CN106354813A - Mass data dimension user positioning method - Google Patents
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- CN106354813A CN106354813A CN201610756179.2A CN201610756179A CN106354813A CN 106354813 A CN106354813 A CN 106354813A CN 201610756179 A CN201610756179 A CN 201610756179A CN 106354813 A CN106354813 A CN 106354813A
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Abstract
The invention discloses a mass data dimension user positioning method, and solves the technical problems that dimension data in mass data cannot be effectively positioned, data use ratio is low, and data analysis efficiency is affected. The positioning method includes the steps: presetting a key value structure of a user, taking a user identification as a key and taking a corresponding value as a key value; forming a position identification of the user by the aid of the key value structure; scanning a data source and positioning single-dimension user position information by the aid of the position identification to form user distribution data.
Description
Technical field
The present invention relates to a kind of data processing method, more particularly to a kind of place to specific type of data in mass data
Reason method.
Background technology
In the digital information epoch competing with opportunity and deposit, the analysis of data and statistics are with the identity appearance of decision support
In many association areas such as economic, management, planning and investment.As for enterprise operation department and decision-making level provide important decision according to
According to technological means, need to complete to carry out real-time collecting, analysis to the significant data of reaction and impact enterprise development, and timely shape
Become key message the processing procedure fed back, meet ageing on the premise of, objectively react business development situation.
For example for operational enterprise, number of users index is a key index of reflection business development.
In the case of subscriber traffic is ever-increasing, under mass data environment, how quickly to calculate number of users,
Become data analysis system business demand urgently to be resolved hurrily.Carrying out magnanimity in the face of the daily more than one hundred million user's usage logs producing
In the calculating of data statistics processing, it is a technical barrier being badly in need of solving that the quick reading of number of users calculates.
At present, the method that the prior art of calculating mass data generally adopts is by once (i.e. a certain to a kind of dimension
Individual parameter or parameter, such as date, type of service, user type etc.) statistical demand it is necessary to one is carried out to total data
Secondary scanning calculates, and concrete grammar includes packet, duplicate removal, the process of summation.The calculating process of number of users is to first have to all counting
According to the mark of middle exclusion duplicate customer, then calculate the number of users after duplicate removal.
When increase statistical dimension when in addition it is also necessary to re-start deduplication operation and double counting, its amount of calculation very huge and
Time-consuming.For example: for the daily record data of a day respectively the number of users of calculating network type and type of service when, according to prior art
Method, need scanning log file data source twice: the quantity of the user object of first time calculating network type, count for second
Calculate the user object quantity of type of service.
When needing the combination number of users of both the above type (two dimensions) in addition it is also necessary to re-start single pass and meter
Calculate, and scanning each time and statistical computation, because of the data object being related to magnanimity, all can take substantial amounts of computer resource.Often
The once scanning to data source (being typically stored in the daily record data in data base), needs to call high-level data interface, process
Complex data object, often will cause larger pressure, magnanimity number to database engine, processor and disk system, memory system
According to scan period longer be also unfavorable for ensureing ageing.
The scanning how being rapidly completed various dimensions user data calculates formation multi-dimensional data analysis result, this computer
Numerical analysis and the technical problem in statistics field, often relate to following technological difficulties:
The how data of certain dimension in location data source, ensures the discretization of data while duplicate removal and can examine
Rope.This is to ensure that and reduces complete scan number of times and meet the key that the accumulation of data scanning result updates.
How to form the efficient intermediate operations to mass data, use rudimentary computing ways and means, it is to avoid senior as far as possible
The high tps (process number of transactions per second) that data object computing is formed consumes.
Content of the invention
In view of this, embodiments provide a kind of localization method of the dimension user to mass data, for solving
Certainly in mass data, dimension data cannot effectively position, and leads to data user rate low, the technical problem of impact data analysiss efficiency.
A kind of localization method of dimension user to mass data of the present invention, comprises the following steps:
The key value structure of pre-set user, with the ID of user as key, corresponding numerical value is key assignments；
Form the station location marker of user using key value structure；
Scan data source, positions the customer position information under single dimension using station location marker, forms user distribution data.
Described utilization key value structure forms the station location marker of user, comprising:
Scan data source, obtains user profile, extracts the unique subscriber identification of user；
Distribute unique Digital ID for each user；
Unique subscriber identification and unique Digital ID are formed the key-value pair data of user.
Described scan data source, positions the customer position information under single dimension using station location marker, forms user distribution
Data, comprising:
Scan data source, extracts the user profile in the user data of single dimension in data source, forms corresponding user
Mark；
ID is compared with user's key-value pair data, forms the corresponding key-value pair data of user；
Relative users key-value pair data is formed linear structure data；
Position storage position the assignment in internal memory using the value in linear structure data, form user distribution data.
Described scan data source adopts segmentation or step scan.
User profile in the user data of single dimension in described extraction data source, comprising:
User profile in the user data of several single dimensions of simultaneous extraction.
Described linear structure data adopts list structure.
Linear structure data deduplication in described list structure.
Described linear structure data adopts queue structure.
Value in the described data using linear structure positions the storage position in internal memory and goes reassignment.
Described reassignment is gone to include:
Carry out Boolean calculation with storage position content in assignment procedure, use operation result assignment.
A kind of localization method of the dimension user to mass data of the present invention, using the discreteness of numerical value, will be with user
Corresponding serial number forms the station location marker of correlation as coordinate, by station location marker be mapped as position in two-dimensional space and away from
From, and determine corresponding position and distance using the seriality of memory address, in the memory object assignment of linear position, formation can
Measurement and the location distribution information of storage.Solving all can only be based on given to the scanning of data source and process in mass data
Data object, and the regularity of distribution of the type of data object and object data lies in the difficulty that cannot extract in data source and utilize
Topic.Using the station location marker set up between user and discrete values, memory headroom is set up position association, can be by mass data
In the positioning of various dimensions user data be applied to follow-up data analysiss, so that data analysiss efficiency is greatly promoted.
Brief description
Fig. 1 a is the flow chart of localization method one embodiment of the dimension user to mass data for the present invention；
Fig. 1 b is the preset flow chart of localization method one embodiment of the dimension user to mass data for the present invention；
Fig. 2 is the formation flow process of the station location marker of localization method one embodiment of the dimension user to mass data for the present invention
Figure；
Fig. 3 is the formation flow process of the user distribution of localization method one embodiment of the dimension user to mass data for the present invention
Figure；
Fig. 4 is the formation flow process of the dimension data of localization method one embodiment of the dimension user to mass data for the present invention
Figure；
Fig. 5 is the shape of the various dimensions customer analysis of localization method one embodiment of the dimension user to mass data for the present invention
Become flow chart；
Fig. 6 is the mistake of the localization method one embodiment formation dimension user data of the dimension user to mass data for the present invention
Journey schematic diagram.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.Based on this
Embodiment in invention, the every other reality that those of ordinary skill in the art are obtained under the premise of not making creative work
Apply example, broadly fall into the scope of protection of the invention.
Number of steps in drawing is only used for the reference as this step, does not indicate that execution sequence.
The localization method of the dimension user to mass data of the present invention, using the discreteness of number, will be corresponding with user
Serial number forms the station location marker of correlation, station location marker is mapped as the position in two-dimensional space and distance, and utilizes internal memory
The seriality of address determines corresponding position and distance, forms the user profile of respective dimensions type using Boolean calculation.
Fig. 1 a is the flow chart of the localization method of the dimension user to mass data of one embodiment of the invention, many for being formed
The process of dimension user profile.As shown in Figure 1a, the method includes:
Step 02: scan data source, position the customer position information under single dimension using station location marker, form user and divide
Cloth data；
Step 03: storable dimension user data is formed according to user distribution data；
Step 04: between storable dimension user data, multidimensional data analysis result is formed by Boolean calculation.
The localization method of the dimension user to mass data of the present embodiment, using the position set up between user and discrete values
Put mark, memory headroom is set up position association, the user profile of mapping is stored with positional information, makes full use of internal memory ring
The addressing performance in border and the absolute advantagess of the rudimentary computing of processor, by the computing week of the dimension Users'Data Analysis in mass data
Phase greatly shortens.In the processing procedure of complicated various dimensions number of users, data source rate of scanning can be reduced, analyze process
Simplify, processing speed can improve one or two magnitude.
Fig. 1 b is the preset flow chart of the localization method of the dimension user to mass data of one embodiment of the invention, provides
A kind of process of forming position mark, as shown in Figure 1 b, the method includes:
Step 01: set up ID key value structure, form the station location marker of user.
Step 01 as carrying out the independent data handling procedure before data processing using station location marker.
The localization method of the dimension user to mass data of the present embodiment, using the discreteness of numerical value, by discrete user
Associate forming position mark with serial number, serial number is mapped as distance and position in two-dimensional space.
Fig. 2 is the formation flow process of the station location marker of localization method one embodiment of the dimension user to mass data for the present invention
Figure.Include as shown in Figure 2:
Step 11: obtain the user profile in data source, extract ID；
By fractional scanning data source, obtain user profile, extract the unique subscriber identification of user.There is stage extraction number
Be conducive to renewal during data source accumulation according to function.
Step 12: distribute unique Digital ID for each user, form the key assignments logarithm of ID and Digital ID
According to；Numeral in Digital ID is numerical value.
Digital ID to be allocated has seriality, and not reproducible, fixing corresponding one of the ID of each user
Digital ID, the distribution of Digital ID has randomness.Ensure the uniqueness of key and key assignments, the distribution of Digital ID meets normal state
Distribution.
Step 13: set up the user's key-value pair structured data that can read in internal memory using key-value pair data.
For example, using java programming, the corresponding relation of this ID and Digital ID is loaded onto Hash mapping object
In hashmap, form the key-value pair data structure of " user name: Digital ID ", ID is key, Digital ID is value.
Fig. 3 is the formation flow process of the user distribution of localization method one embodiment of the dimension user to mass data for the present invention
Figure.Include as shown in Figure 3:
Step 21: fractional scanning data source, extract the user profile in the user data of single dimension in data source, formed
Corresponding ID；
Can be the user profile in the user data of other each dimensions in simultaneous extraction data source, be formed subsequently parallel
Data handling procedure.
Step 22: ID is compared with the user's key-value pair structured data in internal memory, the user's of formation duplicate removal is corresponding
Key-value pair data；
Duplicate removal, embodies the number of users in same dimension, it would however also be possible to employ ID is marked, to labelling
Carry out boolean or computing, filter the user profile repeating.For example, during fractional scanning data source, in user's key-value pair
On structured data, setting is labeled as 1, and marked user's key-value pair corresponding duplicate customer mark is abandoned.It is suitable for once complete
Use in the user distribution data forming process of whole mass data, can coordinate with fractional scanning.
Step 23: relative users key-value pair data is formed linear structure data；
Linear structure data adopts list structure, determines the thresholding of corresponding chained list node using key value preset.
Step 24: position the storage position in internal memory using the value in linear structure data, by the use of key as assignment foundation, assign
Value, forms the user distribution data in two-dimensional space.
Using the continuous addressing feature of internal memory, discrete user data is mapped in the continuation address of internal memory by key-value pair
In space, obtain the index information of position, and make full use of internal memory arithmetic speed, it is to avoid frequently interact with storage device.
Process forming process as shown in Fig. 1 b, Fig. 2 and Fig. 3, can be further simplified as dimension in mass data is used
The concrete position fixing process of amount, comprising:
The key value structure of pre-set user, with the ID of user as key, corresponding numerical value is key assignments；
Form the station location marker of user using key value structure；
Scan data source, positions the customer position information under single dimension using station location marker, forms user distribution data.
Fig. 4 is the formation flow process of the dimension data of localization method one embodiment of the dimension user to mass data for the present invention
Figure.Include as shown in Figure 4:
Step 31: storable data environment is set up by data capsule；
Set up data capsule using the container blob storing binary large object file in data base it is also possible to be programmed with individual
The dignified corresponding data type object of languageization forms container.
Step 32: memory range is determined according to the position data in user distribution data；
The maximum of position data may be used to determine the size of data capsule, the size of data capsule and position data numerical value
It is adapted, can subsequently expand, update with user's accumulation and be adapted.
Step 33: according to the position data in user distribution data data capsule corresponding positions assignment, in data capsule
Other positions filling；
In data capsule position corresponding with position data assignment 1, in other assignment 0.0 can also first be filled, then do
Corresponding assignment procedure.
Step 34: the content in data capsule is formed disk file, as the data of the position of the user of single dimension
File.
Form the corresponding storage of size position corresponding with memory address or byte using data capsule, by memory address
Information MAP is specific position or byte in disk file.For map user mark contiguous memory address in disk file
Formed relative starting position offset address it is ensured that mapping before and after position and spacing fixation.
In the data handling procedure of localization method one embodiment of the dimension user to mass data for the present invention, above-mentioned
In step 21, the user profile in the user data of other each dimensions in simultaneous extraction data source in a parallel fashion is accordingly follow-up
Step can form the data file of the position of the user of several single dimensions.
Fig. 5 is the shape of the various dimensions customer analysis of localization method one embodiment of the dimension user to mass data for the present invention
Become flow chart.Include as shown in Figure 5:
Step 41: obtain the data file of the position of the user of different single dimensions using data capsule；
Step 42: in data capsule, the corresponding positions of each data file are carried out with Boolean calculation, forms the knot of Boolean calculation
Infructescence arranges, the ident value in count results sequence, forms the number of users information of respective dimensions.
Single dimension location data file includes the positional information in the user distribution data being formed with position data
And user profile, the user profile labelling content using same position does necessary Boolean calculation, can make full use of software and hardware
The advantage of environment, completes data analysiss.
For example, in the data file of different dimensions user, the Boolean calculation of same position has following two kinds:
Position be that two binary digits are done and computing: when this two positions must all be 1, they with operation result just for 1,
It is otherwise 0；
Position or two binary digits do or computing: as long as this two positions have one be 1, they or operation result be just
1, otherwise for 0.
Blob storage location binary digit object is converted to the biginteger representing big integer data structure in java
Object；
If the number of users of one latitude of statistics, then biginteger pair is called to single dimension location data file
The bitcount () method of elephant, the number of statistics wherein binary one is it becomes possible to obtain the data of number of users.
If counting the number of users of multiple latitude combinations matches, then will be to multiple single dimension location data file phases
The position object answered carries out Boolean calculation, forms a binary sequence, then calculate 1 of the biginteger object after bit arithmetic
Number, just can obtain required number of users data.
Duplicate removal in above-described embodiment of the localization method of the dimension user to mass data of the present invention, can be with line
Property structured data structure optimization combine, form the forming process of another kind of user distribution data, comprising:
Replace with step 22: ID is compared with the user's key-value pair structured data in internal memory, formed and include repeating
The corresponding key-value pair data of user；
Adopt queue data structure in the linear structure data of step 23: relative users key-value pair data is formed queue,
Record comprises the user's key assignments repeating.
Accordingly, replace with step 33: according to the position data in user distribution data data capsule corresponding positions
Using Boolean calculation duplicate removal, assignment, in other positions of data capsule filling.
In the present embodiment, using the data structure changing linear structure data, change process step and the realization of duplicate removal
Position, so that duplicate removal utilizes rudimentary Boolean calculation as far as possible, it is to avoid the computing to high-level data object, further at raising
Rationality energy.
Fig. 6 is the mistake of the localization method one embodiment formation dimension user data of the dimension user to mass data for the present invention
Journey schematic diagram.As shown in fig. 6, the key-value pair that ID key value structure is formed is " user: numerical value ", a user corresponds to one only
One numerical value, numerical value is with progressive whole number continuous dispensing.Numerical value and the addresses match of internal memory, a numerical value corresponds to internal memory one address
(specially bit address or byte address).When calculating in the customer location of single dimension, such as in the data object of this dimension
User1,2,3,5,6 occur, then be computed mapping, the depositor in correspondence memory address puts 1, then in all user scopes
User profile sequence data position 1110110, this data can directly be processed as corresponding binary sequence shape using blob object
Become corresponding disk file, so that position and user profile is accordingly preserved with dimension, and for follow-up data analysiss.
The corresponding processing meanss that the localization method of the dimension user to mass data according to embodiments of the present invention is formed,
At least include:
User distribution data generating device, for scan data source, positions the user under single dimension using station location marker
Positional information, forms user distribution data；
Dimension user data generating means, for forming storable dimension user data according to user distribution data；
Multidimensional data analysis result generating means, between storable dimension user data, by Boolean calculation shape
Become multidimensional data analysis result.
Also include station location marker device, be used for setting up ID key value structure, form the station location marker of user.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Within god and principle, any modification of being made, equivalent etc., should be included within the scope of the present invention.
Claims (10)
1. a kind of localization method of the dimension user to mass data, comprises the following steps:
The key value structure of pre-set user, with the ID of user as key, corresponding numerical value is key assignments；
Form the station location marker of user using key value structure；
Scan data source, positions the customer position information under single dimension using station location marker, forms user distribution data.
2. the localization method of the dimension user to mass data as claimed in claim 1, described utilization key value structure is formed to be used
The station location marker at family, comprising:
Scan data source, obtains user profile, extracts the unique subscriber identification of user；
Distribute unique Digital ID for each user；
Unique subscriber identification and unique Digital ID are formed the key-value pair data of user.
3. the localization method of the dimension user to mass data as claimed in claim 1, described scan data source, using position
Customer position information under the single dimension of mark location, forms user distribution data, comprising:
Scan data source, extracts the user profile in the user data of single dimension in data source, forms corresponding ID；
ID is compared with user's key-value pair data, forms the corresponding key-value pair data of user；
Relative users key-value pair data is formed linear structure data；
Position storage position the assignment in internal memory using the value in linear structure data, form user distribution data.
4. the localization method of the dimension user to mass data as claimed in claim 3, described scan data source adopts segmentation
Or step scan.
5. the localization method of the dimension user to mass data as claimed in claim 3, single dimension in described extraction data source
User profile in the user data of degree, comprising:
User profile in the user data of several single dimensions of simultaneous extraction.
6. the localization method of the dimension user to mass data as claimed in claim 3, described linear structure data adopts chain
Table structure.
7. the localization method of the dimension user to mass data as claimed in claim 6, the linear junction in described list structure
Structure data deduplication.
8. the localization method of the dimension user to mass data as claimed in claim 3, described linear structure data adopts team
Array structure.
9. the localization method of the dimension user to mass data as claimed in claim 8, in described utilization linear structure data
Value position internal memory in storage position and go reassignment.
10. the localization method of the dimension user to mass data as claimed in claim 9, described goes reassignment to include:
Carry out Boolean calculation with storage position content in assignment procedure, use operation result assignment.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025140A (en) * | 2017-03-31 | 2017-08-08 | 北京快友世纪科技股份有限公司 | A kind of mass data analytic statistics methods based on HDFS clusters |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577583A (en) * | 2013-11-08 | 2014-02-12 | 北京首信科技股份有限公司 | Method for efficiently calculating number of users through large data |
| CN104063376A (en) * | 2013-03-18 | 2014-09-24 | 阿里巴巴集团控股有限公司 | Multi-dimensional grouping operation method and system |
| US20150375083A1 (en) * | 2014-06-05 | 2015-12-31 | Zih Corp. | Method, Apparatus, And Computer Program Product For Enhancement Of Event Visualizations Based On Location Data |
-
2016
- 2016-08-29 CN CN201610756179.2A patent/CN106354813A/en not_active Withdrawn
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104063376A (en) * | 2013-03-18 | 2014-09-24 | 阿里巴巴集团控股有限公司 | Multi-dimensional grouping operation method and system |
| CN103577583A (en) * | 2013-11-08 | 2014-02-12 | 北京首信科技股份有限公司 | Method for efficiently calculating number of users through large data |
| US20150375083A1 (en) * | 2014-06-05 | 2015-12-31 | Zih Corp. | Method, Apparatus, And Computer Program Product For Enhancement Of Event Visualizations Based On Location Data |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025140A (en) * | 2017-03-31 | 2017-08-08 | 北京快友世纪科技股份有限公司 | A kind of mass data analytic statistics methods based on HDFS clusters |
| CN107025140B (en) * | 2017-03-31 | 2018-03-09 | 北京快友世纪科技股份有限公司 | A kind of mass data analytic statistics methods based on HDFS clusters |
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Application publication date: 20170125 |