JP2007179393A - Apparatus and method for analyzing intergroup communication - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To properly measure communications within an organization by efficiently understanding the relationships among users and groups in the organization, and particularly dealing with sectionalism that poses a problem in any organization. <P>SOLUTION: A human position detecting part 14 detects position information. A user relationship quantity calculating part 11 calculates the quantity of relationships among subject people or groups. An intergroup communication index calculating part 12 calculates an intergroup communication index. A result display part 13 displays the distribution of the characteristics of subjects on the two-axis coordinates of, e.g., job groups and groups to which the subjects belong. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an inter-group exchange analysis technique for analyzing exchanges between groups, and is particularly suitable for analyzing an organization from the viewpoint of organizational division.

  Sectionalism (also called divisionalization or specialized differentiation) occurs when operating an organization such as a company. In this case, the profit and survival of its own department will be aimed, and the distribution of useful information will be hindered. It is desirable to grasp the form of the existence of the division and take a prescription for the activation of the organization.

  By the way, it is conceivable to use an RFID tag or an infrared ID badge to grasp the activation of the organization or the like using the person position information indicating who was where and when. However, in such an organization analysis technique, when there are a large number of users and groups, it is difficult to efficiently analyze the relationship between the users and groups.

  It is desirable to grasp the division of the organization using the person location information.

  The prior arts related to this invention include the following.

  Patent Document 1 proposes to measure a behavior pattern of a large number of people in a building or site with a simple and easy-to-add facility. Patent Document 2 proposes summarizing from the probabilities because displaying all nodes and arcs when displaying a graph with actions as nodes and action transitions as arcs. These are systems that use human location information, but do not target organizational behavior.

  Further, Patent Document 3 proposes to collect location information from a terminal carried by a migrant, efficiently analyze it, and perform navigation. This analyzes the migratory behavior from the human position information, but does not target the behavior of the organization.

The above-described prior art analyzes the behavior of “individual” from the position information of a person using a sensor or a reader. However, it is not analyzed as an organization (group) composed of a plurality (many) of people. Further, in an organization analysis technique in which there are a large number of users and groups, it is not possible to effectively analyze their relationship.
Japanese Patent Laid-Open No. 2001-92985 JP 2004-102681 A Japanese Patent Application Laid-Open No. 2004-102697

  The present invention has been made in consideration of the above circumstances. For example, the dependency relationship in the organization is detected from the person position information or the like, and the relationship between the users and groups in the organization is efficiently grasped. In order to deal with the phenomenon (departmentalization) in which communication is divided by the framework on the organizational chart, which is cited as a major problem in Japan, it is the most suitable exchange analysis between groups to appropriately measure organizational communication The purpose is to provide technology.

  In the organization analysis technique using human position information, when there are a large number of users and groups, it is difficult to analyze the relationship between the users and groups in a short time with a low load. In the present invention, the amount of relationship is measured with respect to the inside and outside of a group in which the relationship between users is given in advance, and whether or not the organization is divided into departments is associated with a simple index. As a result, it is possible to efficiently grasp a large amount of data from various viewpoints, and it is possible to analyze a division tendency of an organization by combining various viewpoints. Furthermore, by adopting a variation coefficient of the relationship with other departments, it is possible to visualize the relation between departments, for example, the relative position between departments without drawing a network diagram.

  In a specific configuration example of the present invention, in order to achieve the above-described object, the relation amount between users is estimated based on position information indicating that the users existed simultaneously in the same place. Efficiently grasp large amounts of data by measuring the amount of relationship between users and group information given in advance, and mapping whether the organization is departmental or not, based on the relationship information between users and group information. It becomes possible to do. For example, it is possible to grasp the relative position between departments from the viewpoint of whether each department is inward or outward without drawing a network diagram. Also, by mapping the variation coefficient of the relationship with other departments at the same time, it becomes possible to grasp the relationship with other departments.

  The present invention will be further described. In order to facilitate understanding, corresponding portions of the embodiments are added with corresponding symbols. This is intended to facilitate understanding, and it goes without saying that the present invention is not limited to the embodiments.

  According to one aspect of the present invention, in order to achieve the above-mentioned object, the group exchange analysis apparatus: a group information holding means (organization information database 16) for holding information about a group to which the subject belongs; Group exchange index calculation means (inter-group exchange index calculation unit 12) is provided that calculates a group exchange index that represents the degree to which the target person interacts with other groups based on the information indicating the activity and the information on the group.

  The group exchange analysis device may be any device that analyzes group exchange, and may be a tissue analysis device or system, or any other appropriate device or system.

  The group AC analyzer is typically composed of a plurality of computers and sensors distributed over a network, but may be composed of a stand-alone computer that processes AC information from the detection means.

  In this configuration, it is possible to grasp the exchange between groups simply and accurately from the exchange information and use it for organizational analysis and the like.

  In this configuration, the information representing the activity of the subject is typically the behavior history of the subject, and the behavior history of the subject is more typically the time and It is person position information including the position.

  The group AC analyzer itself may have detection means for detecting the person position information, and may acquire person position information from the outside.

  The information representing the activity of the target person may be communication history information of the target person.

  The group exchange analysis apparatus further includes an output means (result display unit 13) for outputting an analysis result based on the group exchange index, and the output means is obtained by grouping subjects on the basis of one criterion. The target person may be displayed in two-dimensional coordinates on the basis of the group exchange index and the group exchange index obtained by grouping the target persons according to other criteria. Of course, the group exchange may be analyzed not only with two coordinate axes but also with more coordinate axes.

  Further, in addition to the group exchange index of the target person, an inter-group deviation index calculating means (inter-department deviation index calculating unit 17) for calculating an inter-group deviation index indicating variation between groups may be further provided.

  In this case, an output means for outputting an analysis result based on the group exchange index and the intergroup deviation index is further provided, and the output means uses the group exchange index and the intergroup deviation index as a reference for the target person. You may make it display on a dimensional coordinate.

  Further, the exchange index calculation means may calculate the group exchange index using at least one of the target person and the group as a calculation unit, and may similarly calculate the inter-group deviation index.

  The exchange index calculation means may reduce the degree of influence of the information representing the target person's activity on the group exchange index according to the number of collaborators of the activity. The degree of influence on the inter-group deviation index of the information indicating “” may be reduced according to the number of collaborators of the activity.

  Further, the exchange index calculation means typically determines that the target persons are located in the same place based on the person position information, and calculates the inter-group exchange index based on the determination result. May be.

  Further, the detecting means for detecting the human position information specifically determines where the target person is based on the equipment carried by the target person. Devices carried by the subject are RFID tags, infrared ID badges, mobile communication mobile stations, GPS, and the like.

  Further, the communication history information is information on the sender and destination of an electronic message. Specifically, the electronic message uses electronic mail (including a mailing list), electronic bulletin board communication, instant message communication, and the web. Although it is at least one of communication, it is not limited to this.

  The present invention can be realized not only as an apparatus or a system but also as a method. Of course, a part of the invention can be configured as software. Of course, software products used to cause a computer to execute such software are also included in the technical scope of the present invention.

  These and other aspects of the invention are set forth in the appended claims and will be described in detail below with reference to examples.

  According to the present invention, it is possible to efficiently grasp the relationship between users and groups in an organization by paying attention to the dependency relationship between users.

  In a specific configuration, the amount of relationship between users can be measured in relation to internal and external groups given in advance, and whether or not the organization is interacting between groups can be analyzed with a simple index, and multiple viewpoints ( The characteristics of each user can be measured by comparing the group-to-group exchange index from the department and position.

  Also, in other specific configurations, by comparing the inter-group exchange index of a group with the deviation index between other groups and the relationship between other groups, it is possible to simultaneously grasp the relationship with other groups. .

  Examples of the present invention will be described below.

  FIG. 1 shows the whole organization analysis system (inter-group AC analyzer) of Embodiment 1 of the present invention. Although the organization analysis system is shown as a functional block in the figure, it is typically implemented using a computer or other equipment connected to a network, but may be realized by a stand-alone computer.

  1, the organization analysis system includes an analysis instruction unit 10, a user relation amount calculation unit 11, an intergroup exchange index calculation unit 12, a result display unit 13, a human position detection unit 14, a human position database 15, an organization information database 16, and the like. It is comprised including. The person position detection unit 14 is typically a person position detection server, acquires person position information described later from detection information from the position sensor 141 arranged in each department, and registers the person position information in the person position database 15. The position sensor 141 detects, for example, the RFID tag 142 carried by the position detection target person.

  The analysis instruction unit 10 basically inputs a target organization, a totaling period, a department definition, a relationship definition, and the like. For example, as the definition of the relationship, it is specified that only the relationship in all areas / meeting rooms / open spaces / self seats / specific areas is extracted, or the sharing time exceeding the threshold is set as a target. . Examples of grouping include departments / sections / clerks (whether or not the same department), job levels (whether or not the same job), and home floors (whether or not the same floor) ), And can be divided into groups based on the business in charge and the industry in charge. The standard is not limited to this. Also, these criteria can be grouped in various granularities.

  Based on the input from the analysis instruction unit 10, various modes of analysis can be requested from the system.

  The person position database 15 shows an example of person position information (information indicating who is where when and where there are various modes) as shown in FIG. In this example, the human position information represents the duration from when the position sensor 141 starts detecting the RFID tag 142 until it stops detecting, and the record ID, user ID (tag ID), area ID, start time, end Includes time. For example, when the position sensor 141 is set in a certain room, the human position information represents a duration time until the subject enters and exits.

  The organization information database 16 is, for example, as shown in FIG. 3 and includes record ID, user ID, section ID, job floor ID, home floor ID, industry ID, etc. for each target person. A section ID, a job floor ID, a home floor ID, a business type ID, and the like are grouping standards. For example, grouping is performed for each section. The affiliation section is described so that hierarchical grouping is possible. Hierarchical information such as department-section-relationship may be provided separately.

  The user relation amount calculation unit 11 calculates a relation amount between the target persons based on the person position information. Basically, user sets detected at the same place at the same time and for a certain time or more are defined as having a relationship with each other. An aggregated amount of the relations over a certain period is defined as a relation quantity between users. Aggregation by unique number of people, number of times, accumulated time and frequency is possible. The relationship amount can be defined and calculated in various ways, for example, the place (type) where the relationship is established may be limited (for example, the room is ignored), and the threshold value of the duration for which the relationship is established depending on the location It may be set. Further, correction by the number of persons having a relationship at the same time may be performed (for example, the effect is reduced as the number increases).

  In this example, the relational quantity is finally expressed as a binary value of presence / absence, and the number of times and time are set to a binary value with a predetermined threshold value.

  In this example, for a network of target users, the time spent in the same place is accumulated to create a relationship quantity adjacency matrix. First, a person-to-person adjacency matrix is given as an example with a network node as a person.

  To create the person-to-person relationship quantity adjacency matrix, the person-to-person relation quantity is calculated from the number of times, accumulation, time, and frequency detected at the same place at the same time. In this example, the sharing time is the related quantity.

  For example, as shown in FIG. 4, when it is detected that users A1, A2, A3 belonging to group A and users B1, B2 belonging to group B have been simultaneously for 10 minutes, the adjacency matrix between the users is as shown in FIG. Become. All other than the diagonal cell is “10 minutes”.

  In this example, since it is only necessary to know which group the user interacts with, as shown in FIG. 6, it is possible to pre-aggregate in person-to-group from the adjacency matrix in FIG. 5.

  In order to create a person-to-group relationship quantity adjacency matrix, aggregation is performed in groups from a person-to-person matrix, and at this time, an average value, a median value, a sum, etc. are used.

  Make corrections at the same time (such as meetings). For example, in the case of a person-to-group, “how much relationship you have with that group” is, so when you meet two people in the same group or one person, it is considered the same from a person-to-group perspective. Evaluate as you would meet. When each collection occurs, it may be considered as a group.

  In addition, the threshold value (one or more) can be corrected to eliminate cases where it is difficult to actually communicate with each other, for example, when a large number of people gather together in a hall or the like.

  Alternatively, the adjacency matrix of the person-to-group as shown in FIG. 7 may be prepared from the beginning by grasping the network as the user and group nodes.

  To create a group-to-group relationship quantity adjacency matrix, basically, as shown in FIGS. 6 and 8, the individual-to-individual parts are aggregated, and the average value, median value, and sum in group units are calculated. Tally by such as.

  In addition, as described above, the network is grasped as the user and group nodes, and as shown in FIG. 7 and FIG. , Sum, etc. may be used.

  Moreover, you may calculate the relationship between groups by treating a group member set as one person.

  Further, correction may be performed in accordance with the relationship (conference etc.) at the same time. For example, as shown in FIG. 10, when three people in group A and two people in group B have a meeting for 10 minutes, the relationship amount between A and B is 10.

  In addition, as shown in FIG. 10, although it becomes a symmetric matrix if it totals by the group unit beforehand, it cannot divide | segment into an individual unit. Therefore, when analyzing an individual unit, it is necessary to prepare a related amount of the individual unit separately.

  It is preferable to reflect the relationship between groups in the adjacency matrix (person-to-person, person-to-group, group-to-group) according to the situation of each group.

  First, due to the relationship between groups, there is a case where the communication amount must be corrected even in other groups. For example, assume that the relationships between the A, B, C, D, and E departments include information such as the relations between the A and B departments being close and the relation between the A and C being far away (relationships between work, Possible types). Therefore, by weighting the relation amount between A and B and the relation amount between A and C from the prior information, it is possible to calculate an appropriate inter-departmental relationship in consideration of the prior information. In this example, as shown in FIG. 11, the relationship quantity can be adjusted by multiplying the reciprocal of the weighting coefficient.

  As another example, there is a case of grouping by job level. For example, it is assumed that it is necessary to appropriately correct the relationship quantity between adjacent groups such as general positions and section managers, section managers and section managers, and section managers and department managers, and relations between the general and department managers. In this case as well, as in the above example, it is considered effective to set a weighting coefficient between the relationships in advance and make adjustments.

  Such a correction can be generated from the organization information, and it is preferable that the inter-department relationship weight information and the like are supplemented in advance in the organization information database 16.

  Next, the inter-group exchange index calculation unit 12 will be described. The inter-group exchange index calculation unit 12 calculates the index (uniformity coefficient, which will be described later) by calculating the amount of relation to the group (department, section) to which the user belongs, but is not limited to this. Various variations can be adopted. For example, grouping may be performed by department, job level, home position, or the like. In a specific implementation example of this embodiment, a person-to-person cumulative time matrix is used, and a grouping definition of departments and positions is used.

  The uniformity coefficient employed as the AC index in this example is as follows, but is not limited to this.

When P _self is the connection probability [0, 1] to another person in the own department and the number of departments is N, the connection probability to the other department is
It is represented by At this time, the uniformity coefficient (UC: Uniformity Coefficient) is defined as follows.
UC = 1 (ie, 100%): a state where there is no division between divisions UC = 0.3 (ie, 30%): a state in which divisions are normally made UC = 0 (ie, 0%): a state in which division is complete it can.

  The result display unit 13 can display an intergroup exchange index (for example, a uniformity coefficient) in various modes. In this example, the characteristics of each user (or department) are visualized by plotting the uniformity factors of two different groupings on a two-dimensional plane. In this way, it is possible to list the characteristics of many users.

  At this time, various sections (groups) can be compared. In FIG. 12, the degree of exchange between groups is calculated and compared from two groupings of department and job rank. In this example, the degree of exchange between departments can be measured by the department uniformity coefficient, and the degree of exchange with superiors / subordinates can be measured by the rank uniformity coefficient. By knowing the degree of exchange between departments, you can see if you have relationships with various departments or only people in your department. In addition, by knowing the degree of exchange between positions, it is possible to know whether or not there is a relationship beyond the positions (for example, communication between supervisors and subordinates). The user's characteristics can be determined according to the position of the two-dimensional coordinate in FIG. Users at unique positions in the group can be identified at a glance. In addition, as shown in FIGS. 13 to 16, it is possible to analyze the time-series change of the user characteristics by summing up every month.

  The operation example of this embodiment is as follows.

[Step 1]: Person position information is acquired.
[Step 2]: A relation amount is calculated.
[Step 3]: An intergroup exchange index is calculated.
[Step 4]: Display the analysis result.

  According to this embodiment, the relationship quantity between users inside and outside the group given the relation quantity between users is measured, and whether or not the organization exchanges between groups is analyzed by a simple index. The characteristics of each user can be measured by comparing the exchange index between groups from a plurality of viewpoints (departments and positions).

  Next, a second embodiment of the present invention will be described.

  In the first embodiment, for example, the degree of exchange between departments is indexed from the uniformity coefficient. However, I interact a lot with other departments, but I don't know if that is a specific department or the whole. In the second embodiment, a variation coefficient is newly introduced to index the distribution unevenness between departments. In other words, the base time varies depending on the business form, job rank, and the like. Therefore, by adopting a coefficient of variation, the bias between the groups is relativized so that the bias can be calculated even among users whose average values differ greatly.

  In the second embodiment, the relationship between each user's department is visualized from the uniformity coefficient and the variation coefficient. Of course, the uniformity coefficient and the coefficient of variation for various groups may be considered from various aspects.

  FIG. 18 schematically illustrates the uniformity coefficient and the variation coefficient, and the uniformity coefficient represents a difference between an inward relationship (within the same group) and an outward relationship (within another group). The coefficient of variation represents the deviation of the outward quantity.

  FIG. 17 shows the organization analysis system of the second embodiment as a whole. In this figure, parts corresponding to those in FIG. In FIG. 17, an inter-department deviation index calculation unit 17 that calculates an inter-department deviation index (variation coefficient) from the inter-group exchange index (uniform coefficient) from the inter-group exchange index calculation unit 12 is provided.

  In this example, the following operation is performed.

[Step 1]: Person position information is acquired.
[Step 2]: A relation amount is calculated.
[Step 3]: An intergroup exchange index is calculated.
[Step 4]: An interdepartment deviation index is calculated.
[Step 5]: The analysis result is displayed.

  The inter-department deviation index calculation unit 17 calculates the following coefficient of variation as the inter-department deviation index, but other coefficients may be used.

It is assumed that the user Ua1 in the A department has the relationship quantities Tb, Tc, and Td with the users in the B department, the C department, and the D department, respectively. When the average value is ave and the standard deviation is SD, the coefficient of variation CV (Coefficient of variation) is CV = SD / ave.
It is obtained by.
Specifically, the coefficient of variation CV is
Is calculated by The coefficient of variation CV depends on the average value.

  Here, an example of exchange between departments of users belonging to department A will be considered with reference to FIGS.

  In the example of FIG. 19, the uniformity coefficient (UC) is small and the coefficient of variation (CV) is large. In this case, in the example on the left side, the user is biased with D in addition to the own department (A), and AC is seen.

  In the example of FIG. 20, both the uniformity coefficient (UC) and the coefficient of variation (CV) are small. In this case, the user can see the same level of exchange everywhere except in the own department (A). There is little exchange.

  In the example of FIG. 21, both the uniformity coefficient (UC) and the coefficient of variation (CV) are large. In this case, the user can see the exchange with D rather than the own department (A).

  In the example of FIG. 22, the uniformity coefficient (UC) is large and the coefficient of variation (CV) is small. In this case, the user can see the same level of exchange regardless of who else is.

  As shown in FIG. 23, the result display unit 13 of the second embodiment can grasp the degree of user interaction between departments by plotting the uniformity coefficient (UC) and the coefficient of variation (CV) on two axes. indicate. The deviation to the own department can be seen by the department uniformity coefficient, and the deviation among other departments can be seen by the coefficient of variation. When the uniformity coefficient is large and the coefficient of variation is small, it can be evaluated that there is equal exchange with any department.

  FIG. 26 shows an example of interpretation of the coordinate distribution of the uniformity coefficient and the variation coefficient, but the present invention is not limited to this, and various determination criteria can be adopted. In place of or in addition to outputting the coordinates, or in addition to this, each of the uniformity coefficient and the coefficient of variation may be discriminated by a given threshold value to determine the tissue state.

  FIG. 24 shows an output example in which time-series changes in user characteristics can be analyzed by tabulating each month. FIG. 25 shows the distribution of relational quantities in the upper left, upper right, lower left, and lower right examples in the distribution diagram.

  According to this embodiment, by comparing the inter-group exchange index of a certain group with the relationship index between other groups and the inter-group deviation index, it is also possible to simultaneously grasp the relationship with other groups.

  The present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the invention. For example, in the above example, the degree of exchange was analyzed based on the person position information acquired using an RFID tag or the like, but the amount of exchange of electronic messages such as e-mail, mailing list, electronic bulletin board, communication using the web, etc. Alternatively, the degree of exchange may be analyzed based on the amount of common electronic document access. For example, as shown in FIG. 27, when an electronic mail server 18 and an electronic document server 19 are used, an electronic log database 20 holding these logs may be prepared to calculate the user relation amount. In the case of e-mail, a log is stored as shown in FIG. 28, and it is determined that an exchange has occurred between the sender and the receiver. In the example of the figure, it is determined that there is an exchange between the users 001 and 005 and between the users 001 and 010. The same applies to the exchange of electronic messages other than electronic mail. In the case of document access, a document access log is stored as shown in FIG. 29, and it is determined that there is an exchange between users who have accessed a common document. In the example of FIG. 29, since the users 001 and 002 are accessing the document 005 in common, it is determined that there is an exchange between the users. Note that the type of access to be evaluated may be selected. For example, it may be limited to the case where writing is performed.

It is a figure which shows the structure of Example 1 of this invention as a whole. It is a figure explaining the person position information used in Example 1. FIG. It is a figure explaining the organization information used in Example 1. FIG. It is a figure explaining the relational quantity used in Example 1. FIG. It is a figure explaining the adjacency matrix (person to person) used in Example 1. FIG. It is a figure explaining the example of preparation of the adjacency matrix (person vs. group) used in Example 1. FIG. It is a figure explaining the creation example which creates directly the adjacency matrix (person versus group) used in Example 1. FIG. 6 is a diagram for explaining an example of creating an adjacency matrix (group-to-group) used in Embodiment 1. FIG. FIG. 10 is a diagram illustrating another example of creating an adjacency matrix (group vs. group) used in the first embodiment. It is a figure explaining the other example of creation which creates directly the adjacency matrix (group vs. group) used in Example 1. FIG. It is a figure explaining the correction between groups of Example 1. FIG. It is a figure which shows the analysis result of Example 1 typically. 6 is a diagram illustrating an example of analysis results of Example 1. FIG. FIG. 6 is a diagram showing another analysis result example of Example 1. FIG. 6 is a diagram showing another analysis result example of Example 1. FIG. 6 is a diagram showing another analysis result example of Example 1. It is a figure which shows the structure of Example 2 of this invention as a whole. It is a figure which illustrates typically the uniformity coefficient (UC) and variation coefficient (CV) of Example 2. FIG. It is a figure which shows typically the characteristic by the predetermined combination of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure which shows typically the characteristic by the other combination of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure which shows typically the characteristic by the other combination of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure which shows typically the characteristic by the other combination of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure which illustrates typically the analysis result display by the distribution of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure which shows the example of an analysis result display by distribution of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure explaining the correlation with the analysis result display by distribution of the uniformity coefficient (UC) of Example 2, and the variation coefficient (CV), and distribution of relational quantity. It is a figure explaining the example of the interpretation standard of the distribution of the uniformity coefficient (UC) of Example 2, and a coefficient of variation (CV). It is a figure explaining the modification of the above-mentioned Example. It is a figure which shows the example of the log of the email for demonstrating operation | movement of the modification of FIG. It is a figure which shows the example of the log of an electronic document access for demonstrating operation | movement of the modification of FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Analysis instruction | indication part 11 User relation amount calculation part 12 Intergroup exchange index calculation part 13 Result display part 14 Person position detection part 15 Person position database 16 Organization information database 17 Inter-segment deviation index calculation part 18 E-mail server 19 Electronic document server 20 Electronic log database 141 Position sensor 142 RFID tag

Claims (18)

A group information holding means for holding information about the group to which the target person belongs,
A group exchange analysis device comprising group exchange index calculation means for calculating a group exchange index representing information indicating the activity of the target person and the degree to which the target person interacts with other groups based on the information on the group. .   The group exchange analysis device according to claim 1, wherein the information representing the activity of the target person is an action history of the target person.   The group exchange analysis device according to claim 2, wherein the behavior history of the subject is human location information including a time and a location relating to the location of the subject.   A group alternating current analysis apparatus further comprising detecting means for detecting the person position information.   The group exchange analysis device according to claim 2, wherein the information representing the activity of the subject is communication history information of the subject.   It further has an output means for outputting the analysis result based on the group exchange index, and the output means groups the target person according to the group exchange index obtained by grouping the target person according to one standard and the other standard. The group exchange analyzer according to any one of claims 1 to 5, wherein the subject is displayed in two-dimensional coordinates on the basis of the obtained group exchange index.   The group exchange analysis device according to any one of claims 1 to 6, further comprising an intergroup deviation index calculating unit that calculates an intergroup deviation index indicating a variation of the group exchange index of the subject.   The apparatus further includes output means for outputting an analysis result based on the group exchange index and the intergroup deviation index, and the output means is configured to two-dimensionally coordinate the subject based on the group exchange index and the intergroup deviation index. The group alternating current analysis apparatus of Claim 7 displayed on these.   The group exchange analysis apparatus according to any one of claims 1 to 8, wherein the exchange index calculation means calculates the group exchange index using at least one of a target person and a group as a calculation unit.   The group AC analysis device according to any one of claims 1 to 9, wherein the AC index calculation means calculates the group AC index based on a weight set for each combination of the subject groups.   The group exchange according to any one of claims 1 to 10, wherein the exchange index calculation means reduces the degree of influence of the information representing the activity of the target person on the group exchange index according to the number of collaborators of the activity. Analysis equipment.   The group exchange analysis device according to claim 7 or 8, wherein the exchange index calculation means calculates the intergroup deviation index using at least one of the target person and the group as a calculation unit.   5. The AC index calculation means determines whether the target persons are located in the same place based on the person position information, and calculates the inter-group AC index based on the determination result. Group exchange analysis equipment.   The group alternating current analysis apparatus according to claim 4, wherein the detection means determines a location where the subject is located based on a device carried by the subject.   6. The group exchange analysis device according to claim 5, wherein the communication history information is transmission source and transmission destination information of an electronic message.   The group exchange analysis apparatus according to claim 15, wherein the electronic message is at least one of electronic mail, electronic bulletin board communication, instant message communication, and communication using the web. A group information holding hand holds information about a group to which the target person belongs;
The group exchange index calculating means includes a step of calculating a group exchange index representing the degree to which the subject person interacts with other groups based on the information representing the activity of the subject person and the information related to the group. AC analysis method. A group information holding hand holds information about a group to which the target person belongs;
The group exchange index calculation means is used for causing the computer to execute information indicating the activity of the target person and calculating a group exchange index indicating the degree to which the target person interacts with other groups based on the information regarding the group. A computer program for group exchange analysis.