JP2007102657A - Community analysis system, community analysis method, and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system and a method which perform community analysis by analyzing access log to an electronic document storing part. <P>SOLUTION: This system records user IDs and document IDs as access information to an electronic document storing part in association with the time and date of accesses, and selects and extracts a different user who makes access to the same document as a user who has community relation based on the access log, or extracts a different document to which access is made by the same user as the related document. For example, the system determines a set of users who have interest in the same document as the users who are in community relation, and can efficiently prepare network diagrams and analyze community based on these user sets. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a community analysis device, a community analysis method, and a computer program that perform community analysis such as a person-to-person relationship by analyzing an access log for an electronic document stored in a database, for example.

  Conventionally, in order to investigate the characteristics of organizational activities, methods for recording and analyzing communication in various forms such as communication using in-house mail between members of the organization and verbal reports have been used. For example, in a technique called social network analysis found in Non-Patent Document 1, a network between members of an organization is represented by a network diagram. As a result, it is possible to know the roles of members that are not represented in the personnel organization chart and the dependency relationships among the members. However, since network analysis is performed by an analyst conducting interviews and questionnaires to members of the organization, it is a problem that it takes time. Furthermore, analyst's experience is required for analysis.

  In recent years, with the development of the Internet, a lot of communication has been performed by electronic mail. E-mail is performed through a computer that manages transmission / reception of e-mail called a mail server. Communication records (electronic mail logs) are left in the mail server, and network analysis can be easily performed by using these.

  Non-Patent Document 2 proposes a network analysis technique using this electronic mail log. Network analysis is intended to analyze an organization's communication by expressing who has communicated with whom in a graph and examining the structure of the graph.

  With reference to FIG. 16, the analysis method of the organization communication by investigating the structure of a graph is demonstrated. In FIG. 16, a communication participant is represented by a node 11, and communication between the nodes 11 is represented by a link 12. The number of communications is represented by the thickness of the link 12.

  The network created from the access log is a superposition of various networks. That is, (A) all networks shown in FIG. 16 are a combination of various networks. If network analysis techniques are to be used, they need to be separated. For example, as shown in FIG. 16, (B1) a network corresponding to the organization chart, (B2) a general affairs network, (B3) a synchronized hiring network, etc., a separated network is acquired based on the information of each node.

  The important indicators in network analysis are the incoming and outgoing orders for nodes that are participants in communication, the distance between two points, central mediation, and the like. The number of links going to the node is called the incoming order, and the link leaving the node is called the outgoing order. The distance between two points is the number of nodes (the number of nodes) at which a node that directly exchanges e-mails from one node to another node is reached. Further, mediation centrality represents the degree to which information is transmitted when a certain node is removed.

Also, in Patent Document 1, history information of user message exchange is stored, the number of recipients who have returned an e-mail message, the number of recipients who have received a reply, the total amount (length) of posted messages, posts A method for obtaining the user's suitability for the role in the group activity from the number of threads that have been created is disclosed.
JP 2003-216785 A Lawrence Prussack, Rob Cross, Naohisa Nishi, “Social Network: A Source of Organizational Vitality” DIAMOND Harvard Business Review, October 2002, p. 96-107 Yasuda Yuki, “Network Analysis”, Shinsyo, 1997

  Thus, the use of electronic data makes it possible to analyze even a large amount of data. However, some electronic data is not data that directly represents communication, such as e-mail.

  For example, in an electronic filing system storing an electronic document, information on storage and browsing of the electronic document is recorded. These data are information that analyzes the activities of people in the organization, but the relationship between people who have accessed the same document is created, although the relationship between people is not described directly. it can. Using this graph, it is possible to analyze with the network analysis technique. However, the network described in the access relationship may include various types of networks, and these need to be separated. This relationship can be separated by interviewing or analyzing the text of the accessed document, but the ease of processing is lost. Furthermore, investigating the contents of accessed documents can violate the privacy of organizational members and is difficult to implement in this respect as well.

  The present invention has been made in view of the above circumstances, and a community analysis that realizes a community analysis from an electronic document access record in which a person-to-person relationship is not described while maintaining the privacy of a member who is a survey subject. To provide a device, a community analysis method, and a computer program

  A first aspect of the present invention is an access log storage unit that records, as access information for an electronic document storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier in association with access date information. And an access log analysis unit that executes an analysis process of the access log recorded in the access log storage unit, the access log analysis unit based on the access log recorded in the access log storage unit, The community analysis apparatus is characterized in that a process for selecting and extracting different users who have executed access to the same document as users having a community relationship is executed.

  According to this configuration, it becomes possible to efficiently select and extract users who are interested in the same document based on the access log for the electronic document storage unit, and a set of users who are interested in the same document has a community relationship. It is possible to determine as a user and efficiently create a network diagram and perform community analysis based on these user sets.

  Furthermore, in an embodiment of the community analysis device of the present invention, the access log analysis unit generates a network diagram in which each user having the community relationship is a node and the users having the community relationship are linked by a link. It is the structure which performs.

  According to this configuration, it is possible to efficiently create a network diagram and perform community analysis by applying the collective information of users who are interested in the same document extracted based on the access log for the electronic document storage unit. .

  Furthermore, in an embodiment of the community analysis device of the present invention, the access log analysis unit may identify different users who have accessed the same document within a threshold interval as a predetermined time interval as users having a community relationship. It is the structure which performs the process selectively selected as.

  According to this configuration, since the access user for the same document executed within the specified time is extracted based on the access log for the electronic document storage unit, for example, a specific processing phase of a project within a specific period It is possible to select a set of users who have shown interest in the same document, and it is possible to perform extraction processing of users having a deeper community relationship, creation of a network diagram, and community analysis.

  Furthermore, in an embodiment of the community analysis device of the present invention, the access log analysis unit is configured to execute a process of setting the threshold interval as a time interval having a high access density.

  According to this configuration, when the access user for the same document executed within the specified time is extracted based on the access log for the electronic document storage unit, the specified time is set as a time interval with a high access density. Thus, it is possible to perform flexible analysis processing according to the access processing status.

  Furthermore, the second aspect of the present invention provides an access log that records, as access information for the electronic document storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier in association with access date information. A storage unit, and an access log analysis unit that executes an analysis process of the access log recorded in the access log storage unit, wherein the access log analysis unit is based on the access log recorded in the access log storage unit Thus, the community analysis apparatus is characterized in that a process for extracting different documents accessed by the same user as related documents is executed.

  According to this configuration, it is possible to efficiently extract different documents accessed by the same user as related documents based on the access log for the electronic document storage unit, and based on these document related information. Create network diagrams and perform community analysis efficiently.

  Furthermore, in one embodiment of the community analysis device of the present invention, the access log analysis unit extracts different documents accessed by the same user as related documents within a threshold interval as a predetermined time interval. It is the structure which performs a process.

  According to this configuration, since a different document accessed by the same user within a specified time is extracted as a related document based on the access log for the electronic document storage unit, for example, a specific processing phase of a certain project For example, it is possible to select related documents accessed within a specific period.

  Further, according to a third aspect of the present invention, as access information for the electronic document storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier are recorded in the storage unit in association with access date information. An access log storage step, and an access log analysis step for executing an analysis process of an access log recorded in the access log storage unit in the access log analysis unit, wherein the access log analysis step includes the access log storage step. A community analysis method is characterized in that, based on an access log recorded in a section, a process of selecting and extracting different users who have performed access to the same document as users having a community relationship is performed.

  According to this configuration, it becomes possible to efficiently select and extract users who are interested in the same document based on the access log for the electronic document storage unit, and a set of users who are interested in the same document has a community relationship. It is possible to determine as a user and efficiently create a network diagram and perform community analysis based on these user sets.

  Furthermore, in one embodiment of the community analysis method of the present invention, the community analysis method further generates a network diagram in which each user having the community relationship is a node and the users having the community relationship are linked by a link. It has the step which performs a process, It is characterized by the above-mentioned.

  According to this configuration, it is possible to efficiently create a network diagram and perform community analysis by applying the collective information of users who are interested in the same document extracted based on the access log for the electronic document storage unit. .

  Furthermore, in an embodiment of the community analysis method of the present invention, the access log analysis step includes a step in which different users who have performed access to the same document within a threshold interval as a predetermined time interval are designated as users having a community relationship. As a feature, a process of selecting and extracting is executed.

  According to this configuration, since the access user for the same document executed within the specified time is extracted based on the access log for the electronic document storage unit, for example, a specific processing phase of a project within a specific period It is possible to select a set of users who have shown interest in the same document, and it is possible to perform extraction processing of users having a deeper community relationship, creation of a network diagram, and community analysis.

  Furthermore, in one embodiment of the community analysis method of the present invention, in the access log analysis step, a process of setting the threshold interval as a time interval having a high access density is executed.

  According to this configuration, when the access user for the same document executed within the specified time is extracted based on the access log for the electronic document storage unit, the specified time is set as a time interval with a high access density. Thus, it is possible to perform flexible analysis processing according to the access processing status.

  Further, according to a fourth aspect of the present invention, as access information for the electronic document storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier are recorded in the storage unit in association with access date information. An access log storage step, and an access log analysis step for executing an analysis process of an access log recorded in the access log storage unit in the access log analysis unit, wherein the access log analysis step includes the access log storage step. In the community analysis method, a process for extracting different documents accessed by the same user as related documents is executed based on the access log recorded in the section.

  According to this configuration, it is possible to efficiently extract different documents accessed by the same user as related documents based on the access log for the electronic document storage unit, and based on these document related information. Create network diagrams and perform community analysis efficiently.

  Furthermore, in one embodiment of the community analysis method of the present invention, the access log analysis step extracts different documents accessed by the same user as related documents within a threshold interval as a predetermined time interval. It is the structure which performs the process to perform.

  According to this configuration, since a different document accessed by the same user within a specified time is extracted as a related document based on the access log for the electronic document storage unit, for example, a specific processing phase of a certain project For example, it is possible to select related documents accessed within a specific period.

  Further, a fifth aspect of the present invention is a computer program that causes a computer to execute community analysis processing. As access information for an electronic document storage unit, a user ID that is an access user identifier and a document that is an access target document identifier An access log storage step for recording the ID in the storage unit in association with the access date information; and an access log analysis step for executing an analysis process of the access log recorded in the access log storage unit in the access log analysis unit. In the access log analysis step, a process of selecting and extracting different users who have executed access to the same document as users having a community relationship based on the access log recorded in the access log storage unit is executed. It is characterized by In the computer program.

  According to this configuration, it becomes possible to efficiently select and extract users who are interested in the same document based on the access log for the electronic document storage unit, and a set of users who are interested in the same document has a community relationship. It can be determined as a user, and a network diagram can be efficiently created and a community analysis can be performed by a computer based on these user sets.

  Furthermore, a sixth aspect of the present invention is a computer program that causes a computer to execute community analysis processing. As access information for an electronic document storage unit, a user ID that is an access user identifier and a document that is an access target document identifier An access log storage step for recording the ID in the storage unit in association with the access date information; and an access log analysis step for executing an analysis process of the access log recorded in the access log storage unit in the access log analysis unit. And in the access log analyzing step, a process of extracting different documents accessed by the same user as related documents is executed based on the access log recorded in the access log storage unit. Computer program Located in.

  According to this configuration, it is possible to efficiently extract different documents accessed by the same user as related documents based on the access log for the electronic document storage unit, and based on these document related information. Network diagram creation and community analysis can be efficiently performed by a computer.

  Note that the computer program of the present invention is a recording medium provided in a computer-readable format for a computer system capable of executing various program codes, for example, a recording medium such as a CD, FD, or MO. A computer program that can be provided by a medium or a communication medium such as a network. By providing such a program in a computer-readable format, processing corresponding to the program is realized on the computer system.

  Other objects, features, and advantages of the present invention will become apparent from a more detailed description based on embodiments of the present invention described later and the accompanying drawings. In this specification, the system is a logical set configuration of a plurality of devices, and is not limited to one in which the devices of each configuration are in the same casing.

  According to the configuration of one embodiment of the present invention, as access information for the electronic document storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier are recorded in association with access date information, Based on this access log, a process for selecting and extracting different users who have accessed the same document as users having a community relationship is executed, so that users who are interested in the same document are efficiently selected and extracted. It is possible to determine a set of users who have an interest in the same document as users having a community relationship, and to efficiently create a network diagram and perform community analysis based on these user sets.

  Furthermore, according to the configuration of the embodiment of the present invention, based on the access log for the electronic document storage unit, different documents accessed by the same user based on the access log recorded in the access log storage unit Since the process of extracting as related documents is executed, it is possible to efficiently extract related documents, and network diagrams can be efficiently created and community analysis can be performed based on these document related information. .

  Details of the community analysis device, community analysis method, and computer program of the present invention will be described below with reference to the drawings.

  First, an outline of community analysis of the present invention will be described. In the community analysis of the present invention, for example, an access log is analyzed for a data file stored in a database accessible via a network.

  For example, an access log accessing the same file is analyzed, and the relationship between users, that is, the relationship between people, is classified according to the access time interval. For example, a user who accesses the same document is analyzed to create a network, and community analysis is performed using a network analysis technique.

  In many cases, network analysis methods shown in general network analysis textbooks indicate analysis methods for an ideal network. For example, the analysis shows a network that has multiple communities in an organization but does not overlap. However, in addition to the organization on the organization chart, the actual organization includes a community of new employees and a community in charge of general affairs belonging to each group on the organization chart. In such a network, it is often difficult to directly apply a conventional network analysis method.

  In the present invention, for example, people belonging to the same community, such as a community of new employees, perform analysis processing based on the assumption that the same document stored in the database concentrates on a certain period of time. For example, a community that joins a company may access materials for training that has been held for a certain number of years after joining the company. Based on this assumption, a network is created by clustering and classifying users who access the same document within a predetermined period of time, thereby enabling network analysis.

  A configuration example of a network system as an execution environment for community analysis according to the present invention will be described with reference to FIG. The network system is, for example, a wide area network (WAN) constructed across a plurality of offices in a company, and a plurality of user terminals (clients) 51 to 53 are connected by a network 60 as shown in FIG. Has been. An electronic document filing device 70 is connected to the network 60, and the user terminals 51 to 53 can acquire or view an electronic document stored in the electronic document filing device 70.

  A community analysis device 100 is connected to the electronic document filing device 70. The community analysis device 100 analyzes the access status from the user terminals (clients) 51 to 53 to the electronic document filing device 70.

  FIG. 2 shows the configuration of the community analysis device 100. As illustrated in FIG. 2, the community analysis device 100 includes an access log storage unit 101 and an access log analysis unit 102. The access log storage unit 101 records access logs from the user terminals (clients) 51 to 53 for the electronic document filing device 70, and the access log analysis unit 102 analyzes the access logs recorded in the access log storage unit 101. Execute.

  The community analysis device 100 is configured by an information processing device such as a PC, and the access log storage unit 101 is configured by storage means such as a hard disk. The access log analysis unit 102 includes a data processing unit including a CPU that executes a computer program stored in a storage unit such as a ROM. A specific example of processing executed in the access log analysis unit 102 will be described in detail below.

  FIG. 3 shows an example of recorded data in the access log storage unit 101. As shown in FIG. 3, the access log storage unit 101 includes, as an access log from the user terminals (clients) 51 to 53 for the electronic document filing device 70, an access time and a user ID as an identifier of the user who executed the access. The correspondence data of the document ID as the identifier of the document to be accessed is recorded.

The access log analysis unit 102 analyzes the access log recorded in the access log storage unit 101. Hereinafter, different analysis processing examples of the access log analysis processing executed by the access log analysis unit 102 will be sequentially described. The following four processing examples will be described sequentially.
(Processing Example 1) Community Analysis by Analysis of Access Users for Common Documents (Processing Example 2) Community Analysis by Analysis of Access Users for Common Documents within Specified Time (Processing Example 3) Community by Analysis of Access to Different Documents by Common Users Analysis (Processing Example 4) Community analysis by analyzing access to different documents by the same user within a specified time

(Processing Example 1) Community Analysis by Analysis of Access User for Common Document First, community analysis processing by analysis of a user who accesses a common document will be described as community analysis processing example 1. An example of analysis processing in this processing example executed by the access log analysis unit 102 will be described with reference to FIG. The access log analysis unit 102 extracts a plurality of users who have accessed the same document from the access log recorded in the access log storage unit 101, and the users who have accessed the same document are community-related users. Judge that there is.

  As shown in FIG. 4, users A and B who use the user terminals (clients) 51 to 53 shown in FIG. 1 access the same document (document Da) stored in the electronic document filing device 70. When the access log storage unit 101 has an access log indicating that the user A and the user B belong to the same community, the access log analysis unit 102 determines that the user A and the user B belong to the same community. To do.

  Specifically, the number of times that different users access the same document in common is counted, and the user's community analysis is performed based on the count value. For example, the number of accesses is counted from the access log recorded in the access log storage unit 101, where A (U1, U2) is the number of accesses to the common document of user [U1] and user [U2]. The larger the access count A (U1, U2) for the common document, the higher the possibility that the users [U1] and [U2] belong to the same community.

  As described above, the access log analysis unit 102 determines that a set of users having a high access count to the same document is a user belonging to the same community. This analysis processing sequence will be described with reference to the flowchart shown in FIG.

  First, in step S101, the access log analysis unit 102 extracts a log for the analysis target period from the access log (see FIG. 3) recorded in the access log storage unit 101. The analysis period is set in advance by the operator as an analysis period.

  Next, in step S102, a variable [K] indicating the log number of the access log is set to an initial value K = 1. In step S103, the Kth log is selected. In step S104, the user ID recorded in the acquired Kth log is [U1], and the document ID is [D1].

  Next, in step S105, N = 1 is set as the initial setting of the variable [N] for setting the log number [K + N] of the comparison log. In step S106, the K + N-th log is acquired, and in step S107. The user ID recorded in the acquired K + Nth log is [U2], and the document ID is [D2].

  In step S108, the user IDs of the Kth and K + Nth logs are compared, and in step S109, the document IDs of the Kth and K + Nth logs are compared. If it is determined in step S108 that the user IDs of the Kth and K + Nth logs are the same, it is an access log record by the same user, so it is selected as data for analyzing a community between different users. Instead, the process proceeds to step S111.

  If the user IDs of the Kth and K + Nth logs are not the same in step S108, the process proceeds to step S109 to determine whether the document IDs of the Kth and K + Nth logs are the same. When the document IDs of the Kth and K + Nth logs are the same, it is determined that the Kth and K + Nth logs are logs indicating access information for the same document by different users. That is, the relationship described with reference to FIG. 4 is presented.

In this case, the process proceeds to step S110, and a process of increasing the data [A (U1, U2)] indicating the number of accesses to the same document by the user [U1] and the user [U2] by one is performed. That is,
A (U1, U2) ← A (U1, U2) +1
And

  [A (U1, U2)] is data indicating the number of accesses to the same document by the users U1, U2, and it is determined that the higher this value, the higher the probability that the users U1, U2 belong to the same community.

  In step S111, variable update processing for increasing the value of the variable [N] by one for setting the log number [K + N] of the comparison log is performed. That is, it is a process of setting a comparison log with the Kth log as the next log data. In step S112, it is determined whether or not the log number [K + N] of the comparison log exceeds the maximum log number, that is, whether or not it exists in the log acquired as the analysis target. If the log number [K + N] of the comparison log does not exceed the maximum log number, the process returns to step S106, and the comparison process between the Kth log and the K + Nth log is repeated.

  That is, the user ID and the document ID are compared, and if there is a relationship of access logs for the same document by different users, the data [A (U1) indicating the number of accesses to the same document by the user [U1] and the user [U2]. , U2)] is incremented by one.

  In step S112, if it is determined that the log number [K + N] of the comparison log exceeds the maximum log number and it is determined that the comparison up to the final log has been completed, the process proceeds to step S113, and the comparison source log number [K] is set. In step S114, it is determined whether or not the log number [K] of the comparison source log exceeds the maximum log number. That is, it is checked whether the log number of the last log has been exceeded. If the log number [K] does not exceed the maximum log number, the process returns to step S103, and the comparison process between the Kth log and the K + Nth log is repeated.

  In step S114, if it is determined that the log number [K] of the comparison source log exceeds the maximum log number and does not exist in the acquired log, the comparison processing between all acquired logs is completed, and the access log analysis is completed. The process of the unit 102 ends.

  By such log analysis, the access log analysis unit 102 can analyze a combination of users who have a high number of accesses to the same document and acquire such a set of users. These user sets can be estimated to be users belonging to the same community. The number of accesses A (Un, Um) for each user's same document is calculated by the processing flow described with reference to FIG. 5, and a network diagram is created based on the number of accesses A (Un, Um) for each user. To do. The access log analysis unit 102 executes a process of generating a network diagram in which each user having a community relationship is a node and the users having a community relationship are linked by a link.

  It is determined that the connection between users is higher as the number of accesses A (Un, Um) to the common document of each user is larger, and the access number A (Un, Um) between users is an index indicating the connection between users, that is, A network diagram is created by applying as a determination index belonging to the same community. By this method, for example, the network diagram shown in FIG. 6A is created. In the network diagram shown in FIG. 6A, a node 201 indicates a user, and a link 202 indicates a connection between users (nodes).

  The link is set based on the above access count A (Un, Um). For example, a link is set between users having an access count equal to or greater than a preset threshold. Specific examples of the network configuration method are described in, for example, Lawrence Prussack, Rob Cross “Social Network: Source of Organizational Vitality” DIAMOND Harvard Business Review, October 2002, page 96. By applying this method, the network diagram is generated by applying the access count A (Un, Um) between users calculated by the log analysis described above as an index indicating the connection between users, that is, a determination index belonging to the same community. To do. With this network diagram, it is possible to know the roles of the constituent members and the dependency relationships between the constituent members, which are not represented in the personnel organization chart.

  By using such a network diagram and calculating the shortest path mediation used in the network diagram analysis method, a community can be found. For example, as shown in FIG. 6A, the shortest paths A to C-B connecting the user node A and the user node B are detected by calculation of the shortest path mediation, and as shown in FIG. Community 1 to which user A belongs and community 2 to which user B belongs can be found. Each of these communities is a community having a certain common category, such as a community for synchronous employment and a community for development engineers of similar products.

(Processing Example 2) Community Analysis by Analysis of Access User for Common Document within Specified Time Next, as Community Analysis Processing Example 2, community analysis by analysis of a user who accesses a common document within a predetermined stipulated time Processing will be described. An example of analysis processing in this processing example executed by the access log analysis unit 102 will be described with reference to FIG. The access log analysis unit 102 extracts, from the access log recorded in the access log storage unit 101, a plurality of users who have accessed the same document within a predetermined time, and the same document within the predetermined time. It is determined that the user who has accessed is a user who has a community relationship.

  That is, in the previous processing example, whether or not the user belongs to the same community is determined using only whether or not the user has executed access to the same document without considering time. It was. In this processing example, a time element is further added, and it is determined whether or not there is a user relationship belonging to the same community based on whether or not the same document has been accessed within a specified time.

  As shown in FIG. 7, it is assumed that the users U1 to U3 have accessed the same document (document D1) stored in the electronic document filing device 70. Assume that an access log indicating these pieces of access information exists in the access log storage unit 101.

  The access log analysis unit 102 performs analysis based on these log information. The access time difference t1 between the user U1 and the user U2 with respect to the document D1 is less than a threshold interval [Ia] as a predetermined time interval, and the access time difference t2 between the user U1 and the user U3 with respect to the document D1 is determined in advance. More than the threshold interval [Ia] as the time interval. These pieces of time information are acquired based on log information (see FIG. 3) recorded in the access log storage unit 101.

  In this case, the access log analysis unit 102 determines that the relationship between the user U1 and the user U2 is highly likely to belong to the same community, and the relationship between the user U1 and the user U3 is a relationship that is unlikely to belong to the same community. To do.

  Also in this processing example, as in the previous processing example, the number of times that different users commonly access the same document at a time interval less than the specified time (threshold interval Ia) is counted. Based on the above, user community analysis is performed. For example, from the access log recorded in the access log storage unit 101, the number of accesses to the common document executed at a time interval less than the prescribed time (threshold interval Ia) between the user [U1] and the user [U2] is A (U1 , U2), the number of accesses is counted. It is determined that the users [U1] and [U2] are more likely to belong to the same community as the access count A (U1, U2) with respect to the common document executed in less than the specified time is larger.

  As described above, the access log analysis unit 102 determines that a set of users having a high access count for the same document within a specified time is a user belonging to the same community. This analysis processing sequence will be described with reference to the flowchart shown in FIG.

  First, in step S201, the access log analysis unit 102 extracts a log for the analysis target period from the access log (see FIG. 3) recorded in the access log storage unit 101. The analysis period is set in advance by the operator as an analysis period.

  In step S202, a variable [K] indicating the log number of the access log is set to an initial value K = 1. In step S203, the Kth log is selected, and in step S204, the access time recorded in the acquired Kth log is [T1], the user ID is [U1], and the document ID is [D1].

  Next, in step S205, N = 1 is set as the initial setting of the variable [N] for setting the log number [K + N] of the comparison log. In step S206, the K + N-th log is acquired, and in step S207. The access time recorded in the acquired K + Nth log is [T2], the user ID is [U2], and the document ID is [D2].

  In step S208, the user IDs of the Kth and K + Nth logs are compared. In step S209, the document IDs of the Kth and K + Nth logs are compared. In step S210, the access times of the Kth and K + Nth logs are compared. [T2−T1] is compared with a predetermined threshold interval [Ia]. That is, it is determined whether or not the difference between the access times of the two logs is less than the threshold interval [Ia].

  If it is determined in step S208 that the user IDs of the Kth and K + Nth logs are the same, it is the access log record by the same user, so it is selected as the data for analyzing the community between different users. Instead, the process proceeds to step S212.

  If the user IDs of the Kth and K + Nth logs are not the same in step S208, the process proceeds to step S209 to determine whether the document IDs of the Kth and K + Nth logs are the same. When the document IDs of the Kth and K + Nth logs are the same, it is determined that the Kth and K + Nth logs are logs indicating access information for the same document by different users.

In this case, the process further proceeds to step S210, and the difference [T2−T1] between the access times of the Kth and K + Nth logs is compared with a predetermined threshold interval [Ia]. That is,
T2-T1 <Ia
It is determined whether or not the above equation holds. This is a process for determining whether or not the difference between the access times of the two logs is less than the threshold interval [Ia].

If the above equation is satisfied, the process proceeds to step S211, and the data [A (U1, U2)] indicating the number of accesses to the same document within the specified time by the user [U1] and the user [U2] is increased by one. To do. That is,
A (U1, U2) ← A (U1, U2) +1
And This case corresponds to the relationship between the user U1 and the user U2 shown in FIG.

In step S210, the difference [T2-T1] between the access times of the Kth and K + Nth logs is
T2-T1 <Ia
If the difference between the access times of the two logs is equal to or greater than the threshold interval [Ia], the users of the two logs are accessing the same document, but the predetermined threshold interval The access has the above difference, and it is determined that the commonality between users shown in the log is low, and the process proceeds to step S212 without executing the count-up process in step S211. This case corresponds to the relationship between the user U1 and the user U3 shown in FIG.

  In this processing example, [A (U1, U2)] is data indicating the number of accesses to the same document within the specified time of the users U1, U2, and the higher this value, the more the users U1, U2 belong to the same community. It is determined that the probability is high.

  In step S212, variable update processing for increasing the value of the variable [N] by one for setting the log number [K + N] of the comparison log is performed. That is, it is a process of setting a comparison log with the Kth log as the next log data. In step S213, it is determined whether or not the log number [K + N] of the comparison log exceeds the maximum log number, that is, whether or not it exists in the log acquired as the analysis target. If the log number [K + N] of the comparison log does not exceed the maximum log number, the process returns to step S206, and the comparison process between the Kth log and the K + Nth log is repeated.

  That is, the comparison between the user ID and the document ID between the logs and the comparison between the time difference [T2-T1] between the logs and the threshold interval [Ia] are performed, and the access logs for the same document within the threshold time Ta by different users If there is a relationship, a process of increasing data [A (U1, U2)] indicating the number of accesses to the same document within a specified time by user [U1] and user [U2] by one is performed.

  If it is determined in step S213 that the log number [K + N] of the comparison log exceeds the maximum log number, and it is determined that the comparison up to the final log is completed, the process proceeds to step S214, and the log number [K] of the comparison source is set. A process of incrementing by one is performed, and in step S215, it is determined whether or not the log number [K] of the comparison source log exceeds the maximum log number. That is, it is checked whether the log number of the last log has been exceeded. If the log number [K] does not exceed the maximum log number, the process returns to step S203, and the comparison process between the Kth log designated by the updated log number [K] and the K + Nth log is repeated.

  In step S215, if it is determined that the log number [K] of the comparison source log exceeds the maximum log number and does not exist in the acquisition log, the comparison processing between all acquisition logs is completed, and the access log analysis is completed. The process of the unit 102 ends.

  By such log analysis, the access log analysis unit 102 can analyze a combination of users who have a high number of accesses within the specified time for the same document, and acquire such a set of users. These user sets can be estimated to be users belonging to the same community. As described above, the number of accesses A (Un, Um) for the same document within the specified time of each user is calculated by the processing flow described with reference to FIG. (Un, Um) to create a network diagram.

  As the number of accesses A (Un, Um) to the common document within the threshold interval [Ia] of each user is larger, it is determined that the connection between users is higher, and the number of accesses A (Un, Um) between users is determined between users. A network diagram is created by applying as an index indicating the connection between the two, that is, a determination index belonging to the same community. This method also creates a network diagram similar to that described above with reference to FIG.

  By such network analysis based on the access count A (Un, Um) for the common document within the threshold interval [Ia], for example, the existence of different communities, for example, a section manager community and a synchronous joining community, as shown in FIG. Can be grasped. For example, it is possible to analyze the existence of a community that is difficult to read directly from the organization chart by analyzing access logs for annual education materials.

  In the above-described processing example, as described with reference to FIG. 7, in the analysis based on the log information executed by the access log analysis unit 102, the difference in access time for the same document D1 between different users is determined in advance. When the threshold interval [Ia] is compared with the threshold interval [Ia], and the relationship between the users is determined, the threshold interval [Ia] to be applied has been described as a fixed time. A configuration may be adopted in which it is determined according to the access status to the document.

  This processing example will be described with reference to FIG. FIG. 10A shows the processing when the threshold interval [Ia] as a predetermined time interval is applied, as in the processing example described above. (B) shows a configuration example in which the threshold interval is determined according to the access status to the document. In the figure, the passage of time is shown from left to right, and the mark 301 shows the access timing by the user. These pieces of time information are acquired based on log information (see FIG. 3) recorded in the access log storage unit 101.

  From this access history, it is analyzed that access is concentrated between times T1 and T2. This period of high access density is defined as a threshold interval [Ib] applied when determining the relationship between users. Specifically, a period having an access density equal to or higher than a predetermined access density value is set as the threshold interval [Ib]. Thus, by setting the threshold interval [Ib] according to the access density, more flexible analysis can be performed.

(Processing Example 3) Community Analysis by Access Analysis for Different Documents by a Common User Next, community analysis processing by access analysis for different documents by a common user will be described as community analysis processing example 3. An example of analysis processing in this processing example executed by the access log analysis unit 102 will be described with reference to FIG. The access log analysis unit 102 extracts access logs for different documents by a common user from the access logs recorded in the access log storage unit 101, and determines that the documents accessed by the common user are related documents. .

  As shown in FIG. 11, an access log indicating that the same user has accessed different documents stored in the electronic document filing device 70, that is, the document Da and the document Db, exists in the access log storage unit 101. In this case, the access log analysis unit 102 determines that the document Da and the document Db are highly related documents based on the log information.

  Specifically, the number of times the same user accesses different documents is counted, and community analysis is performed based on the count value. For example, from the access log recorded in the access log storage unit 101, the access count for different documents [D1] and [D2] by the same user [U1] is A (D1, D2), and the access count is counted. It is determined that the documents [D1] and [D2] are more likely to be highly related documents as the number of accesses A (D1, D2) to different documents by the common user increases. This analysis processing sequence will be described with reference to the flowchart shown in FIG.

  First, in step S301, the access log analysis unit 102 extracts a log for the analysis target period from the access log (see FIG. 3) recorded in the access log storage unit 101. The analysis period is set in advance by the operator as an analysis period.

  Next, in step S302, a variable [K] indicating the log number of the access log is set to an initial value K = 1. In step S303, the Kth log is selected, and in step S304, the user ID recorded in the acquired Kth log is [U1], and the document ID is [D1].

  Next, in step S305, N = 1 is set as the initial setting of the variable [N] for setting the log number [K + N] of the comparison log. In step S306, the K + N-th log is acquired, and in step S307. The user ID recorded in the acquired K + Nth log is [U2], and the document ID is [D2].

  In step S308, the document IDs of the Kth and K + Nth logs are compared, and in step S309, the user IDs of the Kth and K + Nth logs are compared. If it is determined in step S308 that the document IDs of the Kth and K + Nth logs are the same, it is an access log record for the same document, so that it is not selected as data as a related document determination processing target, and step S311 is performed. Proceed to

  If the document IDs of the Kth and K + Nth logs are not the same in step S308, the process proceeds to step S309 to determine whether the user IDs of the Kth and K + Nth logs are the same. When the user IDs of the Kth and K + Nth logs are the same, it is determined that the Kth and K + Nth logs are logs indicating access information for different documents by the same user. That is, the relationship described with reference to FIG. 11 is presented.

In this case, the process proceeds to step S310, in which data [A (D1, D2)] indicating the number of accesses to different documents by the same user is increased by one. That is,
A (D1, D2) <-A (D1, D2) +1
And

  [A (D1, D2)] is data indicating the number of accesses by the common user to the documents D1, D2, and it is determined that the higher the numerical value, the higher the relevance of the documents D1, D2.

  In step S311, variable update processing for increasing the value of the variable [N] by one for setting the log number [K + N] of the comparison log is performed. That is, it is a process of setting a comparison log with the Kth log as the next log data. In step S312, it is determined whether or not the log number [K + N] of the comparison log exceeds the maximum log number, that is, whether or not it exists in the log acquired as the analysis target. If the log number [K + N] of the comparison log does not exceed the maximum log number, the process returns to step S306, and the comparison process between the Kth log and the K + Nth log is repeated.

  That is, the comparison between the document ID and the user ID is performed, and if there is an access log relationship with respect to different documents by the same user, data [A (D1, D1, D2)] is incremented by one.

  In step S312, when it is determined that the log number [K + N] of the comparison log exceeds the maximum log number and it is determined that the comparison up to the final log is completed, the process proceeds to step S313, and the log number [K] of the comparison source is set. A process of incrementing by one is performed, and in step S314, it is determined whether or not the log number [K] of the comparison source log exceeds the maximum log number. That is, it is checked whether the log number of the last log has been exceeded. If the log number [K] does not exceed the maximum log number, the process returns to step S303, and the comparison process between the Kth log and the K + Nth log is repeated.

  In step S314, if it is determined that the log number [K] of the comparison source log exceeds the maximum log number and does not exist in the acquisition log, the comparison processing between all acquisition logs is completed, and the access log analysis is completed. The process of the unit 102 ends.

  Through such log analysis, the access log analysis unit 102 can analyze a combination of documents having a high number of accesses to different documents by the same user, and obtain a set of such documents. These document sets can be presumed to be document sets that are likely to be accessed by users belonging to the same community. According to the processing flow described with reference to FIG. 12, the access count A (Dn, Dm) for different documents of the same user is calculated, and a network diagram is created based on the access count A (Dn, Dm).

  The larger the number of accesses A (Dn, Dm) for different documents of the same user, the higher the relevance between the documents, and these document sets are likely to be accessed by users belonging to the same community. It is estimated that there is also a high connection between different users who have a connection with such a common document set, and this estimation is an index indicating the connection between users, that is, a determination belonging to the same community Apply as an index to create a network diagram. Also with this method, for example, a network diagram similar to that described with reference to FIG. 6 is set, and network analysis can be performed.

(Processing Example 4) Community Analysis by Analyzing Access to Different Documents by the Same User within a Specified Time Next, as a community analysis processing example 4, accesses made to different documents by the same user within a predetermined specified time A community analysis process performed by the above analysis will be described. An example of analysis processing in this processing example executed by the access log analysis unit 102 will be described with reference to FIG. The access log analysis unit 102 extracts, from the access logs recorded in the access log storage unit 101, logs indicating that the same user has accessed different documents within a predetermined time, and these specified A document accessed by the same user within the time is determined to be a highly relevant document.

  That is, in the previous processing example 3, the relevance determination is performed in which the access document by the same user is determined as a related document without considering the time. However, in this processing example, the time element is further added and the relevance determination is performed. When a user accesses a document within a specified time, it is determined that the document is relevant.

  As shown in FIG. 13, it is assumed that the user U1 accesses a different document stored in the electronic document filing device 70, that is, the documents D1 to D3. Assume that an access log indicating these pieces of access information exists in the access log storage unit 101.

  The access log analysis unit 102 performs analysis based on these log information. The access time difference t1 between the document D1 and the document D2 by the user U1 is less than the threshold interval [Ib] as a predetermined time interval, and the access time difference t2 between the document D1 and the document D3 is a predetermined time interval. The threshold interval [Ib] is over. These pieces of time information are acquired based on log information (see FIG. 3) recorded in the access log storage unit 101.

  In this case, the access log analysis unit 102 determines that the relationship between the document D1 and the document D2 is high and the relationship between the document D1 and the document D3 is low.

  In this processing example, for each combination (Dn, Dm) of documents, the number of times accessed by the same user at a time interval less than the specified time (threshold interval Ia) is counted, and the relevance of the document is determined based on the count value. Judging. For example, from the access log recorded in the access log storage unit 101, the number of accesses from the same user executed for the document [D1] and the document [D2] at a time interval less than a specified time (threshold time Ta). Is A (D1, D2), and the number of accesses is counted. The documents [D1] and [D2] are determined to have higher relevance as the number of accesses A (D1, D2) for different documents executed by the same user in less than the specified time shows a larger value. This analysis processing sequence will be described with reference to the flowchart shown in FIG.

  First, in step S401, the access log analysis unit 102 extracts a log for the analysis target period from the access log (see FIG. 3) recorded in the access log storage unit 101. The analysis period is set in advance by the operator as an analysis period.

  Next, in step S402, a variable [K] indicating the log number of the access log is set to an initial value K = 1. In step S403, the Kth log is selected. In step S404, the access time recorded in the acquired Kth log is [T1], the user ID is [U1], and the document ID is [D1].

  Next, in step S405, N = 1 is set as the initial setting of the variable [N] for setting the log number [K + N] of the comparison log. In step S406, the K + N-th log is acquired, and in step S407. The access time recorded in the acquired K + Nth log is [T2], the user ID is [U2], and the document ID is [D2].

  In step S408, the document IDs of the Kth and K + Nth logs are compared. In step S409, the user IDs of the Kth and K + Nth logs are compared. In step S410, the access times of the Kth and K + Nth logs are compared. The difference [T2−T1] is compared with a predetermined threshold interval [Ib]. That is, it is determined whether or not the difference between the access times of the two logs is less than the threshold interval [Ib].

  In step S408, if it is determined that the document IDs of the Kth and K + Nth logs are the same, it is an access log record for the same document, so it is selected as data as a target for determining the relationship between different documents. Otherwise, the process proceeds to step S412.

  If the document IDs of the Kth and K + Nth logs are not the same in step S408, the process proceeds to step S409 to determine whether the user IDs of the Kth and K + Nth logs are the same. When the user IDs of the Kth and K + Nth logs are the same, it is determined that the Kth and K + Nth logs are logs indicating access information for different documents by the same user.

In this case, the process further proceeds to step S410, and the difference [T2−T1] between the access times of the Kth and K + Nth logs is compared with a predetermined threshold interval [Ib]. That is,
T2-T1 <Ib
It is determined whether or not the above equation holds. This is processing for determining whether or not the difference between the access times of the two logs is less than the threshold interval [Ib].

If the above equation is satisfied, the process proceeds to step S411, and a process of increasing the data [A (D1, D2)] indicating the number of accesses to a different document by the same user by one is performed. That is,
A (D1, D2) <-A (D1, D2) +1
And This case corresponds to the relationship between the document D1 and the document D2 shown in FIG.

  [A (D1, D2)] is data indicating the number of accesses by the common user to the documents D1, D2, and it is determined that the higher the numerical value, the higher the relevance of the documents D1, D2.

In step S410, the difference [T2-T1] between the access times of the Kth and K + Nth logs is
T2-T1 <Ib
Is not satisfied, that is, when the difference between the access times of the two logs is equal to or greater than the threshold interval [Ib], the documents of the two logs are accessed by the same user, but are equal to or longer than a predetermined threshold time. Therefore, it is determined that the relevance between the documents shown in the log is low, and the process proceeds to step S412 without executing the count-up process in step S411. This case corresponds to the relationship between the document D1 and the document D3 shown in FIG.

  In this processing example, [A (D1, D2)] is data indicating the number of accesses by the same user within the prescribed time of the documents D1, D2, and the higher this value, the higher the relevance of the documents D1, D2. It is determined.

  In step S412, variable update processing for increasing the value of the variable [N] by one for setting the log number [K + N] of the comparison log is performed. That is, it is a process of setting a comparison log with the Kth log as the next log data. In step S413, it is determined whether or not the log number [K + N] of the comparison log exceeds the maximum log number, that is, whether or not it exists in the log acquired as the analysis target. If the log number [K + N] of the comparison log does not exceed the maximum log number, the process returns to step S406, and the comparison process between the Kth log and the K + Nth log is repeated.

  That is, the comparison between the document ID and the user ID between the logs and the comparison between the time difference [T2-T1] between the logs and the threshold interval [Ib] are performed, and access corresponding to different documents within the threshold interval Ib by the same user. If there is a log relationship, a process of increasing the data [A (D1, D2)] indicating the number of accesses by the same user within the specified time of the documents D1, D2 by one is performed.

  In step S413, if it is determined that the log number [K + N] of the comparison log exceeds the maximum log number and it is determined that the comparison up to the final log is completed, the process proceeds to step S414, and the log number [K] of the comparison source is set. A process of incrementing by one is performed, and in step S415, it is determined whether or not the log number [K] of the comparison source log exceeds the maximum log number. That is, it is checked whether the log number of the last log has been exceeded. If the log number [K] does not exceed the maximum log number, the process returns to step S403, and the comparison process between the Kth log specified by the updated log number [K] and the K + Nth log is repeated.

  In step S415, if it is determined that the log number [K] of the comparison source log exceeds the maximum log number and does not exist in the acquisition log, the comparison processing between all acquisition logs is completed, and the access log analysis is completed. The process of the unit 102 ends.

  By such log analysis, the access log analysis unit 102 can analyze a combination of different documents having a high number of accesses within a specified time by the same user, and obtain such a set of documents. It is determined that these document sets are highly relevant, and it is assumed that these document sets are highly likely to be accessed by users belonging to the same community. A connection between different users having a connection is also estimated to have a high connection, and this estimation is applied as an index indicating the connection between users, that is, a determination index belonging to the same community, to create a network diagram. Also with this method, for example, a network diagram similar to that described with reference to FIG. 6 is set, and network analysis can be performed.

As described above, as a plurality of community analysis processing examples, the following four processing examples are given as community analysis processing examples by analysis of a document access log by a user.
(Processing Example 1) Community Analysis by Analysis of Access Users for Common Documents (Processing Example 2) Community Analysis by Analysis of Access Users for Common Documents within Specified Time (Processing Example 3) Community by Analysis of Access to Different Documents by Common Users Analysis (Processing Example 4) Community Analysis by Analyzing Access to Different Documents by the Same User within a Specified Time These processing examples have been described.

  For example, in the processing example 2 and the processing example 4, a configuration is set in which a break is set for each specified time and the user's community relationship is analyzed. For example, by executing such a time break community analysis, It becomes easy to analyze the relations of members of a specific project.

  An example of community analysis processing will be described with reference to FIG. For example, when there is a development project for a product in a certain company, each process such as planning, design, prototyping, evaluation, mass production, etc. is performed according to the progress of time as a process flow regarding the product. Members involved in these projects will be involved in any of the processing steps. If there is a community that can obtain Fig. 15 (a) as a network diagram of members who participated in any of the planning, design, prototyping, evaluation, and mass production steps, the processing example 2 and the processing example 4 described above are used. By applying and analyzing the community relationship of users by setting a break at each specified time, an individual community network corresponding to the timing of each stage of the project as shown in FIGS. It can be acquired and analyzed.

  (Processing Example 1) By applying community analysis based on analysis of users accessing common documents, it is Kano to analyze a network of users who are accessing documents related to a certain specialized field, for example, architecture. (Processing Example 3) By executing community analysis by analyzing access to different documents by a common user, it becomes possible to analyze the relevance of different documents, and a user having a connection with such a highly related document set. It becomes possible to perform the connection between them by network analysis.

  The present invention has been described in detail above with reference to specific embodiments. However, it is obvious that those skilled in the art can make modifications and substitutions of the embodiments without departing from the gist of the present invention. In other words, the present invention has been disclosed in the form of exemplification, and should not be interpreted in a limited manner. In order to determine the gist of the present invention, the claims section described at the beginning should be considered.

  The series of processes described in the specification can be executed by hardware, software, or a combined configuration of both. When executing processing by software, the program recording the processing sequence is installed in a memory in a computer incorporated in dedicated hardware and executed, or the program is executed on a general-purpose computer capable of executing various processing. It can be installed and run.

  For example, the program can be recorded in advance on a hard disk or ROM (Read Only Memory) as a recording medium. Alternatively, the program is temporarily or permanently stored on a removable recording medium such as a flexible disk, a CD-ROM (Compact Disc Read Only Memory), an MO (Magneto optical) disk, a DVD (Digital Versatile Disc), a magnetic disk, or a semiconductor memory. It can be stored (recorded). Such a removable recording medium can be provided as so-called package software.

  The program is installed on the computer from the removable recording medium as described above, or is wirelessly transferred from the download site to the computer, or is wired to the computer via a network such as a LAN (Local Area Network) or the Internet. The computer can receive the program transferred in this manner and install it on a recording medium such as a built-in hard disk.

  Note that the various processes described in the specification are not only executed in time series according to the description, but may be executed in parallel or individually according to the processing capability of the apparatus that executes the processes or as necessary. Further, in this specification, the system is a logical set configuration of a plurality of devices, and the devices of each configuration are not limited to being in the same casing.

  As described above, according to the configuration of the embodiment of the present invention, the access date and time information includes the user ID that is the access user identifier and the document ID that is the access target document identifier as the access information for the electronic document storage unit. The user is interested in the same document because the process of selecting and extracting different users who have accessed the same document as users having a community relationship based on the access log is recorded. Can be selected and extracted efficiently, and a group of users who are interested in the same document is determined as a community-related user, and a network diagram is efficiently created and a community analysis is performed based on these users. be able to.

  Furthermore, according to the configuration of the embodiment of the present invention, based on the access log for the electronic document storage unit, different documents accessed by the same user based on the access log recorded in the access log storage unit Since the process of extracting as related documents is executed, it is possible to efficiently extract related documents, and network diagrams can be efficiently created and community analysis can be performed based on these document related information. .

It is a figure which illustrates the structure of the network system to which the community analysis concerning this invention is applied. It is a figure which shows the structure of the community analyzer concerning this invention. It is a figure which shows the example of the recording data of an access log memory | storage part. It is a figure explaining an example of the analysis process which an access log analysis part performs. It is a figure which shows the flowchart explaining the analysis process sequence which an access log analysis part performs. It is a figure which shows the example of the network diagram produced | generated by applying the analysis information performed in the community analysis apparatus concerning this invention. It is a figure explaining an example of the analysis process which an access log analysis part performs. It is a figure which shows the flowchart explaining the analysis process sequence which an access log analysis part performs. It is a figure explaining the example of an analysis process performed in the community analysis apparatus concerning this invention. It is a figure explaining an example of the threshold interval applied in the analysis processing which an access log analysis part performs. It is a figure explaining an example of the analysis process which an access log analysis part performs. It is a figure which shows the flowchart explaining the analysis process sequence which an access log analysis part performs. It is a figure explaining an example of the analysis process which an access log analysis part performs. It is a figure which shows the flowchart explaining the analysis process sequence which an access log analysis part performs. It is a figure explaining the example of an analysis process performed in the community analysis apparatus concerning this invention. It is a figure explaining the analysis method of the organization communication performed by investigating the structure of a graph.

Explanation of symbols

11 nodes 12 links 51 to 53 user terminals (clients)
60 Network 70 Electronic Document Filing Device 100 Community Analysis Device 101 Access Log Storage Unit 102 Access Log Analysis Unit

Claims (14)

As access information for the electronic document storage unit, an access log storage unit that records a user ID that is an access user identifier and a document ID that is an access target document identifier in association with access date and time information;
An access log analysis unit that executes an analysis process of the access log recorded in the access log storage unit,
The access log analysis unit
Based on the access log recorded in the access log storage unit,
A community analysis apparatus characterized by executing a process of selecting and extracting different users who have executed access to the same document as users having a community relationship. The access log analysis unit
The community analysis apparatus according to claim 1, wherein the community analysis apparatus is configured to execute a process of generating a network diagram in which each user having the community relationship is a node and the users having the community relationship are linked by a link. The access log analysis unit
2. The configuration according to claim 1, wherein a process of selecting and extracting different users who have accessed the same document as users having a community relationship within a threshold interval as a predetermined time interval is performed. Community analysis device. The access log analysis unit
The community analysis device according to claim 3, wherein the threshold analysis interval is configured to execute processing for setting a time interval having a high access density. As access information for the electronic document storage unit, an access log storage unit that records a user ID that is an access user identifier and a document ID that is an access target document identifier in association with access date and time information;
An access log analysis unit that executes an analysis process of the access log recorded in the access log storage unit,
The access log analysis unit
Based on the access log recorded in the access log storage unit,
A community analyzing apparatus characterized in that it is configured to execute processing for extracting different documents accessed by the same user as related documents. The access log analysis unit
6. The community analysis according to claim 5, wherein a process of extracting different documents accessed by the same user as related documents is executed within a threshold interval as a predetermined time interval. apparatus. An access log storage step of recording, in the storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier as access information for the electronic document storage unit in association with the access date information;
An access log analysis step for performing an analysis process of the access log recorded in the access log storage unit in the access log analysis unit;
The access log analyzing step includes:
Based on the access log recorded in the access log storage unit,
A community analysis method characterized by executing a process of selecting and extracting different users who have executed access to the same document as users having a community relationship. The community analysis method further includes:
8. The community analysis method according to claim 7, further comprising a step of generating a network diagram in which each user having the community relationship is a node and the users having the community relationship are linked by a link. The access log analyzing step includes:
The community analysis according to claim 7, wherein a process of selecting and extracting different users who have accessed the same document as users having a community relationship within a threshold interval as a predetermined time interval is executed. Method. In the access log analysis step,
The community analysis method according to claim 9, wherein a process of setting the threshold interval as a time interval having a high access density is executed. An access log storage step of recording, in the storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier as access information for the electronic document storage unit in association with the access date information;
An access log analysis step for performing an analysis process of the access log recorded in the access log storage unit in the access log analysis unit;
The access log analyzing step includes:
Based on the access log recorded in the access log storage unit,
A community analysis method characterized by executing a process of extracting different documents accessed by the same user as related documents. The access log analyzing step includes:
The community analysis according to claim 11, wherein a process of extracting different documents accessed by the same user as related documents within a threshold interval as a predetermined time interval is executed. Method. A computer program that executes community analysis processing on a computer,
An access log storage step of recording, in the storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier as access information for the electronic document storage unit in association with the access date information;
In the access log analysis unit, an access log analysis step for executing an analysis process of the access log recorded in the access log storage unit,
Let it run
In the access log analysis step,
Based on the access log recorded in the access log storage unit,
A computer program for executing a process of selecting and extracting different users who have executed access to the same document as users having a community relationship. A computer program that executes community analysis processing on a computer,
An access log storage step of recording, in the storage unit, a user ID that is an access user identifier and a document ID that is an access target document identifier as access information for the electronic document storage unit in association with the access date information;
In the access log analysis unit, an access log analysis step for executing an analysis process of the access log recorded in the access log storage unit,
Let it run
In the access log analysis step,
Based on the access log recorded in the access log storage unit,
A computer program for executing processing for extracting different documents accessed by the same user as related documents.

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