CN108228821A - A kind of target object preferred method and system based on skyline algorithms - Google Patents
A kind of target object preferred method and system based on skyline algorithms Download PDFInfo
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- CN108228821A CN108228821A CN201711491176.1A CN201711491176A CN108228821A CN 108228821 A CN108228821 A CN 108228821A CN 201711491176 A CN201711491176 A CN 201711491176A CN 108228821 A CN108228821 A CN 108228821A
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Abstract
The embodiment of the invention discloses a kind of target object preferred method and system based on skyline algorithms, for choosing target object in object to be selected, first, obtain the set to be selected of object to be selected；Secondly, the optimum condition information of user's selection target object is obtained, wherein, optimum condition information is for the other screening conditions of at least two Attribute class of object to be selected；Finally；Target object is chosen in set to be selected according to optimum condition information and skyline algorithms.Utilize method and system disclosed by the embodiments of the present invention, it can make enterprise or individual in a large amount of enterprises to be selected during selection affiliate, can clearly, it is quick, accurately and efficiently select and meet desired small number of enterprise, so as to be substantially reduced range of choice, mitigate the workload of screening affiliate, working efficiency is improved, leakage choosing caused by effectivelying prevent human negligence or wrong choosing happen.
Description
Technical field
The present invention relates to data screening technical fields, excellent more particularly to a kind of target object based on skyline algorithms
Choosing method and system.
Background technology
Enterprise or individual are when being engaged in production and operating activities, it is often necessary to other enterprises or personal cooperation, therefore, selection
Appropriate affiliate is particularly important.When in face of different production and operating activities, to the criterion of affiliate often
Also different, so affiliate will not be fixed, this undoubtedly increases enterprise or the personal work when selecting affiliate
It measures.
By taking enterprise as an example, when selecting affiliate, due to competition element there are many, for example, Qua-ntile Regression, enterprise
Industry assets etc..Therefore, seek various aspects in numerous enterprises to be selected and all meet desired enterprise to be not easy to.At present, mostly
Number enterprise can all be judged, to select when being selected by the way of planning as a whole according to the information summary of each enterprise's various aspects
Most suitable enterprise.But this pool selection is usually all completed jointly by multiple staff, each staff is led to
It crosses subjective judgement and selects suitable enterprise, and subjective judgement is unfavorable for staff and selects really suitable enterprise, also,
It also is difficult to illustrate that the optimality of done selection embodies wherein.In addition, select one or more in numerous candidate enterprises
The workload of affiliate is very huge, and staff is easy to falsely drop or leak choosing in the selection process.Therefore, there is an urgent need for one
The advanced mode of kind makes enterprise or personal simplicity, accurately and efficiently selects affiliate.
Invention content
A kind of target object preferred method and system based on skyline algorithms are provided in the embodiment of the present invention, with solution
Certainly enterprise or individual's selection affiliate's workload in numerous corporations are larger, and are difficult to simplicity, accurately and efficiently select
The problem of desired affiliate.
In order to solve the above-mentioned technical problem, the embodiment of the invention discloses following technical solutions：
A kind of target object preferred method based on skyline algorithms, for choosing target object in object to be selected, institute
The method of stating includes：
The set to be selected of the object to be selected is obtained, each described object to be selected is respectively provided with multiple attribute classifications, each
Each attribute classification of object to be selected is corresponding, and there are one property values；
The optimum condition information that user selects the target object is obtained, the optimum condition information is for object to be selected
The other screening conditions of at least two Attribute class；
Target object is chosen in the set to be selected according to the optimum condition information and skyline algorithms.
Optionally, the set to be selected for obtaining the object to be selected, including：
It detects whether to receive primary election conditional information input by user, the primary election conditional information is included to described to be selected right
As the primary election range of the other property value of at least one Attribute class；
If detecting the primary election conditional information, chosen just in the object to be selected according to the primary election conditional information
Select object；
Using the set comprising all primary election objects as the set to be selected；
If the primary election conditional information is not detected, using the set comprising all objects to be selected as described to be selected
Set.
Optionally, the optimum condition information for obtaining user and selecting the target object, including：
Show all attribute classifications of the object to be selected；
Judge user whether according to attribute classification input optimum condition information；
If user inputs optimum condition information, the optimum condition information is obtained；
If user does not input optimum condition information, user's input is prompted.
Optionally, it is described that mesh is chosen in the set to be selected according to the optimum condition information and skyline algorithms
Object is marked, including：
According to the optimum condition information, the attribute classification that the optimum condition information is directed to is obtained, as conditional attribute,
And obtain the title and quantity of the conditional attribute；
Obtain the corresponding property value of conditional attribute of each object to be selected in the set to be selected；
The D dimension spaces of data point to be selected are included according to the title of the conditional attribute and quantity foundation, wherein, the D dimensions
The dimension D in space is equal to the quantity of the conditional attribute, and the dimension name of the D dimension spaces is the title of the conditional attribute,
Each described data point to be selected on the D dimension spaces respectively corresponds to an object to be selected in the set to be selected, and
The data value of each data point to be selected is the property value of corresponding object conditional attribute to be selected；
At least one optimal data point, the optimal data are chosen in all data points to be selected based on skyline algorithms
Point is better than in any one dimension of D dimension spaces or the data point to be selected equal to remaining non-optimal data point, and at least exists
It is better than the data point to be selected of remaining non-optimal data point in one dimension；
The corresponding object to be selected of the optimal data point is determined as the target object.
Optionally, it is described that at least one optimal data point is chosen in all data points to be selected based on skyline algorithms, packet
It includes：
Establish minimum outsourcing right angle geometric figure of all data points to be selected in D dimension spaces；
The midpoint equivalent in minimum outsourcing right angle geometric figure along each sideline is divided into 2 D powers region；
Judge whether in each region only comprising a data point to be selected or not comprising data point to be selected；
If so, comparing the data point to be selected in different zones, and optimal data point is determined according to comparison result；
If not, along the midpoint in each sideline, recursively equivalent is divided into 2 by the region comprising more than one data point to be selected
D powers region, until only comprising a data point to be selected or not comprising data point to be selected in each region；Compare not same district
Data point to be selected in domain, and optimal data point is determined according to comparison result.
Optionally, perform it is described by the region comprising more than one data point to be selected along each sideline midpoint recursively etc.
Before the step of amount is divided into 2 D power regions, further include：
A pair of region for being in diagonal positions is chosen according to the optimum condition information, diagonal positions are in described
Two regions in respectively an optional data point to be selected be compared, disadvantage will be in D dimension spaces dimension of taking up an official post
Give up in region where data point to be selected；
Judge to whether there is the region for including more than one data point to be selected in the region retained,
If so, in addition to the region being rejected, by the region comprising more than one data point to be selected along each sideline
Recursively equivalent is divided into 2 D powers region to point, and continues in the region for judging newly to divide with the presence or absence of the area that can be rejected
Domain；
If not, data point to be selected in comparing the region of reservation and determining optimal data point according to comparison result.
Optionally, the minimum outsourcing right angle geometric figure for establishing all data points to be selected in D dimension spaces, including：
The data point to be selected in each dimension of D dimension spaces with maximum value and the number to be selected with minimum value are obtained respectively
Strong point；
Make vertical line to the corresponding dimension of respective extreme value respectively using each data point to be selected with extreme value as basic point；
All vertical lines are intersected into the closing right angle geometric figure of composition as minimum outsourcing right angle geometric figure.
A kind of user's optimum decision system based on skyline algorithms, for choosing target object in object to be selected, including treating
Selected works close acquisition module, optimum condition data obtaining module and target object and choose module；Wherein,
The set acquisition module to be selected, for obtaining the set to be selected of the object to be selected, wherein, it treats described in each
Object is selected to be respectively provided with multiple attribute classifications, there are one property values for each attribute classification correspondence of each object to be selected；
The optimum condition data obtaining module, for obtaining the optimum condition information that user selects the target object,
The optimum condition information is for the other screening conditions of at least two Attribute class of object to be selected；
The target object chooses module, for being treated according to the optimum condition information and skyline algorithms described
Selected works choose target object in closing.
Optionally, the preferred set acquisition module, including detection module and set determining module to be selected；Wherein,
The detection module, for detecting whether primary election conditional information input by user is received, the primary election condition letter
Breath includes the primary election range to the other property value of at least one Attribute class of object to be selected；
The set determining module to be selected, for when detecting the primary election conditional information, according to the primary election condition
Information chooses primary election object in the object to be selected；Set comprising all primary election objects is treated into selected works as described in
It closes；When the primary election conditional information is not detected, using the set comprising all objects to be selected as the set to be selected.
Optionally, the target object chooses module, including conditional attribute acquisition module, property value acquisition module, space
Establish module, optimal data clicks modulus block and target object determining module；Wherein,
The conditional attribute acquisition module, for according to the optimum condition information, obtaining the optimum condition information needle
To attribute classification, as conditional attribute, and obtain the title and quantity of the conditional attribute；
The property value acquisition module, it is corresponding for obtaining the conditional attribute of each object to be selected in the set to be selected
Property value；
Module is established in the space, is established for the title according to the conditional attribute and quantity comprising data point to be selected
D dimension spaces, wherein, the dimension D of the D dimension spaces is equal to the quantity of the conditional attribute, and the dimension name of the D dimension spaces is
The title of the conditional attribute, each described data point to be selected on the D dimension spaces are corresponded in the set to be selected
One object to be selected, and the property value that the data value of each data point to be selected is corresponding object conditional attribute to be selected；
The optimal data clicks modulus block, is chosen at least in all data points to be selected for being based on skyline algorithms
One optimal data point, the optimal data point are better than or non-optimal equal to remaining in any one dimension of D dimension spaces
The data point to be selected of data point, and better than the data point to be selected of remaining non-optimal data point at least in a dimension；
The target object determining module, for the corresponding object to be selected of the optimal data point to be determined as the target
Object.
By above technical scheme as it can be seen that a kind of target object based on skyline algorithms provided in an embodiment of the present invention is excellent
Choosing method and system, for choosing target object in object to be selected, wherein, each object to be selected is respectively provided with multiple Attribute class
Not, there are one property values for each attribute classification of each object to be selected correspondence.The set to be selected of object to be selected is obtained first；So
Afterwards, the optimum condition information that uses when obtaining user's selection target object is included for object to be selected in the optimum condition information
The other screening conditions of at least two Attribute class, that is, the screening conditions included in optimum condition information reflect from multiple to be selected
When target object is screened in object, the other condition of Attribute class as screening foundation is required, and attribute classification at least two；
Finally, on the basis of skyline algorithms, target pair is chosen in the object to be selected of set to be selected according to optimum condition information
As.
So as to, using embodiment disclosed by the invention, make enterprise or individual when selecting affiliate, it can clearly, soon
It is prompt, accurately and efficiently selected in a large amount of enterprises and meet desired small number of enterprise, so as to be substantially reduced selection model
It encloses, mitigates the workload of screening affiliate, improve working efficiency, leakage choosing caused by preventing human negligence or wrong selection condition hair
It is raw.Also, the attribute type as screening conditions is more, and it is quick, accurate that embodiment disclosed by the invention can more embody it
Advantage so that policymaker can finally pick out affiliate from a small number of enterprises for best suiting screening conditions.
Description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, for those of ordinary skill in the art
Speech, without having to pay creative labor, can also be obtained according to these attached drawings other attached drawings.
Fig. 1 shows for a kind of flow of the target object preferred method based on skyline algorithms provided in an embodiment of the present invention
It is intended to；
Fig. 2 is the flow diagram of step S101 in a kind of Fig. 1 provided in an embodiment of the present invention；
Fig. 3 is the flow diagram of step S103 in a kind of Fig. 1 provided in an embodiment of the present invention；
Fig. 4 is the schematic diagram of data point to be selected in a kind of D dimension spaces provided in an embodiment of the present invention；
Fig. 5 is the flow diagram of step S1034 in a kind of Fig. 3 provided in an embodiment of the present invention；
Fig. 6 is the flow diagram of step S201 in a kind of Fig. 5 provided in an embodiment of the present invention；
Fig. 7 is a kind of schematic diagram for dividing minimum outsourcing right angle geometric figure provided in an embodiment of the present invention；
Fig. 8 is the schematic diagram of data point to be selected in relatively different zones provided in an embodiment of the present invention；
Fig. 9 is a kind of schematic diagram that region division result is expressed by tree data structure provided in an embodiment of the present invention；
Figure 10 is the flow of another target object preferred method based on skyline algorithms provided in an embodiment of the present invention
Schematic diagram；
Figure 11 is a kind of structural representation of user's optimum decision system based on skyline algorithms provided in an embodiment of the present invention
Figure；
Figure 12 is the structure diagram for preferably gathering acquisition module 11 in a kind of Figure 11 provided in an embodiment of the present invention；
Figure 13 is the structure diagram that target object chooses module 13 in a kind of Figure 11 provided in an embodiment of the present invention.
Specific embodiment
In order to which those skilled in the art is made to more fully understand the technical solution in the present invention, below in conjunction with of the invention real
The attached drawing in example is applied, the technical solution in the embodiment of the present invention is clearly and completely described, it is clear that described implementation
Example is only part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
Technical staff's all other embodiments obtained without making creative work, should all belong to protection of the present invention
Range.
With the continuous development of database technology and the extensive use of data base management system, the data that are stored in database
Amount sharply increases, and the information that people are most interested in how is found out from mass data, and effective decision service is made for people, into
For an important research topic.Skyline algorithms are exactly to be extracted not by other any data objects from-a database
The set of data objects of domination.
It is multiobjective decision-making that skyline, which calculates most important application, and a classical example is hotel selection.An assuming that trip
Visitor will spend a holiday toward some city tour, it is desirable to found from numerous hotel informations-a price is cheaply from seabeach near trip again
Shop.However, price, with being often but conflicting apart from the two goal conditions, the hotel near from seabeach is often more expensive, and
Cheap is then distant, select it is less expensive or closer to or compromise hotel depend on tourist personal preference.For
Different tourists, travel agency can not accurately judge that their tendency and make optimal selection for it respectively, but can be big from this
The all possible interested information of any tourist is found out in the hotel of amount, i.e., all unlike other hotels on any-a goal condition
The hotel of difference, that is to say the skyline of this database of hotels.Based on skyline, guests just can be far smaller than former from this
In the skyline set of beginning database, according to the preference of oneself, easily make a choice.Because by skyline properties it is found that nothing
To the preference of the two goal conditions what kind of is by tourist, that point for best suiting requirement must be in skyline set.
Skyline algorithms are applied to the preferred of target object by the present invention, are filtered out and are met from a large amount of object to be selected
Desired target object.By taking enterprise or individual select affiliate as an example, based on embodiment disclosed by the invention, enterprise or individual
It can be quick, easy, accurately and efficiently select and best suit expectation in numerous enterprises to be selected, according to its own needs
One or more Target Enterprises, consequently facilitating policymaker in Target Enterprise determine affiliate, effectively improve screening cooperation
The working efficiency of partner.
Fig. 1 shows for a kind of flow of the target object preferred method based on skyline algorithms provided in an embodiment of the present invention
It is intended to, for choosing target object in object to be selected, as shown in Figure 1, this method includes the following steps：
Step S101:Obtain the set to be selected of object to be selected.
Wherein, each object to be selected is respectively provided with multiple attribute classifications, and each attribute classification of each object to be selected is right
It should be there are one property value.For example, certain attribute classification of certain object to be selected is corresponding with a property value, the property value is to be selected right for this
As the numerical value shown in the attribute classification.In one particular embodiment of the present invention, object to be selected can be enterprise to be selected,
Each enterprise to be selected is respectively provided with a variety of attribute classifications, which can be Qua-ntile Regression, enterprise assets, enterprise's building
Area, enterprise set up time etc..Each attribute classification corresponds to a property value, and e.g., the Qua-ntile Regression of a enterprises is 500 people,
Enterprise assets are 500,000,000, and enterprise's construction area is 10000 square metres, and enterprise is 2005 the establishment time.
The set to be selected of object to be selected is obtained, in one embodiment of the invention, the set to be selected of object to be selected can be with
Including all objects to be selected, will all object sets to be selected together, obtain set to be selected.
In one embodiment of the invention, the set to be selected of object to be selected can only include part object to be selected, obtain
The set to be selected of object to be selected can be realized, as shown in Figure 2 by following sub-step.
Step S1011：It detects whether to receive primary election conditional information input by user, primary election conditional information is included to be selected
The primary election range of the other property value of at least one Attribute class of object.
Whether COMPUTER DETECTION receives primary election conditional information input by user, and primary election conditional information includes object to be selected extremely
Few other primary election range of an Attribute class, for screening all objects to be selected roughly, to reduce object to be selected in set to be selected
Quantity improves the efficiency for finally screening target object.
In one embodiment disclosed by the invention, primary election conditional information input by user can be：User is using selection
Or the mode filled in chooses one or more attribute classifications, and other property value setting range to each Attribute class.For example, with
Family inputs Qua-ntile Regression in 100 people between 500 people, then primary election conditional information includes：Attribute classification is enterprise's number rule
Mould, primary election ranging from 100-500.
If detecting primary election conditional information, step S1012 is performed.
S1012:Primary election object is chosen in object to be selected according to primary election conditional information.
It is obtained to carry out the attribute classification of primary election and the other category of Attribute class to all objects to be selected by primary election conditional information
The primary election range of property value.According to the attribute classification and the primary election range of the other property value of Attribute class, object to be selected is screened,
The object to be selected obtained after screening is primary election object, so as to complete the rough screening to all objects to be selected.For example, primary election item
Part information is：Attribute classification is Qua-ntile Regression, primary election ranging from 100-500, then according to the content of the primary election conditional information,
It can be using the object select to be selected that Qua-ntile Regression is 100-500 out as primary election object.
Step S1013:Using the set comprising all primary election objects as set to be selected.
Gather primary election object all in step S1012, form set to be selected.
If primary election conditional information is not detected, step S1014 is performed.
Step S1014:Using the set comprising all objects to be selected as set to be selected.
If not detecting primary election conditional information, illustrating user, there is no do not carry out some or certain several Attribute class
The range set of property value, therefore, user do not screen roughly object to be selected, but will be selected in all objects to be selected
Select target object.
By way of carrying out primary election to object to be selected in above-described embodiment, it can effectively reduce eventually for selection target pair
The number of objects to be selected of elephant reduces the workload needed for screening target object, effectively improves working efficiency.
Step S102:The optimum condition information of user's selection target object is obtained, optimum condition information is for be selected right
As the other screening conditions of at least two Attribute class.
The optimum condition information that user is used for selection target object is obtained, optimum condition information can be user's selection or fill out
The information that the mode write inputs based on optimum condition information, chooses desired target object in set to be selected.
It is the other screening conditions of at least two Attribute class of object to be selected in optimum condition information, which is
The foundation of target object is selected in object to be selected, mesh is selected in the object to be selected of set to be selected according to optimum condition information
Mark object.
For example, it is preferable to conditional information includes the screening conditions of two attribute classifications of enterprise, Qua-ntile Regression and enterprise
The screening conditions of assets, wherein, the screening conditions of Qua-ntile Regression are to be the bigger the better, and the screening conditions of enterprise assets are more
Better.
In one embodiment of the invention, step S102 obtains the optimum condition information of user's selection target object, packet
Include following sub-step.
1) all attribute classifications of object to be selected are shown.
The all properties classification of object to be selected is shown on computer screen, user is made, which to grasp object to be selected, to be had
Attribute classification, so as to set the other screening conditions of Attribute class.
2) judge user whether according to attribute classification input optimum condition information.
After user knows all properties classification of object to be selected on a display screen, it can be inputted preferably to computer according to required
Conditional information, when which is user's selected objective target object, what the attribute classification of expectation target object should meet
Condition.If optimum condition information is only for an other filter information of Attribute class, do not need to using skyline algorithms
It is simple to obtain target object, only with the primary election condition in above-described embodiment.Therefore, in the present embodiment, this is preferred
Conditional information refers at least to two attribute classifications, that is, the setting condition at least meeting user there are two attribute classification just can be true
It is target object to be set to, so as to, using in embodiment disclosed by the invention based on skyline algorithms by the way of can just highlight it
Advantage.
The mode that user inputs optimum condition information can be selection input, for example, preset optimum condition information
Possible option determines optimum condition information in a manner that user selects；Alternatively, user inputs the mode of optimum condition information
It can also be and fill in input, optimum condition information is inputted by the way of text editing.
If user inputs optimum condition information, optimum condition information is obtained.
It obtains optimum condition information input by user and is stored in computer, it should during subsequently to screen target object
With.
If user does not input optimum condition information, user's input is prompted.
Step S103:Target object is chosen in set to be selected according to optimum condition information and skyline algorithms.
The attribute classification of user's concern can be known according to optimum condition information and for the other screening of Attribute class
Therefore condition, can choose target object based on skyline algorithms in set to be selected, the quantity of the target object can be one
It is a or multiple.
In one embodiment of the invention, the step S103 in above-described embodiment according to optimum condition information and
Skyline algorithms choose target object in set to be selected, as shown in figure 3, can include following sub-step.
Step S1031：According to optimum condition information, the attribute classification that optimum condition information is directed to is obtained, as condition category
Property, and obtain the title and quantity of conditional attribute.
Optimum condition information is for the other screening conditions of at least two Attribute class of object to be selected, is believed according to optimum condition
Breath can know the attribute classification of user's concern, and the attribute classification that above-mentioned user is paid close attention to is as conditional attribute.So as to, into
One step obtains the title and quantity of conditional attribute.
For example, it is preferable to conditional information is：Qua-ntile Regression is the bigger the better, and enterprise assets are The more the better, above-mentioned preferred
In conditional information, would know that conditional attribute title is respectively " Qua-ntile Regression ", and " enterprise assets ", the quantity of conditional attribute is
“2”。
Step S1032:Obtain the corresponding property value of conditional attribute of each object to be selected in set to be selected.
After determining conditional attribute according to optimum condition information in step S1031, obtain in this step each to be selected
The property value of the conditional attribute of object.
For example, conditional attribute is Qua-ntile Regression and enterprise assets, the object to be selected in set to be selected is that code name is A-G
7 enterprises, it is as shown in the table, obtain the corresponding property value of conditional attribute of each object to be selected in set to be selected.
| Enterprise | Qua-ntile Regression | Enterprise assets (million) |
| A | 1000 | 1000 |
| B | 1200 | 1500 |
| C | 100 | 400 |
| D | 600 | 800 |
| E | 2000 | 1000 |
| F | 700 | 1300 |
| G | 1700 | 500 |
Step S1033:The D dimension spaces of data point to be selected are included according to the title of conditional attribute and quantity foundation.
Wherein, the dimension D of D dimension spaces is equal to the quantity of conditional attribute, and the dimension name of D dimension spaces is the name of conditional attribute
Claim, each data point to be selected on D dimension spaces respectively corresponds to an object to be selected in set to be selected, and each number to be selected
The data value at strong point is the property value of corresponding object conditional attribute to be selected.
By taking the citing in above-mentioned steps S1032 as an example, object to be selected in set to be selected is A-G totally 7 enterprises, condition category
Property for Qua-ntile Regression and enterprise assets, quantity 2, as shown in figure 4, each enterprise corresponds to an independence data to be selected
Point, the data value of each data point to be selected is the property value of the conditional attribute of corresponding enterprise, for example, the conditional attribute of A enterprises
For Qua-ntile Regression and enterprise assets, as seen from the above table, the data value of the data point of A enterprises is (1000,1000)；B enterprises
Data point data value be (1200,1500).
The D dimension spaces for including data point to be selected are established, wherein, quantity of the D for conditional attribute, the dimension name of D dimension spaces
Title for conditional attribute.Therefore, as shown in figure 4, the D dimension spaces established are two-dimensional space, the title of two dimensions is respectively
Qua-ntile Regression and enterprise assets.
Step S1034:At least one optimal data point is chosen in all data points to be selected based on skyline algorithms, most
Excellent data point is better than in any one dimension of D dimension spaces or the data point to be selected equal to remaining non-optimal data point, and
It is better than the data point to be selected of remaining non-optimal data point at least in a dimension.
In Skyline algorithms, if the collection comprising all data points is combined into S, if the point P in set is tieed up at any one
All it is better than or equal to Q on degree, and is at least better than Q in a dimension, then claims P-domination Q.The collection that P points all in this way are formed
Close the result set for being just called skyline set and finally requiring.
Based on skyline algorithms, at least one optimal data point is chosen in all data points to be selected, the optimal data point
Better than or equal to remaining non-optimal data point in any one dimension for the D dimension spaces necessarily established in step S1033
Data point to be selected, and be better than at least in a dimension data point to be selected of remaining non-optimal data point, the i.e. optimal data
Point dominates the data point to be selected of remaining non-optimal data point.
Step S1035:The corresponding object to be selected of optimal data point is determined as target object.
Each data point to be selected corresponds to an object to be selected, and the corresponding object to be selected of optimal data point is determined as mesh
Mark object.
In one embodiment disclosed by the invention, the step S1034 in above-described embodiment is existed based on skyline algorithms
At least one optimal data point is chosen in all data points to be selected, as shown in figure 5, following sub-step can be included.
Step S201:Establish minimum outsourcing right angle geometric figure of all data points to be selected in D dimension spaces.
In D dimension spaces, the minimum outsourcing right angle geometric figure of all data points to be selected is established.In the reality of the present invention
It applies in example, as shown in fig. 6, the step may include following sub-step.
Step S2011:Data point to be selected in each dimension of D dimension spaces with maximum value is obtained respectively and with minimum
The data point to be selected of value.
According to the data value of each data point to be selected, obtain has the to be selected of extreme value respectively in each dimension of D dimension spaces
Data point.For example, 7 Enterprise Objects of A-G corresponding 7 data points to be selected on two-dimensional space, each data point to be selected
Data value is respectively A (1000,1000), B (1200,1500), C (100,400), D (600,800), E (2000,1000), F
(700,1300), G (1700,500), by the data value of each data point to be selected it is found that two-dimensional space Qua-ntile Regression
In dimension, there is the data point to be selected of extreme value for C and E, in enterprise assets dimension, have the data point to be selected of extreme value for B and
C obtains the data point to be selected in each dimension of two-dimensional space with maximum value or minimum value as a result,.
Step S2012:Make respectively to the corresponding dimension of respective extreme value using each data point to be selected with extreme value as basic point
Vertical line.
For example, the data of C points are minimum value in two dimensions of Qua-ntile Regression and enterprise assets, the data of B points
There is maximum value in enterprise assets dimension, the data of E points have maximum value in Qua-ntile Regression dimension, C, B, E are made
For basic point.Therefore, C points as basic point when corresponding dimension be Qua-ntile Regression and enterprise assets, B points as basic point when should point
Enterprise assets during dimension, E points as basic point when corresponding dimension be Qua-ntile Regression.
C points make vertical line respectively to Qua-ntile Regression dimension and enterprise assets dimension；B points hang down to enterprise assets dimension
Line；E makees vertical line to Qua-ntile Regression dimension.
Step S2013:All vertical lines are intersected into the closing right angle geometric figure of composition as minimum outsourcing right angle geometric graph
Shape.
Each basic point intersects to the vertical line that dimension is made in above-mentioned steps S2012, forms the right angle geometric figure of a closing,
As shown in fig. 7, in the space that the right angle geometric figure is formed, all data points to be selected are contained, therefore, by above-mentioned right angle geometry
Figure as all data points to be selected D dimension spaces minimum outsourcing right angle geometric figure.For example, in two-dimensional space, this is straight
Angle geometric figure is rectangular or square, and in three dimensions, which is cuboid or cube.
Step S202:The midpoint equivalent in minimum outsourcing right angle geometric figure along each sideline is divided into 2 D powers area
Domain.
For example, as shown in fig. 7, minimum outsourcing right angle geometric figure in two-dimensional space is rectangle, by the rectangle along each
The midpoint equivalent in sideline is divided into four regions, and by the region nearest apart from origin, deasil by aforementioned four etc.
Surface area is defined as first area, second area, third region and the fourth region.If D is 3, minimum outsourcing right angle geometry
Figure is cube, can the cube be divided into 9 regions along the midpoint equivalent in each sideline.
Step S203:Judge whether in each region only comprising a data point to be selected or not comprising data point to be selected.
Judge whether only to include a data point to be selected in each regional space or not comprising data point to be selected.
If so, perform step S204.
Step S204:Compare the data point to be selected in different zones, and optimal data point is determined according to comparison result.
For example, it is preferable to which conditional information is the bigger the better for Qua-ntile Regression, enterprise assets are The more the better, then as shown in Figure 8,
With A, two data points to be selected of B are representative, determine optimal data point in the case where A B have different data value respectively.
Each region divided is compared, a), hence it is evident that the abscissa of B points is more than the abscissa of A points, and the ordinate of B points
Also greater than the ordinate of A points, so B is better than A, also, it is possible thereby to any one data point to be selected positioned at third region is released
Better than it is located at the data point to be selected of first area.It, can be by entire first area in another embodiment disclosed by the invention
Give up, all the points that will be in first area are given up；B) it can be seen that the ordinate value of A points is more than the ordinate of B points, A
The abscissa value of point is more than the abscissa of B points, then A is better than B；C), the ordinate of A points is more than the ordinate of B points, and the horizontal seat of A
Abscissa of the mark equal to B, then A is better than B；D), the abscissa of B points is more than the abscissa of A, and the ordinate of B points is more than the vertical of A points
Coordinate, then B is better than A；E), the ordinate of A points is more than the ordinate of B points, but the abscissa of A points is less than the abscissa of B points, because
This, does not dominate mutually between A and B, and A is both not better than B, and B is also not better than A；F) in, the abscissa of B points is more than the abscissa of A points,
But the ordinate of A points is more than B point ordinates, so A, B are not dominated mutually.
Using the above method, compare the preference data point in different zones, and optimal data point is determined according to comparison result,
When optimum condition information difference, for example, it is preferable to conditional information for Qua-ntile Regression it is the smaller the better, enterprise assets it is more few more
Good, then comparison result also can follow optimum condition information accordingly to change.
If not, perform step S205.
Step S205:By the region comprising more than one data point to be selected, along the midpoint in each sideline, recursively equivalent is divided into
2 D powers region, until only comprising a data point to be selected or not comprising data point to be selected in each region.Then it performs
Step S204 compares the data point to be selected in different zones, and determines optimal data point according to comparison result.
For example, as shown in fig. 7, when including more than one data point to be selected in region, by the geometric space in the region after
The continuous midpoint along each sideline in region geometry space is equally divided into 2 D powers region, until in each region only
Comprising a data point to be selected or not comprising data point to be selected.
The above can be represented by tree data structure, for example, when D is 2, by minimum outsourcing right angle geometric figure
The result divided is by Quadtrees for Representing, as shown in figure 9, wherein, each layer of leaf node represents root node and divides phase
With the node obtained after number, when only including a data point to be selected in the division region that leaf node represents, the leaf section
Point stops dividing child node downwards, that is, the region that the leaf node represents stops continuing to be divided.
When in each region only comprising a data point to be selected or not comprising data point to be selected, according to Skyline algorithms
Compare the mode of data point, compare the data point to be selected in different zones, when some data point to be selected is better than or does not dominate other
During data point to be selected, which is determined as optimal data point.
For example, as shown in fig. 7, minimum outsourcing right angle geometric figure first is divided into 4 regions, B, E are located at third region,
C, D is located at first area, is divided according to optimum condition information " Qua-ntile Regression is the bigger the better, and enterprise assets are The more the better "
Analysis show that B, E point better than C, D point, give up C, D point；Continuing to divide, it is found that B, E point do not dominate mutually, A, F point do not dominate mutually,
Compare E, B point and A, F point again, obtain E better than A, B points are better than F, A, therefore give up A, F point；Finally comparing E and G points, to obtain E excellent
In G, so giving up G points, it is skyline points, i.e. optimal data point finally to retain two points of B, E.
In another embodiment disclosed by the invention, the step S205 in execution above-described embodiment will include more than one
The region of data point to be selected is along before the step of recursively equivalent is divided into 2 D power regions the midpoint in each sideline, such as Figure 10
It is shown, it is further comprising the steps of.
Step S301:A pair of region for being in diagonal positions is chosen according to optimum condition information, in diagonal position
A respective optional data point to be selected is compared in two regions put, and will be in disadvantage in D dimension spaces dimension of taking up an official post
Data point to be selected where region give up.
In one embodiment disclosed by the invention, first and third region or second, four regions are respectively a pair of be in diagonally
The region of line position, by taking first and third region as an example, a respective optional data point to be selected in first area and third region,
First area selects data point A, third regional choice data point B to be learnt according to optimum condition information, and data point B is better than data
Point A, and it can be inferred that any one data point to be selected positioned at third region be superior in first area any one is to be selected
Data point, therefore, entire first area are in a disadvantageous position compared with third region, which is given up, will this first
Data point to be selected in region is given up, in the comparison procedure of subsequent region division and data point to be selected, firstth area
Data point to be selected in domain and first area is not involved in.Data point more to be selected is greatly simplified as a result, obtains optimal data point
Work, and then effectively shorten obtain target object time, improve working efficiency.
Step S302:Judge with the presence or absence of the region for including more than one data point to be selected in the region retained, if so,
Perform step S205.
Step S205:By the region comprising more than one data point to be selected, along the midpoint in each sideline, recursively equivalent is divided into
2 D powers region.
After there is the region comprising more than one data point to be selected in the region for determining to retain, except the region being rejected
Outside, step S205 is performed, and is continued in the region for judging newly to divide with the presence or absence of the region that can be rejected.
After being rejected in first area, retained region is two, three, four, is more than being included in above three region
The region of one data point to be selected is recursively divided into four regions along the midpoint in each sideline according to the method described above, and continues to judge
With the presence or absence of the region that can be rejected in the region newly divided.
If not, perform step S303:Compare the data point to be selected in the region of reservation and determined most according to comparison result
Excellent data point.
Figure 11 is a kind of user's optimum decision system based on skyline algorithms, applied to the selection target pair in object to be selected
As the system includes：Acquisition module 11, optimum condition data obtaining module 12 and the target object to be selected gathered chooses module 13.
Set acquisition module 11 to be selected, for obtaining the set to be selected of object to be selected, wherein, each object to be selected has
There are multiple attribute classifications, there are one property values for each attribute classification correspondence of each object to be selected；
Optimum condition data obtaining module 12, for obtaining the optimum condition information of user's selection target object, preferred stripe
Part information is for the other screening conditions of at least two Attribute class of object to be selected；
Target object chooses module 13, for being selected in set to be selected according to optimum condition information and skyline algorithms
Take target object.
In another embodiment disclosed by the invention, the preferred set acquisition module 11 in above-described embodiment, such as Figure 12
It is shown, including detection module 111 and set determining module 112 to be selected；Wherein,
Detection module 111, for detecting whether receiving primary election conditional information input by user, primary election conditional information includes
To the primary election range of the other property value of at least one Attribute class of object to be selected；
Set determining module 112 to be selected, for when detecting primary election conditional information, according to primary election conditional information to be selected
Primary election object is chosen in object；Using the set comprising all primary election objects as set to be selected；Primary election condition letter is being not detected
During breath, using the set comprising all objects to be selected as set to be selected.
In another embodiment disclosed by the invention, target object chooses module 13, as shown in figure 13, including condition category
Property acquisition module 131, property value acquisition module 132, module 133 is established in space, optimal data clicks modulus block 134 and target pair
As determining module 135；Wherein,
Conditional attribute acquisition module 131, for according to optimum condition information, obtaining the Attribute class that optimum condition information is directed to
Not, as conditional attribute, and the title and quantity of conditional attribute are obtained；
Property value acquisition module 132, for obtaining the corresponding attribute of conditional attribute of each object to be selected in set to be selected
Value；
Module 133 is established in space, is established the D comprising data point to be selected for the title according to conditional attribute and quantity and is tieed up sky
Between, wherein, the dimension D of D dimension spaces is equal to the quantity of conditional attribute, and the dimension name of D dimension spaces is the title of conditional attribute, D
Each data point to be selected on dimension space corresponds to an object to be selected in set to be selected, and the number of each data point to be selected
According to property value of the value for corresponding object conditional attribute to be selected；
Optimal data clicks modulus block 134, and at least one is chosen in all data points to be selected for being based on skyline algorithms
A optimal data point, optimal data point is in any one dimension of D dimension spaces better than or equal to remaining non-optimal data point
Data point to be selected, and at least in a dimension be better than remaining non-optimal data point data point to be selected；
Target object determining module 135, for the corresponding object to be selected of optimal data point to be determined as target object.
It should be noted that herein, the relational terms of such as " first " and " second " or the like are used merely to one
A entity or operation with another entity or operate distinguish, without necessarily requiring or implying these entities or operation it
Between there are any actual relationship or orders.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Cover non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only include those
Element, but also including other elements that are not explicitly listed or further include as this process, method, article or set
Standby intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
Also there are other identical elements in the process including element, method, article or equipment.
It the above is only the specific embodiment of the present invention, make skilled artisans appreciate that or realizing of the invention.It is right
A variety of modifications of these embodiments will be apparent to one skilled in the art, general original as defined herein
Reason can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, the present invention will not
Be intended to be limited to the embodiments shown herein, and be to fit to it is consistent with the principles and novel features disclosed herein most
Wide range.
Claims (10)
1. a kind of target object preferred method based on skyline algorithms, special for choosing target object in object to be selected
Sign is, the method includes：
The set to be selected of the object to be selected is obtained, each described object to be selected is respectively provided with multiple attribute classifications, each to be selected
Each attribute classification of object is corresponding, and there are one property values；
Obtain the optimum condition information that user selects the target object, the optimum condition information be for object to be selected at least
Two other screening conditions of Attribute class；
Target object is chosen in the set to be selected according to the optimum condition information and skyline algorithms.
2. according to the method described in claim 1, it is characterized in that, the set to be selected for obtaining the object to be selected, including：
It detects whether to receive primary election conditional information input by user, the primary election conditional information is included to the object to be selected extremely
The primary election range of a few other property value of Attribute class；
If detecting the primary election conditional information, primary election pair is chosen in the object to be selected according to the primary election conditional information
As；
Using the set comprising all primary election objects as the set to be selected；
If the primary election conditional information is not detected, the set comprising all objects to be selected is treated into selected works as described in
It closes.
3. method according to claim 1 or 2, which is characterized in that described to obtain the excellent of user's selection target object
Conditional information is selected, including：
Show all attribute classifications of the object to be selected；
Judge user whether according to attribute classification input optimum condition information；
If user inputs optimum condition information, the optimum condition information is obtained；
If user does not input optimum condition information, user's input is prompted.
It is 4. according to the method described in claim 1, it is characterized in that, described according to the optimum condition information and skyline
Algorithm chooses target object in the set to be selected, including：
According to the optimum condition information, the attribute classification that the optimum condition information is directed to is obtained, as conditional attribute, and is obtained
Take the title and quantity of the conditional attribute；
Obtain the corresponding property value of conditional attribute of each object to be selected in the set to be selected；
The D dimension spaces of data point to be selected are included according to the title of the conditional attribute and quantity foundation, wherein, the D dimension spaces
Dimension D be equal to the quantity of the conditional attribute, the dimension names of the D dimension spaces is the title of the conditional attribute, the D
Each described data point to be selected on dimension space respectively corresponds to an object to be selected in the set to be selected, and each institute
The data value for stating data point to be selected is the property value of corresponding object conditional attribute to be selected；
At least one optimal data point is chosen in all data points to be selected based on skyline algorithms, the optimal data point is in D
In any one dimension of dimension space better than or the data point to be selected equal to remaining non-optimal data point, and at least one dimension
It is better than the data point to be selected of remaining non-optimal data point on degree；
The corresponding object to be selected of the optimal data point is determined as the target object.
5. according to the method described in claim 4, it is characterized in that, the skyline algorithms that are based on are in all data points to be selected
It is middle to choose at least one optimal data point, including：
Establish minimum outsourcing right angle geometric figure of all data points to be selected in D dimension spaces；
The midpoint equivalent in minimum outsourcing right angle geometric figure along each sideline is divided into 2 D powers region；
Judge whether in each region only comprising a data point to be selected or not comprising data point to be selected；
If so, comparing the data point to be selected in different zones, and optimal data point is determined according to comparison result；
If not, along the midpoint in each sideline, recursively equivalent is divided into 2 D time by the region comprising more than one data point to be selected
Side region, until only comprising a data point to be selected or not comprising data point to be selected in each region；Compare in different zones
Data point to be selected, and optimal data point is determined according to comparison result.
6. according to the method described in claim 5, it is characterized in that, described it will include more than one data point to be selected performing
Region is further included along before the step of recursively equivalent is divided into 2 D power regions the midpoint in each sideline：
A pair of region for being in diagonal positions is chosen according to the optimum condition information, it is described in diagonal positions two
A respective optional data point to be selected is compared in a region, will be in the to be selected of disadvantage in D dimension spaces dimension of taking up an official post
Give up in region where data point；
Judge to whether there is the region for including more than one data point to be selected in the region retained,
If so, in addition to the region being rejected, the region comprising more than one data point to be selected is passed along the midpoint in each sideline
Ground equivalent is returned to be divided into 2 D powers region, and is continued in the region for judging newly to divide with the presence or absence of the region that can be rejected；
If not, data point to be selected in comparing the region of reservation and determining optimal data point according to comparison result.
7. according to the method described in claim 5, it is characterized in that, described establish all data points to be selected in D dimension spaces most
Small outsourcing right angle geometric figure, including：
The data point to be selected in each dimension of D dimension spaces with maximum value and the data to be selected with minimum value are obtained respectively
Point；
Make vertical line to the corresponding dimension of respective extreme value respectively using each data point to be selected with extreme value as basic point；
All vertical lines are intersected into the closing right angle geometric figure of composition as minimum outsourcing right angle geometric figure.
8. a kind of user's optimum decision system based on skyline algorithms, for choosing target object in object to be selected, feature exists
In including set acquisition module to be selected, optimum condition data obtaining module and target object selection module；Wherein,
The set acquisition module to be selected, for obtaining the set to be selected of the object to be selected, wherein, each is described to be selected right
As being respectively provided with multiple attribute classifications, there are one property values for each attribute classification correspondence of each object to be selected；
The optimum condition data obtaining module, it is described for obtaining the optimum condition information that user selects the target object
Optimum condition information is for the other screening conditions of at least two Attribute class of object to be selected；
The target object chooses module, for treating selected works described according to the optimum condition information and skyline algorithms
Target object is chosen in conjunction.
9. system according to claim 8, which is characterized in that the preferred set acquisition module, including detection module and
Set determining module to be selected；Wherein,
The detection module, for detecting whether primary election conditional information input by user is received, the primary election conditional information packet
Include the primary election range to the other property value of at least one Attribute class of object to be selected；
The set determining module to be selected, for when detecting the primary election conditional information, according to the primary election conditional information
Primary election object is chosen in the object to be selected；Using the set comprising all primary election objects as the set to be selected；
When the primary election conditional information is not detected, using the set comprising all objects to be selected as the set to be selected.
10. system according to claim 8, which is characterized in that the target object chooses module, is obtained including conditional attribute
Modulus block, property value acquisition module, module is established in space, optimal data clicks modulus block and target object determining module；Wherein,
The conditional attribute acquisition module, for according to the optimum condition information, obtaining what the optimum condition information was directed to
Attribute classification as conditional attribute, and obtains the title and quantity of the conditional attribute；
The property value acquisition module, for obtaining the corresponding attribute of conditional attribute of each object to be selected in the set to be selected
Value；
Module is established in the space, is established the D comprising data point to be selected for the title according to the conditional attribute and quantity and is tieed up
Space, wherein, the dimension D of the D dimension spaces is equal to the quantity of the conditional attribute, and the dimension name of the D dimension spaces is institute
State the title of conditional attribute, each described data point to be selected on the D dimension spaces corresponds to one in the set to be selected
A object to be selected, and the property value that the data value of each data point to be selected is corresponding object conditional attribute to be selected；
The optimal data clicks modulus block, chooses for being based on skyline algorithms in all data points to be selected at least one
Optimal data point, the optimal data point is in any one dimension of D dimension spaces better than or equal to remaining non-optimal data
The data point to be selected of point, and better than the data point to be selected of remaining non-optimal data point at least in a dimension；
The target object determining module, for the corresponding object to be selected of the optimal data point to be determined as the target pair
As.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109976913A (en) * | 2019-03-29 | 2019-07-05 | 齐鲁工业大学 | Based on the Skyline service data selection method calculated and device |
| CN110569260A (en) * | 2019-08-02 | 2019-12-13 | 广州探迹科技有限公司 | automatic circulation method and system for enterprise clues |
| CN110704149A (en) * | 2019-09-24 | 2020-01-17 | 深圳市利深科技有限公司 | System and method for adapting enterprise and industrial space |
-
2017
- 2017-12-30 CN CN201711491176.1A patent/CN108228821A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109976913A (en) * | 2019-03-29 | 2019-07-05 | 齐鲁工业大学 | Based on the Skyline service data selection method calculated and device |
| CN110569260A (en) * | 2019-08-02 | 2019-12-13 | 广州探迹科技有限公司 | automatic circulation method and system for enterprise clues |
| CN110704149A (en) * | 2019-09-24 | 2020-01-17 | 深圳市利深科技有限公司 | System and method for adapting enterprise and industrial space |
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