CN108460097A - A kind of intelligent screening system of big data - Google Patents
A kind of intelligent screening system of big data Download PDFInfo
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- CN108460097A CN108460097A CN201810100142.3A CN201810100142A CN108460097A CN 108460097 A CN108460097 A CN 108460097A CN 201810100142 A CN201810100142 A CN 201810100142A CN 108460097 A CN108460097 A CN 108460097A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/71—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/335—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
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- 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
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
An embodiment of the present invention provides a kind of intelligent screening systems of big data, including：Analysis module, for carrying out screening analysis to the big data in big data group to be screened according to target dimension screening dimension；Preserving module, for data group to be screened that preset condition requires, saving as next round corresponding to the data of at least one dimension subitem under the object filtering dimension will to be met；Screening module in the case of determining that number of screening round meets default screening quantity, terminates the screening process of the big data for the quantity and target call according to preset screening dimension.The present invention is analyzed by multi-turns screen and carries out Stepwise Screening to data, will not caused by data volume is excessive system burden it is excessive to collapse the problem of, and reference value of the target call according to data group to be screened in wheel screening analysis is arranged, and improves the accuracy of screening analysis.
Description
Technical field
The present invention relates to electronic technology field more particularly to a kind of intelligent screening systems of mutual big data.
Background technology
With information-based high speed development, big data is come into being, in order to make up tradition can not handle so measure it is big and non-
The defect of the big data of structure, people have investigated cloud computing, and hand is shared and excavated to information storage based on cloud computing
Section, it is marked down, effectively these are a large amount of, high speed, diverse terminal big data store, however how to these numbers
According to carrying out screening analysis, and guidance is carried out to business decision from different dimensions using the selection result and has become hot issue.
In the prior art, to the screening of data analysis be only deployment analysis is carried out under certain single dimension to data, or
Screening is combined under multiple dimensions.Screening defect under single dimension is if data information point is hidden in multiple screenings
Then it is difficult to be found under dimension；When the defect of combined sorting is to determine certain dimension subitem to carry out data analysis, subitem
Selection is largely dependent on the experience of the people judged, leads to the estimate of situation for being susceptible to mistake.Either single dimension
The screening mode of degree or the screening mode for combining dimension, for can not due to having selected the screening dimension of mistake in screening process
When obtaining final the selection result, it is required to re-start screening, seriously affects screening efficiency.
For example, in video field, usually realized on the operational platform to target information by the combination of different screening dimensions
Flow or interim card situation monitoring analysis, screening dimension include：Region, city, operating system, browser, gender, age
Section etc., the monitoring of the prior art are to choose its subitem respectively in all screening dimensions according to previous experience to carry out target information
Combined sorting is analyzed, if the target information is exactly problem information point, completes to monitor, and otherwise chooses screening dimension again
Other permutation and combination of item carry out screening analysis and complete monitoring.Although the prison to information such as video flow, video cardtons can be realized
It surveys, but entire processing procedure information processing capacity is big, causes processor burden larger, treatment effeciency is low, is unfavorable for promoting and applying.And
And it even if using the information point for having found doubtful problem, due to there is the possibility of other a large amount of permutation and combination, also is difficult to really
It is exactly optimal to recognize the information point.
Invention content
The embodiment of the present invention is designed to provide a kind of intelligent screening system of big data, to improve the standard of screening analysis
Exactness.
In order to achieve the above object, the embodiment of the invention discloses a kind of intelligent screening systems of big data, including：
Analysis module carries out screening point for screening dimension according to target dimension to the big data in big data group to be screened
Analysis；
Preserving module, for will meet preset condition requirement, corresponding at least one dimension under the object filtering dimension
The data of subitem save as the data group to be screened of next round；
Screening module determines that number of screening round meets default screening for the quantity and target call according to preset screening dimension
In the case of quantity, terminate the screening process of the big data.
Optionally, the screening module, is specifically used for：
The selection result table is established, the selection result of each round is put into the selection result table；According to the number of preset screening dimension
Amount and target call determine whether number of screening round terminates to meet default screening quantity according to the selection result table.
Optionally, further include：Enquiry module.
Optionally, the enquiry module is used to the index of the selection result include being established and being indexed according to screening conditions, passed through
The page number being stored in the index finds corresponding record in the selection result table.
Optionally, further include：Generation module.
Optionally, the generation module be used for it is described by meet target call, corresponding under the screening dimension
After the data of at least one dimension subitem save as the data group to be screened of next round, corresponding screening path is generated and preserves,
And can be recalled in each round screening analysis, after recalling, the screening road that has generated and preserved under the screening analysis recalled
Diameter is deleted.
Optionally, the target call is data in the data group to be screened corresponding numerical value under each dimension subitem
It is maximum or minimum, and the absolute value of the difference of greatest measure and minimum value is more than predetermined threshold；Or data under each dimension subitem
Corresponding numerical value is more than preset range relative to the fluctuation range of reference value.
Compared with prior art, the beneficial effects of the present invention are：
Screening analysis provided by the invention and system carry out Stepwise Screening to pending data by multiple screening dimensions, are formed
Multi-turns screen is analyzed, and each round screening analysis is all to analyze data to be screened using last round of the selection result as epicycle screening
Group so that data volume of the often wheel screening analysis all than last round of screening analysis is small, therefore with the prior art disposably in multiple sieves
Screening is combined under the conditions of choosing to compare, it is not easy to caused by data volume is excessive system burden it is excessive to collapse the problem of, and
Reference of the target call to be met all in accordance with its data group to be screened under the screening subitem of the wheel in each round screening analysis
Value setting, improves the accuracy of screening analysis.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is the first structural schematic diagram of the intelligent screening system of the big data of the embodiment of the present invention.
Fig. 2 is second of structural schematic diagram of the intelligent screening system of the big data of the embodiment of the present invention.
Fig. 3 is the third structural schematic diagram of the intelligent screening system of the big data of the embodiment of the present invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Fig. 1 is the flow diagram of the intelligent screening system of big data provided in an embodiment of the present invention, including：
Analysis module 1 carries out screening point for screening dimension according to target dimension to the big data in big data group to be screened
Analysis；
Preserving module 2, for will meet preset condition requirement, corresponding at least one dimension under the object filtering dimension
The data of subitem save as the data group to be screened of next round；
Screening module 3 determines that number of screening round meets default screening for the quantity and target call according to preset screening dimension
In the case of quantity, terminate the screening process of the big data.
Optionally, the screening module 3, is specifically used for：
The selection result table is established, the selection result of each round is put into the selection result table；According to the number of preset screening dimension
Amount and target call determine whether number of screening round terminates to meet default screening quantity according to the selection result table.
Further, further include：Enquiry module 4, for including establishing rope according to screening conditions by the index of the selection result
Draw, corresponding record in the selection result table is found by the page number being stored in the index.
Further, further include：Generation module 5, for it is described by meet target call, correspond to the screening dimension
After the data of at least one dimension subitem under degree save as the data group to be screened of next round, corresponding sieve is generated and preserved
Routing diameter, and can be recalled in each round screening analysis, after recalling, generates and preserved under the screening analysis recalled
Path is screened to be deleted.
Further, the target call is that the data in the data group to be screened are corresponding under each dimension subitem
Numerical value is maximum or minimum, and the absolute value of the difference of greatest measure and minimum value is more than predetermined threshold；Or under each dimension subitem
The corresponding numerical value of data is more than preset range relative to the fluctuation range of reference value.
By being set to the attribute that data have in the embodiment of the present invention, and it is that can screen the attribute setup of adaptation
Attribute to get to screening dimension.The screening analysis of embodiment illustrated in fig. 1 carries out more wheel sieves by multiple screening dimensions to data
Choosing analysis obtains the selection result, and each round screening analysis is all to be screened using last round of the selection result as epicycle screening analysis
Data group so that data volume of the often wheel screening analysis all than last round of screening analysis is small, therefore with the prior art disposably more
Screening is combined under a screening conditions to compare, it is not easy to excessive the asking to collapse of system burden caused by data volume is excessive
Topic, and each round screening analysis in the target call to be met all in accordance with its data group to be screened under the screening subitem of the wheel
Reference value is arranged, and improves the accuracy of screening analysis.
When the data of unmet target call are analyzed in the screening of a certain wheel, if no longer reselect screening dimension into
Row screening analysis, then the screening path before showing is wrong, recalls wrong screening analysis, deletes under the screening analysis recalled
The screening path for generating and preserving.In screening analytic process, if it find that the dimension subitem of the selection of a certain wheel is wrong, sieve
Routing diameter is incorrect, by recalling wheel screening analysis and deleting the screening path so that remove the wheel in multi-turns screen analysis
The data that screening analysis obtains become the data group to be screened of next round, can reselect deletion to avoid from the data of most initial
The screening dimension of the wheel dimension subitem or its subitem carry out the trouble of screening analysis.
The embodiment of the present invention advanced optimizes, and the target call in the embodiment of the present invention includes：In data group to be screened
The corresponding numerical value of data is maximum, the corresponding numerical value of data in data group to be screened is minimum and greatest measure and minimum value
Absolute value of the difference be more than predetermined threshold；Or the corresponding numerical value of data is big relative to the fluctuation range of reference value under each dimension subitem
In preset range.Predetermined threshold, reference value and preset range are determined according to the historical data in historical data base.The present invention is real
A large amount of historical results data that example can have system are applied as reference, and with this given threshold and range, using waiting sieving
Maximum value, minimum value and predetermined threshold or reference value and preset range in data group under dimension subitem is selected to carry out screening point
Analysis, and the selection result that screening analysis obtains every time is maintained in historical data base, is coached, is gone through for later screening analysis
History database constantly by more and more accurate data extending and update, compared with the prior art in the selection made according to personal experience
Carry out accuracy higher for screening analysis.
Illustratively, industry wants to check that user in certain specific time period watches flow that video uses to send out on service platform
Now when hiding information, multiple screening dimensions, such as region, operating system, browser are first set, wherein under each screening conditions
There is respective dimension subitem, for example, region includes the part province of the China such as Beijing, Shanghai, Tianjin, Guangdong, operating system
Including Windows, Android, IOS system, browser includes 360 browsers, baidu browser, Google's browser.
First round screening analysis is executed, process is as follows.
The flow that data in initial data base, that is, user's viewing video is used is as data group to be screened, random selection
One screening dimension, such as region, are screened under the screening dimension.Target call determination unit determines wheel screening analysis
Middle target call is the maximum value and minimum value that user uses flow under the subitem for searching out region dimension, and maximum value and minimum
The difference of value is more than predetermined threshold, and predetermined threshold is determined as 1000T by predetermined threshold determination unit and historical data base.
The user that the ground such as Beijing, Shanghai, Tianjin, Guangdong are obtained by screening analytic unit watches the flow that video uses：
Pekinese user has used 568T, the user in Shanghai to use 642T, the user of Tianjin that 295T, the user in Guangdong has been used to use
1546T.Thus it is Guangdong 1546T to obtain maximum value, and minimum value is Tianjin 295T, while the difference of maximin is 1251T,
More than predetermined threshold 1000T.Meet data demand, therefore data group to be screened using flow under dimension subitem Guangdong and Tianjin
Generation unit is by the data group to be screened for saving as next round using flow in Guangdong and Tianjin.Also, it is as shown at 203, next
After the data group to be screened of wheel is saved, screening path processing unit generates and preserves corresponding screening path.
Execute the second wheel screening analysis.
Data group to be screened has been changed to Tianjin, In Guangdong Province user watches the flow of video.Selection operation system conduct
The screening dimension of epicycle, target call determination unit determine that target call is to search out operating system dimension in wheel screening analysis
Subitem under user use the maximum value of flow, while calculated minimum, and the difference of maximum value and minimum value is more than predetermined threshold,
Predetermined threshold is determined as 50T by predetermined threshold determination unit and historical data base in epicycle screening analysis.
The user that In Guangdong Province is obtained by screening analytic unit is seen using Windows, Android and IOS operating system
See that the flow that video uses is respectively 658T, 423T and 460T, the user of Efficiency in Buildings in Tianjin Area uses Windows, Android and IOS
The flow that operating system viewing video uses is 132T, 95T and 60T respectively, and the user for thus obtaining In Guangdong Province uses flow
Maximum value be 658T, minimum value 423T, the difference of maximin is 235T；The family of Efficiency in Buildings in Tianjin Area uses the maximum value of flow
Difference for 132T, minimum value 60T, maximin is 72T.Two regional maximins are all higher than predetermined threshold, therefore
The flow for the user that Windows systems are used under the flow of the user of Windows systems and Efficiency in Buildings in Tianjin Area is used under In Guangdong Province
Meet target call.Therefore data group generation unit to be screened sees the user of Guangdong and Tianjin under using Windows systems
See that flow that video uses saves as the data group to be screened of next round.Also, as shown at 203, the data to be screened of next round
After group is saved, screening path processing unit generates and preserves corresponding screening path.
Execute third round screening analysis.
Screening dimension is browser, and subitem is 360 browsers, baidu browser and Google's browser.Target call determines
Unit determines that the target call in epicycle screening analysis is the maximum that user uses flow under the subitem for searching out browser dimension
Value, while calculated minimum, and the difference of maximum value and minimum value is more than predetermined threshold, predetermined threshold is by pre- in epicycle screening analysis
Determine threshold value determination unit and historical data base is determined as 3 multiple values of minimum value under each subitem.
In Guangdong Province Windows user, which is obtained, by screening analytic unit uses 360 browsers, baidu browser and Google
The flow that uses of browser viewing video is respectively 75T, 31T and 158T, Efficiency in Buildings in Tianjin Area Windows user using 360 browsers,
The flow that baidu browser and Google's browser viewing video use is 12T, 5T and 23T respectively, thus obtains In Guangdong Province
Windows user is 158T using the maximum value of flow, and the difference of minimum value 31T, maximin are 127T, are more than predetermined threshold
Value 92T；Efficiency in Buildings in Tianjin Area Windows user is 23T using the maximum value of flow, and the difference of minimum value 5T, maximin are
18T is more than predetermined threshold 15T.The Windows user in two areas uses flow in wheel screening analysis under respective subitem
Maximin is all higher than predetermined threshold, thus In Guangdong Province Windows user using Google's browser viewing video flow and
The flow that Efficiency in Buildings in Tianjin Area Windows user watches video using Google's browser meets target call.Data group to be screened at this time
The Windows user of Guangdong and Tianjin is watched the flow that video uses under Google's browser and saves as next round by generation unit
Data group to be screened.Also, after as shown at 203, the data group to be screened of next round is saved, screening path processing unit life
Path is screened accordingly at preservation.
By judging that obtaining the screenings analysis under all screening dimensions is performed both by and finishes, therefore the selection result is third round screening
Data group to be screened is obtained in analysis, i.e. the Windows user in Guangdong and Tianjin watches what video used under Google's browser
Flow.The selection result is stored in historical data base to update historical data base.Path is screened in third round screening analysis
The screening path that processing unit is generated and preserved can make as the flow for inquiring user's viewing video in the specific time next time
With the entrance of the query composition of situation.
It should be noted that herein, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion so that the process including a series of elements, article or equipment include not only those elements, and
And further include other elements that are not explicitly listed, or further include for this process, article or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, article or equipment.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all
Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention
It is interior.
Claims (7)
1. a kind of intelligent screening system of big data, which is characterized in that including：
Analysis module carries out screening point for screening dimension according to target dimension to the big data in big data group to be screened
Analysis；
Preserving module, for will meet preset condition requirement, corresponding at least one dimension under the object filtering dimension
The data of subitem save as the data group to be screened of next round；
Screening module determines that number of screening round meets default screening for the quantity and target call according to preset screening dimension
In the case of quantity, terminate the screening process of the big data.
2. a kind of intelligent screening system of big data according to claim 1, which is characterized in that the screening module, tool
Body is used for：
The selection result table is established, the selection result of each round is put into the selection result table；According to the number of preset screening dimension
Amount and target call determine whether number of screening round terminates to meet default screening quantity according to the selection result table.
3. a kind of intelligent screening system of big data according to claim 1, which is characterized in that further include：Enquiry module.
4. a kind of intelligent screening system of big data according to claim 3, which is characterized in that the enquiry module is used for
Include being established the index of the selection result and being indexed according to screening conditions, screening is found by the page number being stored in the index
As a result corresponding record in table.
5. a kind of intelligent screening system of big data according to claim 1, which is characterized in that further include：Generation module.
6. a kind of intelligent screening system of big data according to claim 5, which is characterized in that the generation module is used for
It is described by meet target call, corresponding to it is described screening dimension under at least one dimension subitem data save as it is next
After the data group to be screened of wheel, corresponding screening path is generated and preserved, and can recall in each round screening analysis, recalling
Afterwards, the screening path for having generated and having preserved under the screening analysis recalled is deleted.
7. a kind of intelligent screening system of big data according to claim 1, which is characterized in that the target call is institute
It is maximum or minimum to state the corresponding numerical value under each dimension subitem of the data in data group to be screened, and greatest measure and minimum number
The absolute value of the difference of value is more than predetermined threshold；Or fluctuation range of the corresponding numerical value of data relative to reference value under each dimension subitem
More than preset range.
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| CN107391724A (en) * | 2017-08-01 | 2017-11-24 | 佛山市深研信息技术有限公司 | A kind of screening technique of big data |
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| CN107391724A (en) * | 2017-08-01 | 2017-11-24 | 佛山市深研信息技术有限公司 | A kind of screening technique of big data |
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Application publication date: 20180828 |