showing a processing flow of a data extraction condition creation routine.

FIG. 9 is a view showing an example of extracted data.

FIG. 10 is a flowchart showing a processing flow of a know-how creation method input routine.

FIG. 11 is a flowchart showing a processing flow of an existing know-how reading routine.

FIG. 12 is a flowchart showing a processing flow of a know-how creation routine.

FIG. 13 is a diagram showing an example of graphing.

FIG. 14 is a flowchart showing a processing flow of an extraction condition processing update routine.

FIG. 15 is a flowchart showing a processing flow of a know-how processing update routine.

[Description of sign]

1010: input device, 1020: output device, 1030 ...
All data storage devices, 1040 ... extraction data storage device, 1
050: Sharer information storage device, 1060: Extraction condition storage device, 1070: Know-how storage device, 1100: Only data suitable for the user's purpose is extracted from all data stored in all data storage devices, and extracted data Processing group for temporarily storing data in the storage device, 1200 ... Processing group for collecting and graphing the extracted data, determining what pattern it belongs to from the graph shape, and outputting it, 1300 ...
Steps of selecting whether or not to repeat, and if so, whether partial or full repetition, by a user's input, 1400.
Analysis result creation step 1500: Step of processing and updating the extraction conditions and know-how used for analysis,

Claims (7)

[Claims] 1. A computer system having an input device, an output device, and a storage device, wherein the storage device is an all data storage device,
An extraction data storage device, a sharer information storage device, an extraction condition storage device, a know-how storage device, and a method for sharing data extraction conditions and know-how with a plurality of persons using the computer system, wherein (a) From the data stored in all data storage devices, extract data that meets the extraction conditions created by the user, and (b) process and output the extracted data based on the know-how created by the user. (C) repeating the steps (a) and (b), or only (b), and (d) based on the extraction conditions and know-how used in the steps (a) and (b), an extraction condition storage device. And updating the extraction conditions stored in the storage device and the know-how stored in the know-how storage device. 2. The information processing know-how sharing method according to claim 1, wherein the step (a) comprises: (a-1) converting an existing extraction condition stored in the extraction condition storage device into a key input by a user. Search based on (a-2) to create the desired extraction condition by editing the extraction condition obtained by the search,
-3) An information processing know-how sharing method characterized in that data conforming to the created extraction condition is extracted from all data storage devices and stored in the extracted data storage device. 3. The information processing know-how sharing method according to claim 2, wherein in the step (a-1), when the key input by the user is information on the creator of the extraction condition,
-1-1) Using the key, search the information of the sharer information storage device to obtain the ID number of the creator, and (a-1-2)
An information processing know-how sharing method, wherein an extraction condition stored in the extraction condition storage device is searched using a D number as a key. 4. The information processing know-how sharing method according to claim 1, wherein in the step (b), (b-1) existing know-how stored in the know-how storage device is converted based on a key input by a user. Search, and (b-2) create new know-how by editing the know-how obtained by the search;
-3) An information processing know-how sharing method characterized by processing and outputting the extracted data based on the created know-how. 5. The information processing know-how sharing method according to claim 1, wherein each of the extraction conditions stored in the extraction condition storage device and the know-how stored in the know-how storage device are new extraction conditions or know-how. Including the number of references used to make as information, further including the creation date and time as information, in the step (d), read the current date and time from the computer system, the reference times and the creation date and time and the current date and time A method for sharing information processing know-how, comprising: calculating a reference frequency based on the reference frequency; and determining whether to delete the extraction condition or the know-how based on the magnitude of the reference frequency. 6. The information processing know-how sharing method according to claim 1, wherein said know-how includes a graph axis and a parameter for graphing data, and a pattern dividing condition for patterning a data group. A know-how sharing method characterized by including as information, processing the extracted data based on the graph axis, the parameter, and the pattern classification condition, and outputting a result to assist a user in data analysis. 7. A storage medium storing the information processing know-how sharing method according to claim 1 as a program.