CN106250397A - A kind of analysis method and device of user behavior feature - Google Patents
A kind of analysis method and device of user behavior feature Download PDFInfo
- Publication number
- CN106250397A CN106250397A CN201610569206.5A CN201610569206A CN106250397A CN 106250397 A CN106250397 A CN 106250397A CN 201610569206 A CN201610569206 A CN 201610569206A CN 106250397 A CN106250397 A CN 106250397A
- Authority
- CN
- China
- Prior art keywords
- daily record
- user
- record data
- restructuring
- eigenvalue
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/1805—Append-only file systems, e.g. using logs or journals to store data
- G06F16/1815—Journaling file systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention provides the analysis method and device of a kind of user behavior feature.The method includes: by gathering journal file, afterwards journal file is resolved, obtain daily record data, and according to the information of daily record data, daily record data is carried out structural rearrangement, obtain restructuring daily record data, finally according to restructuring daily record data, obtain user behavior feature, thus to related personnel's feedback user behavior characteristics, help that ISP is more targeted improves service quality.
Description
Technical field
The present invention relates to computer realm, particularly relate to the analysis method and device of a kind of user behavior feature.
Background technology
Grid environment is by integrating, managing and dispatch distributed Heterogeneous Computing resource so that it is form a virtual meter
Calculate cluster, the high-performance calculation level of resources utilization, the target of raising user's service reliability can be improved.
Chinese Academy of Sciences's supercomputing environment is a kind of three-tier architecture grid computing environment, uses scientific computing grid software
SCE provides the user the high performance computing service of high-quality.SCE software is a kind of grid middleware, and user can be soft by this SCE
Part uses all resources in whole grid environment.SCE software has Internet portal Portal and two kinds of users of order line
Formula, the submission that can be fulfiled assignment by SCE software, is revised, inquires about, downloads the work such as destination file.SCE middleware can produce
Raw SCE journal file, for recording the user's various operations in supercomputing environment.It is to say, at supercomputing environment
Under, system journal and SCE daily record can be generated.
But, the system journal of generation under supercomputing environment is only analyzed by prior art, makes the user of acquisition
Behavior characteristics is inaccurate, and in order to analyze the feature of all user behaviors in grid environment, it is necessary to first observation in the past one or
All user journals in several stages.Make the such a ultra-large log recording of analysis, become one more complicated, the most relatively
Long work.
Summary of the invention
Grid environment log analysis framework is (English: log analyzing framework in grid
Environment, LARGE) system is as the Log Analysis System of supercomputing environment, by the parsing of SCE daily record, structure
Restructuring and statistical analysis obtain the behavior characteristics of user, thus improve Consumer's Experience.
First aspect, it is provided that a kind of analysis method of user behavior feature, the method is applied in supercomputing environment,
The method includes: gathers journal file, and resolves journal file, obtains daily record data, afterwards according to daily record data
Information, carries out structural rearrangement to daily record data, obtains restructuring daily record data, further according to restructuring daily record data, obtains user behavior
Feature, thus feedback user behavior characteristics.
In one optionally realizes, according to the information of daily record data, daily record data is carried out structural rearrangement, obtain restructuring
Daily record data, particularly as follows: according to the information of daily record data, daily record data is carried out structural rearrangement in units of the session of user,
Obtain restructuring daily record data.
In one optionally realizes, restructuring daily record data there is tertiary structure, wherein, tertiary structure include data level,
Session level and single operation level.
In one optionally realizes, before obtaining user behavior feature, the method also includes: according to restructuring daily record number
According to, extract the eigenvalue of user conversation frequency；According to eigenvalue, obtain user behavior feature.
In one optionally realizes, this feature value includes: the First Eigenvalue that user's session number every day is relevant, Yong Hushi
The third feature value that border logs in the natural law Second Eigenvalue relevant to total natural law and user's actual log span and total natural law is correlated with.
On the other hand, it is provided that the analytical equipment of a kind of user behavior feature, this device is applied in supercomputing environment,
This device includes: collecting unit, resolution unit, acquiring unit and feedback unit.Wherein, collecting unit is used for gathering daily record literary composition
Part.Resolution unit resolves for the journal file collecting collecting unit, obtains daily record data.Structural rearrangement unit is used
In the information of the daily record data obtained according to resolution unit, daily record data is carried out structural rearrangement, obtain restructuring daily record data.Obtain
Take unit for the restructuring daily record data obtained according to structural rearrangement unit, acquisition user behavior feature.Feedback unit is for anti-
Feedback user behavior feature.
In one optionally realizes, structural rearrangement unit specifically for: according to the information of daily record data, to daily record data
In units of the session of user, carry out structural rearrangement, obtain restructuring daily record data.
In one optionally realizes, the restructuring daily record data that structural rearrangement unit obtains has tertiary structure, wherein, and three
Level structure includes data level, session level and single operation level.
In one optionally realizes, this device also includes: extraction unit.Extraction unit is for according to described restructuring daily record
Data, extract the eigenvalue of user conversation frequency.Acquiring unit, according to eigenvalue, obtains user behavior feature.
In one optionally realizes, the eigenvalue that extraction unit extracts includes: user's session number every day relevant first
Second Eigenvalue and user's actual log span that eigenvalue, user's actual log natural law are relevant to total natural law are correlated with total natural law
Third feature value.
In one optionally realizes, this device can also include that memory element is for storing answering of the use of said units
By program and data.
LARGE resolves reconstruct by carrying out SCE daily record, can carry out the behavior characteristics of supercomputing environment user point
Analysis, helps that ISP is more targeted improves service quality
Accompanying drawing explanation
In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, required use in embodiment being described below
Accompanying drawing be briefly described, it should be apparent that, below describe in accompanying drawing be only some embodiments of the present invention, for this
From the point of view of the those of ordinary skill of field, on the premise of not paying creative work, it is also possible to obtain other according to these accompanying drawings
Accompanying drawing.
The grid environment log analysis frame system structural representation that Fig. 1 provides for the embodiment of the present invention；
The flow chart of the analysis method of the user behavior feature that Fig. 2 provides for the embodiment of the present invention；
The structural representation of the restructuring daily record data that Fig. 3 provides for the embodiment of the present invention；
The analytical equipment of a kind of user behavior feature that Fig. 4 provides for the embodiment of the present invention.
Detailed description of the invention
Below by drawings and Examples, technical scheme is described in further detail.
The analysis method of the user characteristics that the present invention provides is applied in the LARGE system shown in Fig. 1.LARGE system is
The log processing being deployed in supercomputing environment analyzes system, by being collected the daily record of computing cluster, arranging, divides
Analysis, obtains various result, thus help system attendant's monitors system conditions, find run-time error or the improper behaviour of user
Make, improve service quality.
In FIG, LARGE system 100 can include log acquisition module 110, log analysis module 120 and result feedback
Module 130.
Log acquisition module 110, for all log transmission the setting to log analysis module that will generate in grid environment
Standby upper.Log acquisition module 110 can apply to any suitable data acquisition modes, such as face book net Facebook
Scribe system, it is also possible to be the Flume system of Cloudera offer.
Log analysis module 120, for the daily record collected carried out Log Filter classification and pretreatment, resolve reconstruct,
Statistical information and applied analysis etc. process, and generate analysis result.Log analysis module 120 can apply to any suitable number
According to analysis mode, such as Hadoop project is increased income data collection analysis system Chukwa in distributed system.
Log analysis module 130, is more conducive to be correlated with for the analysis result that log analysis module 120 generates being organized into
Personnel understand correlation report, Visual Chart or show program accordingly, or by analysis result formulate for certain pattern day
The rule of response of will, such as, send alarm mail or automatically perform responder.
According to the two distinct types of system journal in supercomputing environment and SCE daily record, LARGE system provides use
The analysis method of family feature.Illustrate as a example by resolving SCE daily record below.
Wherein, SCE daily record is the daily record produced by SCE software, have recorded user's operation in supercomputing environment,
SCE daily record be divided into record clustering job information and order line user operation SCE daily record and record user by webpage submit to and
Management calculates the Portal daily record of the operation of operation.By SCE daily record is carried out statistical analysis, we can obtain environment user
Various actions feature, these features include: the use habit of user, usage frequency, to service condition of SCE order etc.,
Exchange with user so that more targeted, thus the user without type is used the service strategy being more suitable for, Yi Jifa
Defect that may be present and deficiency in existing supercomputing environment, thus promote the overall quality of service of supercomputing environment.
The flow chart of the analysis method of the user characteristics that Fig. 2 provides for the embodiment of the present invention.In supercomputing environment, should
The executive agent of method can be LARGE system, as in figure 2 it is shown, the method includes:
Step 210, collection journal file.
The journal file that all devices in supercomputing environment is produced by LARGE system is acquired, and wherein, collects
Journal file can be difference/same type, difference/identical content, and journal file can include system journal and SCE daily record.
Step 220, journal file is resolved, obtain daily record data.
The journal file collected is resolved by LARGE system, parses the attribute information of journal file, obtains daily record
Data.
The attribute information of SCE daily record can include session belonging to time that daily record produces, main frame, user name, source IP, operation
The necessary information such as numbering, action type and various operating parameters, every segment information can by space or self-defining separator every
Open, to facilitate each segment information of extraction and to be reassembled as structurized data, obtain daily record data.Such as, the form of SCE daily record is such as
Under:
" logging time | main frame | Log Types | session id | user name | source IP | operation ID | action type | operation coding |
Operating parameter "
It should be noted that due to the middleware daily record of the group operation information in SCE daily record Yu order line user operation,
Submitted to consistent with the content characteristic of Portal daily record managing the operation calculating operation by webpage with record user, therefore both
Can use identical process step and analysis method, wherein content characteristic unanimously can be understood as the two form fix, content
Compact, and be prone to be readable by a computer.
Step 230, information according to daily record data, carry out structural rearrangement to daily record data, obtains restructuring daily record data.
Concrete, daily record data, according to the information of daily record data, can be entered in units of the session of user by LARGE system
Row structural rearrangement, obtains restructuring daily record data.Restructuring daily record data can have data level, session level and the three of single operation level
Level structure.
LARGE system is by the session data information of all users, and such as user name, log-on count, the information such as operation is stored in
In data level.Session is a process (by, operation is to the process of end operation) of complete operation, LARGE system according to
The session of user is classified by name in an account book, sorted user session information is stored in session level, afterwards with session as list
Operation is classified by position, obtains each single operation in this session and is stored in single operation level, in order to every to each session
Individual single operation resolves.
As it is shown on figure 3, this restructuring daily record data has a tertiary structure of data level-session level-single operation level, wherein,
Data level includes user A and two users of user B, realizes all of user A and user B in the session of 3 times, and 3 these sessions altogether
Operation, operation can be inquiry operation and exit operation.The session of user will be classified by session level according to user name, as incited somebody to action
User A is that unit is divided into according to user name: user A-session 1-inquires about operation, exits operation, user A-session 2-exits operation
With user B-session 3-inquiry operation, exit operation.In units of session, operation is classified again, obtain in this session each
Single operation is stored in single operation level, is divided in units of session: user A-session 1-inquiry operation, user A-session 1-move back
Go out operation and user A-session 2-exit operation, be that unit is divided into by user B according to session: user B-session 3-inquiry operation and
User B-session 3-exits operation.
LARGE system carries out structural rearrangement to daily record data in units of the session of user, due to the daily record of same session
Data are the most identical in the value of the information such as session id, host address, user name and IP, and LARGE system is in units of the session of user
Daily record data is carried out structural rearrangement, is i.e. set to absolute construction with session and contributes to reducing memory data output, and be conducive to
Session carries out statistics and analysis for relying on to user operation habits.
Step 240, according to restructuring daily record data, obtain user behavior feature.
By extracting restructuring daily record data, LARGE system can be integrated with the entitled classification of user and mark
Relevant information, thus can obtain user behavior feature, i.e. about the elementary statistics result of user's behaviour in service.Wherein, user
Behavior characteristics refers to that the Behavior law being embodied users single or multiple in certain time phase is formed and summarizes and sum up,
Analyze user behavior feature to contribute to understanding the use habit of user, understand user's request and the defect found in user's service.
Alternatively, before obtaining user behavior feature, LARGE system can extract user according to restructuring daily record data
The eigenvalue of session frequency, further according to this feature value, obtains user behavior feature, classifies user.
Concrete, LARGE system, according to restructuring daily record data information, will can reflect some features of user conversation frequency
Value extracts, and uses clustering algorithm to classify user.Wherein, the frequency of user conversation refers to use in certain time period
Family is connected to supercomputing environment, initiates the number of times of a session.It should be noted that the session frequency of user is to a great extent
On reflect user and environment calculated to the demand of resource, and can provide accordingly according to user Demand and service side in various degree
Different service strategies.
Further, the eigenvalue of user conversation frequency may include that the First Eigenvalue that user's session number every day is relevant,
Second Eigenvalue that user's actual log natural law is relevant to total natural law and user's actual log span relevant with total natural law the 3rd
Eigenvalue.According to the size of each 3 eigenvalues of user, LARGE system can obtain relative users by using clustering algorithm
Behavior characteristics, thus relative users is classified.Wherein, clustering algorithm belongs to prior art, does not repeats at this.
In one example, LARGE system wants the usage frequency of supercomputing user, total natural law in P in section P analysis time
For NP, user collects { U1,U2,…,Un, sum is n, and the most each user session number in time period P is S1,S2,…,Sn, with
And session number S that user u (u{x | 1≤x≤n}) every day is concreteu1,Su2,…,SuNP.Owing to user can log in super not every day
Level computing environment calculates, and we also need to add up the actually used natural law NA of each user1,NA2,…,NAn, and user is real
Span (i.e. in during P, first session of user is to the natural law shared by last session) ND of border login time1,ND2,…,
NDn。
For a user u, following 3 eigenvalues can be obtained: LARGE system of users initiates the flat of session number every day
Average, user initiate to initiate every day when the standard deviation of session number average, user's actual log meansigma methods and the user of session every day
During actual log, the standard deviation of session every day average carries out Function Mapping, gets the First Eigenvalue and is:
LARGE system is according to the ratio of user's actual log natural law Yu total natural law, and getting Second Eigenvalue is RA=NAu/
NP。
LARGE system is according to the ratio of user's actual log span Yu total natural law, and getting third feature value is RD=NDu/
NP。
If the session that the average every day of first kind user initiates is most, span is the longest, then it represents that first kind user is super
Sustained user in computing environment, of a relatively high to the demand calculating resource.If the meeting of the average every day of Equations of The Second Kind user
Talking about several the most less, span is identical with first kind user, then it represents that Equations of The Second Kind user is the sustained user in supercomputing environment,
But it is general to the demand calculating resource.If the 3rd class user session number average every day is close with Equations of The Second Kind user, but actual step on
Record natural law is less, then it represents that such user does not has the demand of persistence to the demand calculating resource.If the 4th class user logs in
Natural law is minimum, then it represents that this kind of user is not the fixing user in supercomputing environment.
It should be understood that utilize the above-mentioned analysis method of LARGE system, user can be provided after user is classified
Pertinent service.Such as first kind user priority is provided and calculate resource, and seek the opinion of super meter to the 4th class user return visit
Which problem calculates environment has need improvement etc..
Step 250, feedback user behavior characteristics.
LARGE system can be with correlation report, Visual Chart or the shape of display program by the user behavior feature obtained
Formula is presented to related personnel, or the user behavior feature of acquisition can be formulated for certain pattern daily record by LARGE system
Alarm mail or automatically execution responder inform related personnel, and related personnel can be from the user behavior feature of feedback, can
To analyze user's use state for order, the correctness of confirmation user operation or detecting system are about user behavior record
Concordance.
For analyzing user's use state for order.User can be to LARGE system in the way of input order
Submission, inquiry job and download feedback result.User's behaviour in service for various orders is analyzed from feedback result, thus can
Write with whether implementation effect makes user satisfied with preferably viewing command itself, and user is to each order the most
Solve.
For confirming the correctness of user operation.User's activity in supercomputing environment is around calculating operation and carries out
, calculate operation and can include submitting to, check, delete and download the behaviors such as result.Wherein, it is super for submitting job command (bsub) to
The core operation of user in level computing environment.All operations in user conversation is separated into for boundary by LARGE system with bsub order
Some command strings, then add up the ratio that each order occurs at user command string in feedback result.When user is submitting work to
When lacking some important step (such as SCE daily record output order sceput) before industry, operation can be viewed from feedback result
Submit the incorrect or incorrect information of result of calculation to.So that further being linked up with this user and being instructed.
For detecting system about the concordance of user behavior record.SCE daily record, in addition to user operation records, also includes
The record of system distribution operation, in the case of LARGE system is properly functioning, both records are according to mutual of session id
Join.In same session, two class log recordings are consistent, and therefore, LARGE system is by detecting the consistent of this two classes log recording
Property, it appeared that SCE software is the most properly functioning, or the record of SCE daily record is the most properly functioning.
During for verifying two class records, the situation that the session id of appearance is inconsistent, need to revise accordingly user operation records
Program, or the incorrect record of update the system.
The analysis method of the user behavior feature that the present invention provides, by gathering journal file, enters journal file afterwards
Row resolves, and obtains daily record data, and according to the information of daily record data, daily record data carries out structural rearrangement, obtains restructuring daily record
Data, finally according to restructuring daily record data, obtain user behavior feature, thus to related personnel's feedback user behavior characteristics, side
Help that ISP is more targeted improves service quality.
Corresponding with said method, embodiments provide the analytical equipment of a kind of user behavior feature, such as Fig. 4 institute
Showing, this device includes: collecting unit 410, resolution unit 420, structural rearrangement unit 430, acquiring unit 440 and feedback unit
450。
Collecting unit 410 is used for gathering journal file.
Resolution unit 420 resolves for the journal file collecting collecting unit, obtains daily record data.
The information of the structural rearrangement unit 430 described daily record data for obtaining according to resolution unit, enters daily record data
Row structural rearrangement, obtains restructuring daily record data.
Concrete, structural rearrangement unit 430 is according to the information of daily record data, to daily record data in units of the session of user
Carry out structural rearrangement, obtain restructuring daily record data.
Wherein, restructuring daily record data has tertiary structure, and wherein, tertiary structure includes data level, session level and single behaviour
Make level.
Acquiring unit 440, for the restructuring daily record data obtained according to structural rearrangement unit, obtains user behavior feature.
Alternatively, this analytical equipment can also include: extraction unit 460.Extraction unit 460 is for according to restructuring daily record number
According to, extract the eigenvalue of user conversation frequency, in order to acquiring unit 440, according to eigenvalue, obtains user behavior feature.
This feature value includes: the First Eigenvalue, user's actual log natural law and the total natural law that user's session number every day is relevant
The third feature value that relevant Second Eigenvalue is relevant to total natural law with user's actual log span.
Feedback unit 450 is for feedback user behavior characteristics.
Alternatively, this device can also include: memory element 470, for storing the application program of the use of said units
And data.
The function of each functional module of embodiment of the present invention device, can be by each step of above-mentioned analysis embodiment of the method
Realize, therefore, the specific works process of the device that the present invention provides, repeat the most again at this.
The analytical equipment of the user behavior feature that the present invention provides, by parsing, structural rearrangement and statistics to SCE daily record
Analyze and obtain the behavior characteristics of user, help that ISP is more targeted improves service quality.
The method described in conjunction with the embodiments described herein or the step of algorithm can use hardware, processor to perform
Software module, or the combination of the two implements.Software instruction can be made up of corresponding software module, and software module can be by
Deposit in random access memory, flash memory, read only memory, erasable programmable read-only register (English: erasable
Programmable read-only memory, EPROM) memorizer, EEPROM memorizer (English:
Electrically erasable programmable read-only memory, EEPROM), hard disk, read-only optical disc (English
Literary composition: compact disc read-only memory, CD-ROM) or storage Jie of other form any well known in the art
In matter.A kind of exemplary storage medium coupled to processor, thus enables a processor to from this read information, and
Information can be write to this storage medium.Certainly, storage medium can also be the ingredient of processor.Processor and storage medium
May be located in ASIC.It addition, this ASIC may be located in subscriber equipment.Certainly, processor and storage medium can also conducts
Discrete assembly is present in subscriber equipment.
Those skilled in the art it will be appreciated that in said one or multiple example, merit described in the invention
Can be able to realize by hardware, software, firmware or their combination in any.When implemented in software, can be by these functions
It is stored in computer-readable medium or is transmitted as the one or more instructions on computer-readable medium or code.
Above-described detailed description of the invention, has been carried out the purpose of the present invention, technical scheme and beneficial effect further
Describe in detail, be it should be understood that the detailed description of the invention that the foregoing is only the present invention, be not intended to limit the present invention
Protection domain, all on the basis of technical scheme, any modification, equivalent substitution and improvement etc. done, all should
Within being included in protection scope of the present invention.
Claims (10)
1. the analysis method of a user behavior feature, it is characterised in that in supercomputing environment, described method includes:
Gather journal file；
Described journal file is resolved, obtains daily record data；
According to the information of described daily record data, described daily record data is carried out structural rearrangement, obtain restructuring daily record data；
According to described restructuring daily record data, obtain user behavior feature；
Feed back described user behavior feature.
Method the most according to claim 1, it is characterised in that the described information according to described daily record data, to described day
Will data carry out structural rearrangement, obtain restructuring daily record data, particularly as follows:
According to the information of described daily record data, described daily record data is carried out in units of the session of user structural rearrangement, obtain
Restructuring daily record data.
Method the most according to claim 2, it is characterised in that described restructuring daily record data has tertiary structure, wherein, institute
State tertiary structure and include data level, session level and single operation level.
Method the most according to claim 1, it is characterised in that before described acquisition user behavior feature, described method
Also include:
According to described restructuring daily record data, extract the eigenvalue of user conversation frequency；
According to described eigenvalue, obtain user behavior feature.
Method the most according to claim 4, it is characterised in that described eigenvalue includes: user's session number every day is correlated with
Second Eigenvalue that the First Eigenvalue, user's actual log natural law are relevant to total natural law and user's actual log span and total natural law
Relevant third feature value.
6. the analytical equipment of a user behavior feature, it is characterised in that in supercomputing environment, described device includes:
Collecting unit, is used for gathering journal file；
Resolution unit, resolves for the described journal file collecting described collecting unit, obtains daily record data；
Structural rearrangement unit, the information of the described daily record data for obtaining according to described resolution unit, to described daily record data
Carry out structural rearrangement, obtain restructuring daily record data；
Acquiring unit, for the described restructuring daily record data obtained according to described structural rearrangement unit, obtains user behavior feature；
Feedback unit, is used for feeding back described user behavior feature.
Device the most according to claim 6, it is characterised in that described structural rearrangement unit, specifically for:
According to the information of described daily record data, described daily record data is carried out in units of the session of user structural rearrangement, obtain
Restructuring daily record data.
Device the most according to claim 7, it is characterised in that the described restructuring daily record number that described structural rearrangement unit obtains
According to having tertiary structure, wherein, described tertiary structure includes data level, session level and single operation level.
Device the most according to claim 6, it is characterised in that described device also includes: extraction unit,
Described extraction unit, for according to described restructuring daily record data, extracting the eigenvalue of user conversation frequency；
Described acquiring unit, for according to described eigenvalue, obtains user behavior feature.
Device the most according to claim 9, it is characterised in that the described eigenvalue that described extraction unit extracts includes: use
Second Eigenvalue that the First Eigenvalue that family session number every day is relevant, user's actual log natural law are relevant to total natural law and user's reality
Border logs in the third feature value that span is relevant to total natural law.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610569206.5A CN106250397B (en) | 2016-07-19 | 2016-07-19 | User behavior characteristic analysis method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610569206.5A CN106250397B (en) | 2016-07-19 | 2016-07-19 | User behavior characteristic analysis method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106250397A true CN106250397A (en) | 2016-12-21 |
| CN106250397B CN106250397B (en) | 2019-12-31 |
Family
ID=57613332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610569206.5A Active CN106250397B (en) | 2016-07-19 | 2016-07-19 | User behavior characteristic analysis method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106250397B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107370628A (en) * | 2017-08-17 | 2017-11-21 | 阿里巴巴集团控股有限公司 | Based on the log processing method and system buried a little |
| CN108229154A (en) * | 2017-12-12 | 2018-06-29 | 顺丰科技有限公司 | Sensitive data operation log recording method, device, storage medium and equipment |
| CN108733543A (en) * | 2017-04-24 | 2018-11-02 | 北京京东尚科信息技术有限公司 | A kind of method, apparatus of log analysis, electronic equipment and readable storage medium storing program for executing |
| CN111695108A (en) * | 2020-06-04 | 2020-09-22 | 中国科学院计算机网络信息中心 | Unified account identification system for multi-source accounts in heterogeneous computing environment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1567851A (en) * | 2003-07-07 | 2005-01-19 | 华为技术有限公司 | A method for monitoring user behavior in network management system |
| CN103188104A (en) * | 2011-12-31 | 2013-07-03 | ***通信集团浙江有限公司 | Method and device for analyzing user behaviors |
| US20150254125A1 (en) * | 2013-01-11 | 2015-09-10 | Hitachi, Ltd. | Information processing system monitoring apparatus, monitoring method, and monitoring program |
| CN105049232A (en) * | 2015-06-19 | 2015-11-11 | 成都艾尔普科技有限责任公司 | Network information log audit system |
| CN105162614A (en) * | 2015-06-19 | 2015-12-16 | 成都艾尔普科技有限责任公司 | Network user behavior auditing and responsibility management method |
-
2016
- 2016-07-19 CN CN201610569206.5A patent/CN106250397B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1567851A (en) * | 2003-07-07 | 2005-01-19 | 华为技术有限公司 | A method for monitoring user behavior in network management system |
| CN103188104A (en) * | 2011-12-31 | 2013-07-03 | ***通信集团浙江有限公司 | Method and device for analyzing user behaviors |
| US20150254125A1 (en) * | 2013-01-11 | 2015-09-10 | Hitachi, Ltd. | Information processing system monitoring apparatus, monitoring method, and monitoring program |
| CN105049232A (en) * | 2015-06-19 | 2015-11-11 | 成都艾尔普科技有限责任公司 | Network information log audit system |
| CN105162614A (en) * | 2015-06-19 | 2015-12-16 | 成都艾尔普科技有限责任公司 | Network user behavior auditing and responsibility management method |
Non-Patent Citations (1)
| Title |
|---|
| 赵一宁 等: ""网格环境日志分析框架LARGE的设计"", 《科研信息化技市与应用》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108733543A (en) * | 2017-04-24 | 2018-11-02 | 北京京东尚科信息技术有限公司 | A kind of method, apparatus of log analysis, electronic equipment and readable storage medium storing program for executing |
| CN108733543B (en) * | 2017-04-24 | 2021-11-12 | 北京京东尚科信息技术有限公司 | Log analysis method and device, electronic equipment and readable storage medium |
| CN107370628A (en) * | 2017-08-17 | 2017-11-21 | 阿里巴巴集团控股有限公司 | Based on the log processing method and system buried a little |
| CN108229154A (en) * | 2017-12-12 | 2018-06-29 | 顺丰科技有限公司 | Sensitive data operation log recording method, device, storage medium and equipment |
| CN111695108A (en) * | 2020-06-04 | 2020-09-22 | 中国科学院计算机网络信息中心 | Unified account identification system for multi-source accounts in heterogeneous computing environment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106250397B (en) | 2019-12-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9367601B2 (en) | Cost-based optimization of configuration parameters and cluster sizing for hadoop | |
| Kagdi et al. | Assigning change requests to software developers | |
| US9565129B2 (en) | Resource provisioning planning for enterprise migration and automated application discovery | |
| US11182691B1 (en) | Category-based sampling of machine learning data | |
| US9942167B2 (en) | Mesh platform utility computing portal | |
| US9286571B2 (en) | Machine learning for database migration source | |
| US20150254328A1 (en) | Methods and systems that categorize and summarize instrumentation-generated events | |
| CN106294101B (en) | The page gets test method and device ready | |
| US8706684B2 (en) | System and method for managing enterprise data | |
| Buschle et al. | A Tool for automatic Enterprise Architecture modeling | |
| CN103329129A (en) | Multi-tenant audit awareness in support of cloud environments | |
| CN113377805B (en) | Data query method and device, electronic equipment and computer readable storage medium | |
| US20160196564A1 (en) | Systems and methods for analyzing consumer sentiment with social perspective insight | |
| CN106250397A (en) | A kind of analysis method and device of user behavior feature | |
| WO2014055579A1 (en) | Pagination of data based on recorded url requests | |
| CN109039787A (en) | log processing method, device and big data cluster | |
| WO2022029593A1 (en) | Shadow experiments for serverless multi-tenant cloud services | |
| KR100912177B1 (en) | User behavior analysis method and system | |
| Hesse et al. | Senska–towards an enterprise streaming benchmark | |
| Al-Sayed et al. | CloudFNF: An ontology structure for functional and non-functional features of cloud services | |
| Brown et al. | An approach to benchmarking configuration complexity | |
| Kaviani et al. | Cross-tier application and data partitioning of web applications for hybrid cloud deployment | |
| Anjum et al. | A mapping study of the definitions for service oriented architecture | |
| Slaninová et al. | User segmentation based on finding communities with similar behavior on the web site | |
| Nuckolls | Azure storage, streaming, and batch analytics: a guide for data engineers |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |