KR20090001751A - User behavior analysis method and system - Google Patents

Abstract

A user behavior analytical method and a system are provided to arrange user behaviors through a user log according to the order of time of each user, thereby conducting time-serial analysis on the user behaviors easily and simply . Through an LCS(Log Collection Server), user logs are collected(410). The user logs are converted into flow field formats to generate a flow field set(420). The flow field set is processed as a processing instruction on the flow field set is inputted(430).

Description

USER BEHAVIOR ANALYSIS METHOD AND SYSTEM

1 is an example for explaining a method of measuring a user behavior pattern in the prior art.

2 is an example for explaining an overview of a user behavior analysis method according to the present invention.

3 is another example for explaining an overview of a user behavior analysis method according to the present invention.

4 is a flowchart illustrating a user behavior analysis method according to an embodiment of the present invention.

5 is an example for explaining a user log collection method according to the present invention.

6 is a flowchart illustrating a method of generating a floating field set according to an embodiment of the present invention.

7 is an example for explaining the flow field aggregation and flow field according to the present invention.

8 is an example of a flow field set, in accordance with the present invention.

9 is an example for explaining the splitting and merging between flow field sets according to the present invention.

10 is an example of processing a flow field set in accordance with the present invention.

11 is an example for explaining a processing instruction according to the present invention.

12 is a block diagram illustrating an internal configuration of a user behavior analysis system according to another embodiment of the present invention.

Fig. 13 is an example for explaining a method for distributing data according to the present invention.

<Explanation of symbols for the main parts of the drawings>

1200: user behavior analysis system

1210: Log Collection Server

1211: user log receiver

1212: URL change section

1213: Service Tree Database (STDB)

1214: Service Tree Management System (STMS)

1220: floating field set generator

1230: flow field aggregation processing unit

Understanding the behavior of users in an online business is very important. Analysis of user behavior in terms of usability is an important source of user feedback for online services. In addition, from a marketing perspective, the user trend, that is, the above-described user behavior analysis, may be a measure of understanding the needs and demands of the users. In addition, the user behavior analysis may be a basis for solving the problem of resource allocation in terms of system management.

In the prior art, various performance indicators are used as a method for analyzing such user behavior. That is, the user behavior is analyzed through performance indicators such as page view, unique visitor, or query count, and most of the performance indicators are related to user behavior occurring on each page on the web. Can be obtained through counting.

1 is an example for explaining a method of analyzing user behavior in the prior art. That is, the prior art counts the number of occurrences each time the activity 102 on the website such as login, game, purchase or event participation of the users 101 occurs, such as game index, sales index, web usage index, etc. The index 103 can be measured and can be used to analyze the behavior of the users 101 as the performance indicator described above.

However, the conventional user behavior analysis method has a problem that it is difficult to understand the detailed activity on the user's website which is the cause of the performance indicator. For example, indices such as the game index, sales index, and web usage index described above can only be used to determine how behavior patterns change after a user spends all of their game money on a game site, or if a change in sales occurs on a particular purchase path. It is difficult to understand the detailed activities of the user. The difficulty in understanding the detailed activities of the user, that is, the difficulty in analyzing the user behavior, leads to a problem in that it is difficult to improve the service of the website through performance indicators.

The present invention proposes a new technology related to the user behavior analysis method and system in order to solve the above problems of the prior art.

The present invention provides a method and system for analyzing user behavior that can easily and conveniently perform time series analysis of user behavior by sorting user behavior by user and by time order through user logs.

The present invention provides a method and system for analyzing user behavior by analyzing the user's behavior in time series and analyzing the cause of the website's usage status, which is difficult to grasp previously, such as a purchase path and a member behavior pattern for a specific event. do.

The present invention provides a method and system for analyzing user behaviors by understanding the user behavior by analyzing the cause of the usage status of the website and thereby improving the service of the website.

The present invention solves the problem of traffic increase by using a plurality of collection servers that can be included in the log analysis server, user behavior analysis that can reduce the analysis time for the user behavior through distributed processing using a plurality of computing clusters Provide a method and system.

In order to achieve the above object and to solve the above-mentioned problems of the prior art, the user behavior analysis method according to an embodiment of the present invention, collecting the user log through a log collection server (LCS: Log Collection Server) Converting the user log into a floating field format to generate a floating field set, and processing the floating field set according to an input of a processing command for the floating field set. In this case, the flow field format may be a predetermined format of the flow field, and the flow field may be classified into a classification into a user identification field for identifying a user and a behavior recording field for recording the behavior of the user or the system. The attribute may be composed of an attribute name classifying the attribute and an attribute value defining user behavior.

According to one aspect of the invention, the step of collecting the user log through the log collection server, the step of receiving the user log via a web page provided by a web server and the URL included in the user log STDB (Service Tree Changing to an ST name (Service Tree name) using a DataBase. In this case, the step of receiving the user log through a web page provided by a web server, the user log using the user's web browser and a java script tag (java script tag) or in the case of a client, the log It may be a step of receiving the user log directly at the collection server.

According to another aspect of the present invention, the step of converting the user log into a flow field format to generate a flow field set, generating each flow field by converting each of the user logs into the flow field format; And arranging the flow field for each user in the order of time to generate the flow field set.

According to another aspect of the invention, the processing command is (1) a draw operator for extracting the flow field of the input time range, (2) a filter operator for extracting the flow field conforming to the input pattern And an aggr operator that measures the total or number of the attributes according to the input attributes, wherein the step of processing the floating field set in accordance with an input of a processing instruction for the floating field set includes: 1) extracting a corresponding flow field according to the draw operator or the filter operator, or (2) measuring the total or number of attributes corresponding to the set operator among the flow field set or the extracted flow field; have.

In another embodiment of the present invention, a user behavior analysis system, a log collection server for collecting user logs, a flow field set generation unit for generating a flow field set by converting the user log into a flow field format and the flow field set And a flow field aggregation processing unit for processing the flow field aggregation according to an input of a processing command for.

Hereinafter, various embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

The pattern of user behavior in web services, more specifically, the sequence listing the user behavior can be useful in many ways. For example, how the visitor accesses a given page on the site through the user behavior, what is the most frequently used path for the user to access the page, and the user purchased after purchasing the product 'A'. Time series information such as what the product is.

Since these statistical data are obtained by tracking each user's behavior, they are different from the performance indicators in the prior art described above, such as simply measuring the number of visitors entering each page. Such statistical data can be collected through user logs, that is, web logs.

2 is an example for explaining an overview of a user behavior analysis method according to the present invention.

Here, FIG. 2 illustrates user behavior 201 and user 'A' 202 and user 'B' defined according to an analysis purpose and scenario such as login, game, all-in, purchase, and logout in a web server providing a game. Represent each user action for 203. Referring to FIG. 2, the user 'A' 202 proceeds to play the game after logging in. The user's 'B' 203 performs the same action as the user 'A' 202. You can see that after the game money is all-in, you purchase the game money, play the game again, and log out later.

That is, if the user's actions can be known by user and by time using the user log in this way, the user's actions can be understood. In other words, it is difficult to understand the detailed behavior of the user simply by the number of logins, the number of purchases, or the purchase amount of the user. However, if the user can know not only various performance indicators 204 such as game index and sales index, but also the order of user behavior, Easily analyze and understand behavior.

To this end, it is necessary to know the user behavior of the user and the attributes at the time of the user behavior, and this information can be obtained through the user log.

3 is another example for explaining an overview of a user behavior analysis method according to the present invention.

Reference numeral 310 represents the time point at which four users purchased the game money. However, the reference numeral 310 simply indicates the time point at which the game money was purchased, and since the behavior before and after the purchase action is not known at all, only the amount of sales for each user, that is, the result-oriented data, can be obtained.

However, in the reference numeral 320, since the actions performed at each time point for four users, that is, time series information, can be obtained, before and after the purchase, for example, 'all in' and 'purchase' and then 'game' Situational user behavioral pattern can be obtained, and by analyzing it, it becomes possible to grasp the change between user behavior and property.

As such, when user behavior can be checked by user and according to time, it is possible to identify the cause of user behavior, and through this, purchase path analysis, which is difficult to grasp conventionally using performance indicators, and user behavior pattern analysis after a specific event occurs. Etc. are possible. Such a method and system will be described in more detail later with reference to FIGS. 4 to 13.

4 is a flowchart illustrating a user behavior analysis method according to an embodiment of the present invention.

In step S410, the user behavior analysis system collects user logs through a log collection server. Here, the user log includes all actions such as purchase of game money, entry into a game, and actions of a system generated by the web server due to the game money all-in, generated through events such as a click from a user and a keyboard input. It may include. That is, the user behavior analysis system may collect the user log without requiring a separate action from the user through the log collection server.

In this case, the user log may include a web server log generated by the web server, the step of collecting the user log through a log collection server, including the web server as the log collection server to the web server log It may be a step of generating. That is, the user behavior analysis system may include the web server and use the web server log generated by the web server as the user log.

Alternatively, the user behavior analysis system may allow the log collection server to collect the user log through the user's browser. In this case, as shown in FIG. 4, the user behavior analysis system may perform steps S411 to S413 to collect the user log.

In step S411, the user behavior analysis system manages and maintains an STDB in a service tree management system (STMS). Here, the STDB may be stored in association with the URL and the ST name corresponding to the URL. That is, the STDB includes various URLs associated with a website provided to the user by the user accessing through a web server, and stores the ST name associated with the URL in association with the URL.

In step S412, the user behavior analysis system receives the user log through a web page provided by a web server. That is, the user behavior analysis system accesses the web server through the user or the web server, and defines a meaningful behavior according to the analysis purpose and scenario, such as a login, logout, purchase of game money, or an all-in processing procedure. When a user action occurs, the generated user log may be received. In this case, the user behavior analysis system may receive the user log using a web browser of a user and a java script tag or, in the case of a client, the user log directly from the log collection server.

In step S413, the user behavior analysis system changes the URL included in the user log to the ST name using the STDB. This is to easily identify the path through which the user action occurs through the URL, and to remove unnecessary elements from the URL. In addition, since the analyst cannot easily grasp the webpage associated with the URL through the URL, the URL may be changed to the ST name so that the analyst can easily grasp the result of the user behavior analysis system.

In step S420, the user behavior analysis system converts the user log into a flow field format to generate a flow field set. In this case, the flow field format may be a predetermined format of the flow field, and the flow field may include a field name and a plurality of attributes.

In addition, the flow field may be classified into a user identification field for identifying a user and a behavior recording field for recording a behavior of the user or the system. Further, the user identification field may be classified into (1) a login field representing a user classified as a login name or (2) a persistent cookie field representing a user classified by a persistent cookie assigned to each browser according to the field name. The action history field may include (1) a page view field indicating a user's visit to a particular page, (2) a click field indicating a click on a user's specific link, or (3) an action field indicating a system's action on the user. Can be classified as At this time, it will be apparent to those skilled in the art that the user identification field and the action recording field may be classified into more detailed fields as necessary.

The attribute may be composed of an attribute name classifying the attribute and an attribute value defining user behavior.

In addition, the flow field may include the time of occurrence of user behavior included in the user log as the attribute. That is, every flow field may include the time of occurrence of user action associated with the flow field.

The step of generating the flow field set (S420) will be described in more detail using the flowchart of FIG. 6 and the examples of FIGS. 7 and 8.

In step S430, the user behavior analysis system processes the flow field set according to an input of a processing command for the flow field set. The floating field set may be divided and merged as needed, which will be described in more detail with reference to FIG. 9. The processing instructions may also include (1) typical analysis queries, (2) pre-selected operators or analysis scripts.

First, if the processing instruction is a typical analysis query, in step S430 of processing the flow field set, the user behavior analysis system includes a time period, pattern, and statistics included in the analysis query. The corresponding flow field may be extracted according to an aggregation function or grouping criteria, or the processing result of the attribute included in the flow field may be provided.

Secondly, the processing instruction is the pre-selected operator, that is, (1) a draw operator for extracting a flow field of the input time range, and (2) a filter for extracting a flow field that matches the input pattern. ) And an aggr operator that measures the sum or the number of the attributes according to the input attributes, in step S430 the user behavior analysis system performs (1) in accordance with the draw operator or the filter operator. A corresponding flow field may be extracted or (2) the total or number of attributes corresponding to the set operator may be measured among the flow field set or the extracted flow field.

Finally, when the processing command is the analysis script, in step S430, the user behavior analysis system according to the input analysis script is input according to the time range, the pattern, or the attribute processing command. The field may be extracted or (2) the sum or the number of attributes corresponding to the processing instruction may be measured among the attributes included in the flow field set or the extracted flow field.

As described above, a method of processing the floating field set will be described in more detail with reference to FIG. 10.

5 is an example for explaining a user log collection method according to the present invention.

The user may request a web page to the web server 502 through the browser 501, and the web server 502 may transmit a web page including the JavaScript tag to the browser 501. That is, the web server 502 may set a login persistent cookie in the browser 501 through the JavaScript tag, and the user behavior analysis system may set the URL through the URL of the log collection server 503 included in the login persistent cookie. The user log can be received.

Thereafter, when a user action occurs through the web page, the log collection server 503 uses the URL included in the login persistent cookie in the JavaScript tag to transmit the user log transmitted through the browser 501. Can be received. In this case, when the user is not logged in, the log collection server 503 may identify the user through a method of identifying the browser 501, and for this purpose, a persistent cookie may be set in the browser 501. Can be. That is, the persistent cookie may be used to identify a user who has not logged in through the browser 501.

6 is a flowchart illustrating a method of generating a floating field set according to an embodiment of the present invention. As shown in FIG. 6, step S601 and step S602 may be included in step S420 described with reference to FIG. 4.

In step S601, the user behavior analysis system converts each of the user logs into the flow field format to generate a respective flow field. That is, the user log includes the user behavior, and the user behavior analysis system may convert the user log into the floating field format that the user behavior analysis system can use. As described above, when converting the user log to the floating field format because the floating field format is a predetermined format of the floating field, the user log is converted as the floating field. In other words, it is possible to create the flow field.

In step S602, the user behavior analysis system creates the flow field set by arranging the flow field in order of time for each user. Each of these flow fields is not meaningful for the flow field itself, so that the flow field set is aligned with the time of occurrence of the user action included in the flow field and the user. Such a flow field set and the flow field will be described in more detail with reference to FIGS. 7 and 8.

7 is an example for explaining the flow field aggregation and flow field according to the present invention. FIG. 7 illustrates a plurality of flow fields arranged by user 701 and in the order of occurrence time 702 of the user action. In this case, the floating field may include a field name indicating the type of field and an attribute according to the user's behavior. In addition, the attribute may be composed of an attribute name and an attribute value, or may be composed of a plurality of attribute names and attribute values.

That is, the field name 'L' 704 included in the floating field 703 in the example of FIG. 7 is a field for classifying a user by a login name, and a user log generated when the user logs in to a website through a browser. Can be generated via In addition, the attribute 'id = xxxxxx ts = 1163168755' 705 may be composed of the attribute name 'ts' 706 and the attribute value '1163168755' 707. In the example of FIG. 7, a flow field 703 with two attributes is shown. In this case, the first attribute may mean that the login ID of the user is 'xxxxxx', and the second attribute may mean that the generation time of the login is '1163168755'.

In this case, since one row of the floating field set is a user action for one user in order of time, the first element of the row, that is, the first attribute, may be an attribute for identifying the user. .

8 is an example of a flow field set, in accordance with the present invention. Here, reference numeral 800 denotes a plurality of flow fields having a plurality of attributes. That is, the example of FIG. 8 includes user behavior from the moment a user having a login ID 'xxxxxx' logs in to the game server to the moment of logging out.

9 is an example for explaining the splitting and merging between flow field sets according to the present invention.

As described above, the floating field set is a list of user behaviors, which is a set of information corresponding to each user and in chronological order. When analyzing the flow field set, there may be a case where a user's behavioral pattern for a week or a month or more is to be analyzed. In this case, the flow field sets should be created and collected for the user for a period of more than one month, and for the sake of storage or technical reasons the flow field for any period of time rather than creating this whole in one flow set. It is convenient to create a set dynamically.

In other words, by generating a plurality of floating field sets for a user in a short time unit and combining the floating field sets using a join operator or a union operator as necessary, the user for a desired time unit. You can get a set of floating fields for the user behavior of. For example, the flow field set 901 and 902 can be merged through the join operator to generate the flow field set 903, or the flow field set 901 and 903 can be merged via the union operator to flow field set 905 may be generated. That is, merged flow field sets 903 and 905 may be generated by merging between flow field sets 901, 902, or 904.

On the contrary, it is also possible to separate large sets of flow fields into smaller units of flow fields.

The user behavior analysis system may comprise (1) a processing instruction entered in the form of a typical analysis query, (2) a processing instruction comprising a pre-selected operator for easily processing the typical analysis query, or (3) the pre-selected operator. In addition, the floating field set may be processed by analyzing a processing instruction using an analysis script that can be expressed in the form of the typical analysis query. The relationship between the typical analysis query, the predetermined operator and the analysis script will be described in more detail with reference to FIG. 11.

10 is an example of processing a flow field set in accordance with the present invention.

First, an example of a method of processing the floating field set through the processing instruction including the predetermined operator, that is, the draw operator, the filter operator and the set operator described above in the user behavior analysis system will be described.

First, when the draw operator 1001 is input, the flow field set 1002 may extract a record of a user's behavior in a time range included in the draw operator 1001, that is, a flow field corresponding to the time range. In addition, when the filter operator 1003 is input, a flow field matching the pattern included in the filter operator 1003 may be extracted from the extracted flow field. Finally, for the set operator 1004, the user behavior analysis system may provide an analyst for measuring the total or number of attributes corresponding to the attributes included in the set operator in the flow field extracted by the filter operator 1003. Can be.

In this case, for example, when only the draw operator 1001 and the filter operator 1003 are input according to the input operator as illustrated in FIG. 10, the flow field extracted after performing the filter operator 1003 is transmitted to the analyst. It is also possible to provide. In addition, it is also possible to perform the same operator in different conditions in succession or repeat the same operator in the same condition. That is, FIG. 10 is an example of processing a floating field set, and various modifications may be made according to values of an order of an operator input from an analyst and a time range or pattern included in the operator.

11 is an example for explaining a processing instruction according to the present invention.

As described above, the user behavior analysis system may comprise (1) a processing instruction entered in the form of a typical analysis query, (2) a processing instruction comprising a pre-selected operator to easily process the typical analysis query or (3) the It is possible to process the floating field set through any one of the processing instructions using an analysis script that can be expressed in the form of the typical analysis query, while incorporating pre-defined operators.

Reference numeral 1110 is an example of a typical analysis query. This typical analytic query uses the pre-selected operator described above, i.e., a draw operator that can be expressed as 'draw', and a process for time ranges, and a filter operator that can be expressed as 'regex'. Processing can be performed on aggregate functions or grouping criteria, respectively, through the set operator, which can be expressed as 'aggr'.

Reference numeral 1120 denotes an example of processing the typical analysis query through a draw operator 1121, a filter operator 1122, and a set operator 1123. In this case, reference numerals 1124 to 1126 denote processing results according to the draw operator 1121, the filter operator 1122, and the set operator 1123, respectively.

Reference numeral 1130 is an example of expressing the typical analysis query by the analysis script described above. The analysis script is designed to be easily recognized by a person as including the predetermined operator, which is designed to easily process the typical analysis query, but is expressed in the form of the typical analysis query beyond the mechanical framework of the predetermined operator. It is a script.

That is, the user behavior analysis system may process the floating field set through a processing instruction including any one of the typical analysis query, the predetermined operator, or the analysis script described above.

As described above, using the user behavior analysis method according to the present invention, time series analysis of user behavior can be easily and conveniently performed by aligning user behaviors by user and according to the order of time through the user log. By thermally analyzing, it is possible to analyze the causes of the use of the website, which was previously difficult to grasp, such as purchase paths and membership behavior patterns for specific events.

In addition, by analyzing the causes of the usage of the website, the user behavior can be understood, thereby improving the service of the website, and increasing traffic by using a plurality of collection servers that the log analysis server can include. In addition, the analysis time of the user's behavior can be shortened by solving the problem of distributed processing using a plurality of computing clusters.

12 is a block diagram illustrating an internal configuration of a user behavior analysis system according to another embodiment of the present invention. As illustrated in FIG. 12, the user behavior analysis system 1200 may include a log collection server 1210, a flow field set generation unit 1220, and a flow field set processing unit 1230.

The log collection server 1210 collects user logs. Here, the user log includes all actions such as purchase of game money, entry into a game, and actions of a system generated by the web server due to the game money all-in, generated through events such as a click from a user and a keyboard input. It may include. That is, the log collection server 1210 may collect the user log without requiring a separate action from the user. In this case, as shown in FIG. 12, the log collection server 1210 may include a user log receiver 1211, a URL changer 1212, an STDB 1213, and an STMS 1214 to collect the user log. have.

The user log receiver 1211 receives the user log through a web page provided by a web server. That is, the user log receiving unit 1211 is defined according to the analysis purpose and scenarios such as access to the web server through the user or the web server, such as a login, logout, purchase of game money, or an all-in processing procedure. When the user action occurs, the generated user log may be received. In this case, the user log receiver 1211 may receive the user log using a web browser and a Javascript tag of the user or, in the case of a client, the user log directly from the log collection server.

The URL changing unit 1212 changes the URL included in the user log to the ST name using the STDB 1213. This is to easily identify the path through which the user action occurs through the URL, and to remove unnecessary elements from the URL. In addition, since the analyst cannot easily grasp the webpage associated with the URL through the URL, the URL may be changed to the ST name so that the analyst can easily grasp the result of the user behavior analysis system.

The STMS 1214 manages and maintains the STDB 1213. Here, the STMS 1214 may store the URL and the ST name corresponding to the URL in association with each other. That is, the STDB 1213 includes various URLs associated with a website provided to the user by the user accessing through a web server, and stores the ST name associated with the URL in association with the URL.

The floating field set generation unit 1220 converts the user log into a floating field format to generate a floating field set. In this case, the flow field format may be a predetermined format of the flow field, and the flow field may include a field name and a plurality of attributes.

In addition, the flow field may be classified into a user identification field for identifying a user and a behavior recording field for recording a behavior of the user or the system. Further, the user identification field may be classified into (1) a login field representing a user classified as a login name or (2) a persistent cookie field representing a user classified by a persistent cookie assigned to each browser according to the field name. The action history field may include (1) a page view field indicating a user's visit to a particular page, (2) a click field indicating a click on a user's specific link, or (3) an action field indicating a system's action on the user. Can be classified as

The attribute may be composed of an attribute name classifying the attribute and an attribute value defining user behavior.

In addition, the flow field may include the time of occurrence of user behavior included in the user log as the attribute. That is, every flow field may include the time of occurrence of user action associated with the flow field.

The flow field aggregation generator 1220 may include a flow field generator 1221 and an alignment unit 1222 as illustrated in FIG. 12.

The floating field generator 1221 generates each floating field by converting each of the user logs into the floating field format. That is, the user log includes the user behavior, and the user behavior analysis system may convert the user log into the floating field format that the user behavior analysis system can use. As described above, when converting the user log to the floating field format because the floating field format is a predetermined format of the floating field, the user log is converted as the floating field. In other words, it is possible to create the flow field.

The alignment unit 1222 generates the flow field set by arranging the flow fields for each user in the order of time. Each of these flow fields does not have much meaning as the flow field itself, so that the flow field set is arranged based on the time of occurrence of the user action included in the flow field and the user.

The flow field aggregation processing unit 1230 processes the flow field aggregation according to an input of a processing command for the flow field aggregation. The floating field set may be divided and merged as needed, and the processing instruction may include (1) a typical analysis query, (2) a predetermined operator or an analysis script.

First, when the processing command is a typical analysis query, the flow field set processing unit 1230 which processes the flow field set may have a corresponding flow according to the time range, pattern, statistical function, or group classification criteria included in the analysis query. The field may be extracted or the processing result of the attribute included in the floating field may be provided.

Second, the processing instruction is the pre-selected operator, that is, (1) a draw operator for extracting a flow field of the input time range, (2) a filter operator for extracting a flow field conforming to the input pattern, and (3) In the case of including a set operator for measuring the total or the number of the attribute according to the input attribute, the flow field set processing unit 1230 (1) extracts the corresponding flow field in accordance with the draw operator or the filter operator or ( 2) The total or number of attributes corresponding to the set operator may be measured among the flow field set or the extracted flow field.

Finally, when the processing command is the analysis script, the flow field set processing unit 1230 extracts a corresponding flow field according to the time range, the pattern, or the attribute processing command according to the input analysis script. Or (2) the total or number of attributes corresponding to the processing instruction among the attributes included in the flow field set or the extracted flow field.

In addition, the log collection server may be configured with a plurality of collection servers for distributed processing of the data in the user behavior analysis system 1200, the user log may be collected distributed in the plurality of collection servers. In this case, the flow field aggregation processing unit 1230 may include a plurality of computing clusters (not shown) for processing the flow field aggregation.

That is, the flow field for the same user may be processed according to the processing instruction in the designated computing cluster among the plurality of computing clusters, and the flow field aggregation processing unit 1230 may perform processing according to the processing instructions in the plurality of computing clusters. It may further include a commander for integrating the distributed processing result. As described above, the dispersion process will be described in more detail with reference to FIG. 13.

Fig. 13 is an example for explaining a method for distributing data according to the present invention.

Increasing traffic usage per unit time may cause overloading of the log collection server or loss of some user logs if too many user logs are entered within the unit time. Increasing the amount of data that has to be recorded, analyzed, and the like has a problem that leads to an increase in time of the user behavior analysis.

Thus, a structure such as the example of FIG. 13 can be used to process the user action in time.

Reference numeral 1300 may solve the problems caused by the traffic increase and the data increase by distributing user logs and the data by increasing the number of log collection servers and computing units, that is, the computing cluster. Shows. For example, if the user log spikes about 10 times only during a certain time period or a specific event period, such as 0:00 AM, and there is no significant difference in the amount of user logs throughout the day, the number of log collection servers is left alone. Increasing may solve the problem of traffic increase.

That is, the log collection server may be configured with a plurality of collection servers, and the user log may be collected in a distributed manner to the plurality of collection servers. In addition, since the floating field set includes at least one floating field generated through the user log and classified by the user, the floating field for the same user may be assigned to the designated computing cluster among a plurality of computing clusters processing the floating field set. The problem can be solved by processing in accordance with the processing instruction.

More specifically, the user behavior analysis system distributes and records the user logs to a plurality of collection servers, each of which operates in the same manner as the log collection server, and generates a uniform distribution such as a hash function. By designating the computing cluster for each user, user behavior for one user can be handled through one computing cluster. As such, all data for one user is processed in one computing cluster, so communication with an external host is unnecessary, and the time for processing the floating field set is reduced because the floating field set is processed by a plurality of computer clusters. do. In addition, it is possible to solve the traffic problem by using the plurality of collection servers.

For simple counting or user counting operations, counting the number by each computing cluster and adding the number to the corresponding group can yield the total number, which is the number of counting and summating operations exchanged. It comes from the property of keeping the law of law and union. In other words, in case of obtaining unique visitor in the existing method, it is necessary to maintain the set of all users to check whether the same user is counted, but to maintain the set by storing each user in each computing cluster. There is no need to effect.

In addition, the operations according to the draw operators, filter operators, and set operators described above may all be executed in the computing cluster. In this case, the operation according to the set operator may be performed once more by using the commander shown in reference numeral 1300 to merge the results of each computing cluster.

As described above, when using the user behavior analysis system according to the present invention, time series analysis of user behavior can be easily and conveniently performed by sorting user behaviors by user and according to the order of time through the user log. By thermally analyzing, it is possible to analyze the causes of the use of the website, which was previously difficult to grasp, such as purchase paths and membership behavior patterns for specific events.

In addition, by analyzing the causes of the usage of the website, the user behavior can be understood, thereby improving the service of the website, and increasing traffic by using a plurality of collection servers that the log analysis server can include. In addition, the analysis time of the user's behavior can be shortened by solving the problem of distributed processing using a plurality of computing clusters.

Embodiments according to the present invention can be implemented in the form of program instructions that can be executed by various computer means can be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

In the present invention as described above has been described by specific embodiments such as specific components and the like embodiments and drawings, but this is only provided to help a more general understanding of the present invention, the present invention is limited to the above embodiments In other words, various modifications and variations are possible to those skilled in the art to which the present invention pertains.

Therefore, the spirit of the present invention should not be limited to the described embodiments, and all the things that are equivalent to or equivalent to the claims as well as the following claims will belong to the scope of the present invention. .

According to the present invention, time series analysis of user behavior can be easily and conveniently by sorting user behavior by user and by time order through the user log.

According to the present invention, by analyzing the user's behavior in time series, it is possible to analyze the cause of the usage status of the website, which is difficult to grasp previously, such as a purchase path and a member behavior pattern for a specific event.

According to the present invention, it is possible to understand the user behavior by analyzing the cause of the use status of the website, thereby improving the service of the website.

According to the present invention, it is possible to solve the problem of traffic increase by using a plurality of collection servers that can be included in the log analysis server, and to shorten the analysis time for user behavior through distributed processing using a plurality of computing clusters.

Claims (25)

In the user behavior analysis method, Collecting user logs through a log collection server (LCS); Converting the user log into a floating field format to generate a floating field set; And Processing the floating field set in accordance with an input of a processing instruction for the floating field set. User behavior analysis method comprising a. The method of claim 1, The floating field format is a predetermined format of the floating field, The flow field comprises a field name and a plurality of attributes. The method of claim 2, The floating field is classified into a classification into a user identification field for identifying a user and a behavior recording field for recording the behavior of the user or system, And the attribute comprises an attribute name classifying the attribute and an attribute value defining user behavior. The method of claim 2, And the flow field includes, as the attribute, an occurrence time of user behavior included in the user log. The method of claim 1, The step of converting the user log into a floating field format to generate a floating field set, Converting each of the user logs into the flow field format to generate a respective flow field; And Generating the floating field set by arranging the floating fields in order of time for each user. User behavior analysis method comprising a. The method of claim 1, The processing instruction comprises a typical analysis query, The step of processing the flow field set in accordance with an input of a processing command for the flow field set, Extract the corresponding flow field according to a time period, pattern, aggregation function, or grouping criteria included in the analysis query, or extract an attribute of the flow field. User behavior analysis method that provides the results of the processing. The method of claim 1, The processing instruction may include (1) a draw operator for extracting a flow field of an input time range, (2) a filter operator for extracting a flow field according to an input pattern, and a property of the attribute according to an input attribute. Includes an aggr operator that measures total or count, The step of processing the flow field set in accordance with an input of a processing command for the flow field set, (1) a user action of extracting a corresponding floating field according to the draw operator or the filter operator, or (2) measuring the total or number of attributes corresponding to the set operator among the floating field set or the extracted floating field; Analytical Method. The method of claim 1, The processing instruction comprises an analysis script consisting of a time range, pattern or attribute processing instruction, The step of processing the flow field set in accordance with an input of a processing instruction for the flow field set, According to the analysis script inputted according to the time range, the pattern or the attribute processing command (1) extract the corresponding flow field, or (2) the processing from among the attributes of the flow field set or the extracted flow field; A method of analyzing user behavior that measures the sum or count of attributes corresponding to a command. The method of claim 1, The above step of collecting the user log through the log collection server, Receiving the user log through a web page provided by a web server; And Changing the URL included in the user log to an ST tree name using a service tree database (STDB); User behavior analysis method comprising a. The method of claim 9, The step of receiving the user log through a web page provided by a web server, Receiving the user log using a user's web browser and java script tag (java script tag) or, in the case of a client, the user behavior analysis method directly receiving the user log from the log collection server. The method of claim 9, The STDB is stored in association with the URL and the ST name corresponding to the URL, The above step of collecting the user log through the log collection server, Managing and maintaining the STDB in the Service Tree Management System (STMS) User behavior analysis method further comprising. The method of claim 1, The user log includes a web server log generated by the web server The above step of collecting the user log through the log collection server, And generating the web server log by including the web server as the log collection server. The method of claim 1, The log collection server is composed of a plurality of collection servers, The user log is collected and distributed to the plurality of collection server user behavior analysis method. The method of claim 13, The flow field set includes at least one flow field generated through the user log and classified for each user, The flow field for the same user is processed according to the processing instruction in the designated computing cluster of the plurality of computing clusters processing the flow field set. A computer-readable recording medium in which a program for executing the method of any one of claims 1 to 14 is recorded. In the user behavior analysis system, A log collection server collecting user logs; A flow field set generation unit for converting the user log into a flow field format to generate a flow field set; And Flow field set processing unit for processing the flow field set in accordance with the input of the processing command for the flow field set User behavior analysis system comprising a. The method of claim 16, The floating field format is a predetermined format of the floating field, The flow field comprises a field name and a plurality of attributes. The method of claim 17, The floating field is classified into a classification into a user identification field for identifying a user and a behavior recording field for recording the behavior of the user or system, And the attribute comprises an attribute name classifying the attribute and an attribute value defining user behavior. The method of claim 16, The floating field set generation unit, A flow field generator for converting each of the user logs into the flow field format to generate respective flow fields; And Flow field alignment unit for generating the flow field set by arranging the flow field by the user in order of time User behavior analysis system comprising a. The method of claim 16, The processing instruction may include (1) a draw operator for extracting a flow field of an input time range, (2) a filter operator for extracting a flow field according to an input pattern, and a property of the attribute according to an input attribute. Includes an aggr operator that measures total or count, The floating field aggregation processing unit, (1) a user action of extracting a corresponding flow field according to the draw operator or the filter operator, or (2) measuring the total or number of attributes corresponding to the set operator among the flow field set or the extracted flow field; Analysis system. The method of claim 16, The log collection server A user log receiver configured to receive the user log through a web page provided by a web server; And URL changing unit for changing the URL included in the user log to the ST name using STDB User behavior analysis system comprising a. The method of claim 21, The STDB is stored in association with the URL and the ST name corresponding to the URL, The log collection server, An STMS for managing and maintaining the STDB; User behavior analysis system further comprising. The method of claim 16, The log collection server is composed of a plurality of collection servers, The user log is collected and distributed to the plurality of collection server user behavior analysis system. The method of claim 23, wherein The flow field set includes at least one flow field generated through the user log and classified for each user, The floating field set processing unit includes a plurality of computing clusters for processing the floating field set. The method of claim 24, The floating field for the same user is processed according to the processing instruction in the designated same computing cluster among the plurality of computing clusters, The floating field aggregation processing unit, Commander for integrating the results distributed in accordance with the processing command in the plurality of computing clusters User behavior analysis system further comprising.

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