CN106250397B - User behavior characteristic analysis method and device - Google Patents
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Abstract
The invention provides a method and a device for analyzing user behavior characteristics. The method comprises the following steps: the log file is collected, then the log file is analyzed to obtain log data, structural reorganization is carried out on the log data according to information of the log data to obtain reorganized log data, and finally user behavior characteristics are obtained according to the reorganized log data, so that the user behavior characteristics are fed back to relevant personnel, and service providers are helped to improve service quality in a more targeted mode.
Description
Technical Field
The invention relates to the field of computers, in particular to a method and a device for analyzing user behavior characteristics.
Background
The grid environment forms a virtual computing cluster by integrating, managing and scheduling distributed heterogeneous computing resources, so that the utilization efficiency of high-performance computing resources can be improved, and the reliability of user services can be improved.
The super computing environment of Chinese academy of sciences is a three-layer architecture grid computing environment, and adopts scientific computing grid software SCE to provide high-quality and high-performance computing service for users. The SCE software is a grid middleware through which a user can use all resources in the entire grid environment. The SCE software has two use modes of an internet entrance Portal and a command line, and can complete the work of submitting, modifying, inquiring, downloading result files and the like of jobs. The SCE middleware may generate SCE log files for recording various operations of a user in a supercomputing environment. That is, in a supercomputing environment, a system log and SCE log are generated.
However, the prior art only analyzes the system logs generated in the super computing environment, so that the acquired user behavior characteristics are inaccurate, and all the user logs of one or several past stages must be observed first in order to analyze the characteristics of all the user behaviors in the grid environment. Making the analysis of such a very large-scale log record a relatively complex and time-consuming task.
Disclosure of Invention
A grid environment log analysis framework (English) system is used as a log analysis system of a super computing environment, and behavior characteristics of a user are obtained through analysis, structural reorganization and statistical analysis of SCE logs, so that user experience is improved.
In a first aspect, a method for analyzing user behavior characteristics is provided, and the method is applied in a super computing environment, and includes: the method comprises the steps of collecting log files, analyzing the log files to obtain log data, performing structural reorganization on the log data according to information of the log data to obtain reorganized log data, and obtaining user behavior characteristics according to the reorganized log data so as to feed back the user behavior characteristics.
In an optional implementation, according to the information of the log data, performing structural reorganization on the log data to obtain reorganized log data, specifically: according to the information of the log data, carrying out structural reorganization on the log data by taking the conversation of the user as a unit to obtain reorganized log data.
In an alternative implementation, the reorganization log data has a three-level structure, wherein the three-level structure includes a data level, a session level, and a single operation level.
In an optional implementation, before obtaining the user behavior feature, the method further includes: extracting a characteristic value of the user session frequency according to the recombined log data; and acquiring the user behavior characteristics according to the characteristic values.
In an alternative implementation, the characteristic values include: the first characteristic value related to the conversation number of each day of the user, the second characteristic value related to the actual login days of the user and the total days, and the third characteristic value related to the actual login span of the user and the total days.
In another aspect, an apparatus for analyzing user behavior characteristics is provided, the apparatus being applied in a super computing environment, the apparatus comprising: the device comprises an acquisition unit, an analysis unit, an acquisition unit and a feedback unit. The acquisition unit is used for acquiring log files. The analysis unit is used for analyzing the log file acquired by the acquisition unit to acquire log data. The structure restructuring unit is used for carrying out structure restructuring on the log data according to the information of the log data acquired by the analysis unit to acquire restructured log data. The obtaining unit is used for obtaining the user behavior characteristics according to the restructuring log data obtained by the structure restructuring unit. The feedback unit is used for feeding back the user behavior characteristics.
In an alternative implementation, the structural reorganization unit is specifically configured to: according to the information of the log data, carrying out structural reorganization on the log data by taking the conversation of the user as a unit to obtain reorganized log data.
In an alternative implementation, the restructuring log data obtained by the structure restructuring unit has a three-level structure, wherein the three-level structure includes a data level, a session level and a single operation level.
In an alternative implementation, the apparatus further comprises: and an extraction unit. And the extraction unit is used for extracting the characteristic value of the user session frequency according to the recombined log data. The obtaining unit obtains the user behavior characteristics according to the characteristic value.
In an alternative implementation, the feature values extracted by the extraction unit include: the first characteristic value related to the conversation number of each day of the user, the second characteristic value related to the actual login days of the user and the total days, and the third characteristic value related to the actual login span of the user and the total days.
In an alternative implementation, the apparatus may further comprise a storage unit for storing applications and data for use by the above-mentioned unit.
The LARGE can analyze the behavior characteristics of the super computing environment user by analyzing and reconstructing the SCE log, and helps the service provider to improve the service quality more pertinently
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a grid environment log analysis framework system according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for analyzing user behavior characteristics according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of reorganizing log data according to an embodiment of the present invention;
fig. 4 is a device for analyzing user behavior characteristics according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The method for analyzing the user characteristics provided by the invention is applied to the LARGE system shown in FIG. 1. The LARGE system is a log processing and analyzing system deployed in a super computing environment, and various results are obtained by collecting, sorting and analyzing logs of a computing cluster, so that system maintenance personnel are helped to monitor the system condition, find out operation errors or improper operation of users, and improve service quality.
In fig. 1, the source system 100 may include a log collection module 110, a log analysis module 120, and a result feedback module 130.
And the log collection module 110 is used for transmitting all logs generated in the grid environment to the equipment of the log analysis module. The log collection module 110 may be adapted to any suitable data collection method, such as the script system of Facebook web Facebook, or the Flume system provided by Cloudera.
The log analysis module 120 is configured to perform log screening, classification, preprocessing, parsing and reconstruction, statistical information, application analysis, and the like on the collected logs, and generate an analysis result. The log analysis module 120 may be any suitable data analysis means, such as Chukwa, a data collection and analysis system in a Hadoop project open source distributed system.
A log analysis module 130, configured to arrange the analysis results generated by the log analysis module 120 into related reports, visual charts or corresponding display programs more helpful for the related personnel to understand, or formulate response rules for some pattern log, such as sending an alarm mail or automatically executing a response program.
The LARGE system provides a method of analyzing user characteristics based on two different types of system logs and SCE logs in a supercomputing environment. The following description will take the analysis of SCE logs as an example.
The SCE log is a log generated by SCE software and records the operation of a user in a super computing environment, and is divided into an SCE log for recording cluster job information and command line user operation and a Portal log for recording the operation of submitting and managing computing jobs by the user through a webpage. By performing statistical analysis on the SCE log, we can obtain various behavior characteristics of the environmental user, including: the use habits, the use frequency, the use conditions of the SCE commands and the like of the users are convenient for more targeted communication with the users, so that more suitable service strategies are adopted for the users of different types, and possible defects and deficiencies in the super computing environment are found, thereby improving the overall service quality of the super computing environment.
Fig. 2 is a flowchart of a method for analyzing user characteristics according to an embodiment of the present invention. In a super computing environment, the execution subject of the method may be a source system, as shown in fig. 2, the method comprising:
step 210, collecting log files.
The LARGE system collects log files generated by all devices in the super computing environment, wherein the collected log files can be different/same types and different/same contents, and the log files can comprise system logs and SCE logs.
Step 220, analyzing the log file to obtain log data.
The LARGE system analyzes the collected log file, analyzes the attribute information of the log file and acquires log data.
The attribute information of the SCE log may include necessary information such as log generation time, host, user name, source IP, number of session to which the operation belongs, operation type, and various operation parameters, and each piece of information may be separated by a space or a custom separator to facilitate extracting each piece of information and recombining the information into structured data to obtain log data. For example, the SCE log is of the form:
"Log time | host | Log type | Session ID | user name | Source IP | operation ID | operation type | operation encoding | operation parameter"
It should be noted that, since the cluster job information in the SCE log is consistent with the content characteristics of the middleware log of the command line user operation and the Portal log of the operation of recording the user submitting and managing the computing job through the web page, the two may use the same processing steps and analysis method, wherein the consistency of the content characteristics may be understood as that the two have fixed formats, compact contents, and are easy to read by the computer.
And step 230, performing structural reorganization on the log data according to the information of the log data to obtain reorganized log data.
Specifically, the merge system may perform structural reorganization on the log data by taking a session of the user as a unit according to the information of the log data, and obtain reorganized log data. The reorganization log data may have a three-level structure of a data level, a session level, and a single operation level.
The LARGE system stores session data information of all users, such as user names, session times, operation information, and the like, in the data level. The session is a process of complete operation (a process from a beginning operation to an ending operation), the LARGE system classifies the session of the user according to the user name, stores the classified user session information in a session level, classifies the operation by taking the session as a unit, acquires each single operation in the session and stores the single operation in the single operation level so as to analyze each single operation of each session.
As shown in fig. 3, the reorganization log data has a three-level structure of data level-session level-single operation level, wherein the data level includes two users, user a and user B, 3 sessions are implemented totally, and 3 all operations of user a and user B in the session, which may be query operation and exit operation. The session level classifies the user's session according to the user name, for example, the user a is classified according to the user name: user A-session 1-query operation, quit operation, user A-session 2-quit operation, user B-session 3-query operation, quit operation. Classifying the operation by taking the conversation as a unit, acquiring each single operation in the conversation, storing the single operation in a single operation level, and dividing the operation into the following steps by taking the conversation as a unit: the method comprises the following steps of user A-session 1-query operation, user A-session 1-quit operation and user A-session 2-quit operation, wherein a user B is divided into the following steps according to the session as a unit: user B-session 3-query operation and user B-session 3-logout operation.
The log data of the same session are identical in values of session ID, host address, user name, IP and other information, and the log data of the same session are structurally recombined by the LARGE system, namely, the session is set as an independent structure, so that the data storage amount is reduced, and statistics and analysis on user operation habits are facilitated by the session.
And 240, acquiring user behavior characteristics according to the recombined log data.
By extracting the reorganized log data, the LARGE system can integrate and label the related information by taking the user name as a category, so that the user behavior characteristics, namely the basic statistical result about the use condition of the user can be obtained. The user behavior characteristics are to summarize and summarize behavior laws embodied by a single or multiple users in a certain time period, and analyzing the user behavior characteristics is helpful for understanding the use habits of the users, understanding the user requirements and discovering defects in user services.
Optionally, before obtaining the user behavior feature, the merge system may extract a feature value of the user session frequency according to the recombined log data, and then obtain the user behavior feature according to the feature value to classify the user.
Specifically, the LARGE system extracts some characteristic values capable of reflecting the conversation frequency of the user according to the recombined log data information, and classifies the user by using a clustering algorithm. The frequency of user sessions refers to the number of times a user connects to the super computing environment and initiates a session within a certain period of time. It should be noted that the conversation frequency of the user reflects the requirement of the user for the environment computing resource to a great extent, and the service party can provide different service policies correspondingly according to the requirements of the user at different degrees.
Further, the characteristic value of the user session frequency may include: the first characteristic value related to the conversation number of each day of the user, the second characteristic value related to the actual login days of the user and the total days, and the third characteristic value related to the actual login span of the user and the total days. According to the size of 3 characteristic values of each user, the LARGE system can obtain the behavior characteristics of the corresponding user by using a clustering algorithm, so that the corresponding user is classified. The clustering algorithm belongs to the prior art, and is not described herein.
In one example, the LARGE system analyzes the frequency of usage of super-computation users in a time period P, where the total number of days is NPUser set { U1,U2,…,UnAnd f, if the total number is n, the session number of each user in the time period P is S1,S2,…,SnAnd a specific number of sessions S per day for user u (u { x |1 ≦ x ≦ n })u1,Su2,…,SuNP. Since the user does not log in the super computing environment every day for calculation, we also need to count the actual usage days NA of each user1,NA2,…,NAnAnd the span of the user's actual login time (i.e., the number of days the user takes from the first session to the last session during P period) ND1,ND2,…,NDn。
For a user u, the following 3 eigenvalues can be obtained: the LARGE system performs function mapping on the average value of the number of sessions initiated by the user every day, the standard deviation of the average value of the number of sessions initiated by the user every day, the average value of the sessions initiated by the user every day when the user actually logs in and the standard deviation of the average value of the sessions initiated by the user every day when the user actually logs in, and obtains a first characteristic value as follows:
the LARGE system obtains a second characteristic value R according to the ratio of the actual login days of the user to the total daysA＝NAu/NP。
The LARGE system obtains a third characteristic value R according to the ratio of the actual login span of the user to the total daysD＝NDu/NP。
If the average daily session of the first type of users is the most, and the span is the longest, it means that the first type of users are persistent users in the super computing environment, and the demand for computing resources is relatively high. If the average number of sessions per day of the second type of users is less and the span is the same as that of the first type of users, the second type of users are persistent users in the super computing environment, but the demand of computing resources is general. If the average number of sessions per day of the third type of users is close to that of the second type of users, but the actual login days are less, the average number of sessions per day indicates that the type of users do not have continuous demands on the demand of the computing resources. If the number of login days of the fourth type of user is the minimum, the fourth type of user is not a fixed user in the super computing environment.
It can be understood that, by using the above analysis method of the resource system, the user can be provided with the targeted service after being classified. Such as giving preference to computing resources for a first class of users, and revisiting a fourth class of users to assess what problems with the supercomputing environment need improvement, etc.
And step 250, feeding back the user behavior characteristics.
The LARGE system can display the acquired user behavior characteristics to related personnel in the form of related reports, visual charts or display programs, or the LARGE system can make an alarm mail aiming at a certain pattern log or automatically execute a response program to inform the related personnel of the acquired user behavior characteristics, and the related personnel can analyze the use state of the user for commands from the feedback user behavior characteristics, confirm the correctness of user operation or detect the consistency of the system with respect to user behavior records.
The use state of the user for the command is analyzed. The user may submit, query jobs and download feedback results to the LARGE system by entering commands. And analyzing the use condition of the user for various commands from the feedback result, thereby being capable of better checking whether the writing and execution effects of the commands are satisfactory to the user, whether the user knows each command, and the like.
For confirming the correctness of the user operation. User activity within the supercomputing environment is around computing jobs, which may include actions to submit, view, delete, and download results. Wherein the submit job command (bsub) is a core operation of the user within the supercomputing environment. The LARGE system divides all operations in the user session into a plurality of command strings by taking the bsub command as a boundary, and then counts the proportion of each command appearing in the user command strings in the feedback result. When the user lacks some important steps (such as SCE log output command sceput) before submitting the job, information that the job submission is incorrect or the calculation result is incorrect can be seen from the feedback result. To facilitate further communication and guidance with the user.
The consistency of the system on the record of the user behavior is detected. The SCE log includes, in addition to the user operation record, a record of a system allocation job, and in the case where the LARGE system is normally operated, the two records are matched with each other by a session ID. Therefore, the LARGE system can find whether the SCE software normally operates or whether the record of the SCE log normally operates by detecting the consistency of the two types of log records.
For the case that the session IDs are inconsistent when the two types of records are verified, it is necessary to modify the program of the user operation record correspondingly or correct the incorrect record of the system.
According to the analysis method for the user behavior characteristics, the log file is collected and then analyzed to obtain the log data, the structure of the log data is recombined according to the information of the log data to obtain the recombined log data, and finally the user behavior characteristics are obtained according to the recombined log data, so that the user behavior characteristics are fed back to related personnel, and the service provider is helped to improve the service quality in a more targeted manner.
Corresponding to the foregoing method, an embodiment of the present invention provides an apparatus for analyzing user behavior characteristics, and as shown in fig. 4, the apparatus includes: an acquisition unit 410, a parsing unit 420, a structure reorganization unit 430, an acquisition unit 440, and a feedback unit 450.
The collecting unit 410 is used for collecting log files.
The parsing unit 420 is configured to parse the log file acquired by the acquisition unit to obtain log data.
The structure restructuring unit 430 is configured to perform structure restructuring on the log data according to the information of the log data obtained by the parsing unit, so as to obtain restructured log data.
Specifically, the structure reorganizing unit 430 performs structure reorganization on the log data by taking the session of the user as a unit according to the information of the log data, and obtains reorganized log data.
Wherein the reorganization log data has a three-level structure, wherein the three-level structure comprises a data level, a session level and a single operation level.
The obtaining unit 440 is configured to obtain the user behavior characteristics according to the restructured log data obtained by the structure restructuring unit.
Optionally, the analysis apparatus may further include: an extraction unit 460. The extracting unit 460 is configured to extract a feature value of the user session frequency according to the reorganized log data, so that the obtaining unit 440 obtains the user behavior feature according to the feature value.
The characteristic values include: the first characteristic value related to the conversation number of each day of the user, the second characteristic value related to the actual login days of the user and the total days, and the third characteristic value related to the actual login span of the user and the total days.
The feedback unit 450 is used for feeding back the user behavior characteristics.
Optionally, the apparatus may further include: a storage unit 470 for storing applications and data used by the above units.
The functions of the functional modules of the device according to the embodiment of the present invention can be realized through the steps of the foregoing analysis method embodiment, and therefore, the detailed working process of the device provided by the present invention is not repeated herein.
The user behavior characteristic analysis device provided by the invention obtains the behavior characteristic of the user through analysis, structural reorganization and statistical analysis of the SCE log, and helps a service provider to improve the service quality more pertinently.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. The software instructions may be comprised of corresponding software modules that may be stored in ram, flash memory, ROM, EPROM memory, EEPROM memory, hard disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in user equipment.
Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (6)
1. A method for analyzing user behavior characteristics, in a supercomputing environment, the method comprising:
collecting a log file;
analyzing the log file to obtain log data;
according to the information of the log data, performing structural reorganization on the log data to obtain reorganized log data;
extracting a characteristic value of the user session frequency according to the recombined log data, and acquiring user behavior characteristics according to the characteristic value; wherein the characteristic values include: a first eigenvalue, a second eigenvalue and a third eigenvalue; the first characteristic value is obtained by performing function mapping on the average value of the number of sessions initiated by the user every day, the standard deviation of the average value of the number of sessions initiated by the user every day, the average value of the sessions initiated by the user every day when the user actually logs in and the standard deviation of the average value of the sessions initiated by the user every day when the user actually logs in, the second characteristic value is the ratio of the actual login days of the user to the total days, and the third characteristic value is the ratio of the actual login span of the user to the total days; specifically, the first characteristic valueSecond characteristic value RA＝NAu/NPThird characteristic value RD＝NDu/NPWherein, NA isuActual number of days logged in for user, NDuFor the actual login span of the user, NPTotal number of days, SuiIndicates that user u is on total days NPNumber of sessions on day i;
feeding back the user behavior characteristics; the user behavior characteristics comprise: the use state of the user for the command, the correctness of the user operation and the consistency of the system on the record of the user behavior.
2. The method according to claim 1, wherein the performing structural reorganization on the log data according to the information of the log data to obtain reorganized log data specifically includes:
according to the information of the log data, carrying out structural reorganization on the log data by taking the conversation of the user as a unit to obtain reorganized log data.
3. The method of claim 2, wherein the reorganization log data has a three-level structure, wherein the three-level structure comprises a data level, a session level, and a single operation level.
4. An apparatus for analyzing user behavior characteristics, the apparatus comprising, in a super computing environment:
the acquisition unit is used for acquiring log files;
the analysis unit is used for analyzing the log file acquired by the acquisition unit to acquire log data;
the structure restructuring unit is used for carrying out structure restructuring on the log data according to the information of the log data acquired by the analyzing unit to acquire restructured log data;
the extraction unit is used for extracting a characteristic value of the user session frequency according to the recombined log data; wherein the characteristic values include: a first eigenvalue, a second eigenvalue and a third eigenvalue; the first characteristic value is obtained by performing function mapping on the average value of the number of sessions initiated by the user every day, the standard deviation of the average value of the number of sessions initiated by the user every day, the average value of the sessions initiated by the user every day when the user actually logs in and the standard deviation of the average value of the sessions initiated by the user every day when the user actually logs in, the second characteristic value is the ratio of the actual login days of the user to the total days, and the third characteristic value is the ratio of the actual login span of the user to the total days; specifically, the first characteristic valueSecond characteristic value RA＝NAu/NPThird characteristic value RD＝NDu/NPWherein, NA isuActual number of days logged in for user, NDuFor the actual login span of the user, NPTotal number of days, SuiIndicates that user u is on total days NPNumber of sessions on day i;
the obtaining unit is used for obtaining the user behavior characteristics according to the characteristic values;
the feedback unit is used for feeding back the user behavior characteristics; the user behavior characteristics comprise: the use state of the user for the command, the correctness of the user operation and the consistency of the system on the record of the user behavior.
5. The device according to claim 4, characterized in that said structural reorganization unit is specifically configured for:
according to the information of the log data, carrying out structural reorganization on the log data by taking the conversation of the user as a unit to obtain reorganized log data.
6. The apparatus according to claim 5, wherein the restructuring log data obtained by the structure restructuring unit has a three-level structure, wherein the three-level structure includes a data level, a session level and a single operation level.
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