CN109669969A - A kind of data service system and method - Google Patents
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Abstract
This application provides a kind of data service system and methods, the system includes: processor, database, output device, wherein: the data service request that processor response is sent in user side, data acquisition request is sent to the database, the data acquisition request is for obtaining target data corresponding with the data service request；The database root sends the target data according to the data acquisition request, Xiang Suoshu processor；The processor determines that the characteristic parameter of the target data, the characteristic parameter include the life parameters of index value and the target data of the target data under each quality index；According to the characteristic parameter of the target data, the value parameter of the target data is determined；The value parameter of the target data is sent to the output device；The output device exports the value parameter of the target data to the user side.Utilize the value parameter of the available relatively reliable data of the embodiment of the present application.
Description
Technical field
This application involves data analysis technique fields, in particular to a kind of data service system and method.
Background technique
In today of digital information rapid development, influence of the data to enterprise is increasingly enhanced, and more and more enterprises need
" being spoken with data ".For enterprise, specific gravity shared by business datum is increasing, and the value of business datum directly determines sometimes
Therefore how the value of enterprise determines the value of data, be a pressing issue.
Summary of the invention
In view of this, the application's is designed to provide a kind of data service system and method, obtained more with accurate
Reliable data value improves the application range of data.
In a first aspect, the embodiment of the present application provides a kind of data service system, comprising: processor, database, output dress
It sets, in which:
The data service request that the processor response is sent in user side, Xiang Suoshu database send data acquisition and ask
It asks, the data acquisition request is for obtaining target data corresponding with the data service request；
The database root sends the target data according to the data acquisition request, Xiang Suoshu processor；
The processor determines that the characteristic parameter of the target data, the characteristic parameter include the target data each
The life parameters of index value and the target data under quality index；According to the characteristic parameter of the target data, determine
The value parameter of the target data；The value parameter of the target data is sent to the output device；
The output device exports the value parameter of the target data to the user side.
Optionally, according to the characteristic parameter of the target data, the value parameter of the target data is determined, it is specific to wrap
It includes:
By the product of the life parameters and index value of the target data under each quality index, as the target
The value parameter of data.
Optionally, the characteristic parameter further includes associated data and the institute of each application field of the target data association
State the degree of correlation of target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
Optionally, according to the life parameters, the index value and the degree of correlation, the valence of the target data is determined
Value parameter specifically includes:
Determine the degree of correlation of the associated data of the target data and each application field and value；
By described and value, the product of index value and the life parameters of the target data under each quality index, make
For the value parameter of the target data.
Optionally, according to the degree of correlation as described in determining under type:
For each application field with the target data association, determine that each associated data of the application field is corresponding
Weight；
According to each associated data of the application field and the corresponding weight of each associated data, determine that the application field is corresponding
Predicted value；
According to the corresponding predicted value of the application field and the target data, determine that the target data and the application are led
The associated data multiple correlation coefficient in domain；
Using the multiple correlation coefficient as the degree of correlation of the target data and the associated data of the application field.
Optionally it is determined that the corresponding weight of each associated data of the application field, specifically includes:
It is returned using each associated data of the target data to the application field, obtains each association of the application field
The corresponding weight of data.
Optionally, the quality index is included at least with the next item down:
Data consistency index, data integrity index, data scarcity index and data age index.
Second aspect, the embodiment of the present application provide a kind of data service method, this method comprises:
The data service request that processor response is sent in user side sends data acquisition request, the number to database
According to acquisition request for obtaining target data corresponding with the data service request；
The database root sends the target data according to the data acquisition request, Xiang Suoshu processor；
The processor determines that the characteristic parameter of the target data, the characteristic parameter include the target data each
The life parameters of index value and the target data under quality index；According to the characteristic parameter of the target data, determine
The value parameter of the target data；The value parameter of the target data is sent to output device；
The output device exports the value parameter of the target data to the user side.
Optionally, according to the characteristic parameter of the target data, the value parameter of the target data is determined, it is specific to wrap
It includes:
By the product of the life parameters and index value of the target data under each quality index, as the target
The value parameter of data.
Optionally, the characteristic parameter further includes associated data and the institute of each application field of the target data association
State the degree of correlation of target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
Data service system provided by the embodiments of the present application obtains corresponding according to the data service request that user side is sent
Target data, determine that the characteristic parameter of target data, characteristic parameter include index value of the target data under each quality index
The value parameter of target data is determined according to the characteristic parameter of target data with the life parameters of target data.In this way, comprehensive
The life parameters of the consideration quality of data and data determine the value parameter of target data, so that the valence of the target data determined
Be worth it is relatively reliable, using determining value parameter may be target data application provide decision recommendation.
To enable the above objects, features, and advantages of the application to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only some embodiments of the application, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is a kind of structural schematic diagram of data service system provided by the embodiments of the present application；
Fig. 2 is a kind of flow diagram of data service method provided by the embodiments of the present application.
Specific embodiment
To keep the purposes, technical schemes and advantages of the embodiment of the present application clearer, below in conjunction with the embodiment of the present application
Middle attached drawing, the technical scheme in the embodiment of the application is clearly and completely described, it is clear that described embodiment is only
It is some embodiments of the present application, instead of all the embodiments.The application being usually described and illustrated herein in the accompanying drawings is real
The component for applying example can be arranged and be designed with a variety of different configurations.Therefore, below to the application's provided in the accompanying drawings
The detailed description of embodiment is not intended to limit claimed scope of the present application, but is merely representative of the selected reality of the application
Apply example.Based on embodiments herein, those skilled in the art institute obtained without making creative work
There are other embodiments, shall fall in the protection scope of this application.
The embodiment of the present application provides a kind of data service system, as shown in Figure 1, comprising: processor 11, database 12,
Output device 13, wherein processor 11, database 12, output device 13 are arranged in computer equipment, portable device, network
In the equipment such as equipment, such as: computer, laptop, tablet computer, mobile phone, portable equipment, mobile unit, processing
Device 11, database 12, output device 13 can be set in same equipment, also can be set in different equipment, specifically
It can be arranged according to the actual situation, here without limitation.Wherein, output device 13 can with but be not limited to display screen, touch screen
Deng.
The data service request that the processor 11 is sent in response to user side, Xiang Suoshu database send data acquisition and ask
It asks, the data acquisition request is for obtaining target data corresponding with the data service request.
In specific implementation, target data can according to user demand specified data, wherein target data can be
The data of every field, e.g., the traffic data of field of traffic, the meteorological data of meteorological field, agriculture field farm output number
According to etc.；User side can be but be not limited to computer, laptop, tablet computer, mobile phone, portable equipment, vehicle-mounted
The equipment such as equipment, wherein the equipment of user side can be same equipment with the equipment of application processor 11, or different
Equipment, the application to this not limit.
It should be noted that data service request, data acquisition request all can be but not be limited to HTTP (HyperText
Transfer Protocol, hypertext transfer protocol), TCP (Transmission Control Protocol, transmission control
Agreement) etc. formats request, target data mark is carried in data acquisition request, in order to obtain and target data mark pair
Answer data.
Target data mark is carried in the data service request that user side is sent, processor 11 is getting data service
After request, the target data mark that will acquire is carried is sent to database 12 in data acquisition request.
The database 12 sends the target data according to the data acquisition request, Xiang Suoshu processor.
In specific implementation, target data is previously stored in database, database root is obtained according to the data that processor is sent
Request is taken, returns to corresponding data to processor.
The processor 11 determines the characteristic parameter of the target data, and the characteristic parameter includes that the target data exists
The life parameters of index value and the target data under each quality index；According to the characteristic parameter of the target data, really
The value parameter of the fixed target data；The value parameter of the target data is sent to the output device.
Wherein, quality index include at least data consistency index, data integrity index, data scarcity index and
One in data age index；Life parameters characterize the life cycle of target data, and life parameters are bigger, characterize number of targets
It is longer according to the life cycle of itself；Value parameter characterizes the value of target data itself, and value parameter is bigger, characterizes target data
The value of itself is bigger, then the application value of the target data is bigger.
Processor 11 determines the characteristic parameter of target data after receiving target data, and this feature parameter includes at least
The life parameters (described below) of index value and determining target data of the target data under each quality index, is obtaining life
Parameter and target data refer to the target data in each quality after the index value under each quality index, by the life parameters
The product of index value under mark, the value parameter as the target data.Value parameter is higher, illustrates the value of target data
Higher, user can determine the value of target data according to value parameter, there is policy-making shadow in use to target data
It rings.Wherein, the corresponding relationship being previously stored in the equipment where processor between Data Identification and life parameters, processor exist
After receiving target data, the inquiry life corresponding with the target data matched Data Identification of mark from above-mentioned mapping table
Parameter, using the life parameters inquired as the life parameters of target data.
For example, user side it needs to be determined that the meteorological data in May, 2017 of meteorological field data value, user side passes through
Meteorological data mark is sent to processor by mobile terminal, and processor is sent the data acquisition request that meteorological data identifies is carried
To database, database root obtains Data Concurrent corresponding with meteorological data mark according to data acquisition request and gives processor；Place
It manages device and receives the corresponding meteorological data that database is sent；Further, processor determines meteorological data under each quality index
Index value, and determine meteorological data life parameters；Processor calculates the index under life parameters and each quality index
The product of value obtains the value parameter of meteorological data.Wherein, in the life parameters for determining meteorological data, if mapping table
In pre-stored meteorological data to identify corresponding life cycle be 5 years, it is determined that the life parameters of meteorological number are 5.
Wherein, processor 11 can determine the value parameter BV of target data according to the following formula:
BV=f1*f2*f3*f4*T
Wherein, BV is the value parameter of target data；f1For index value of the target data under data consistency index；f2
For index value of the target data under data integrity index；f3For index value of the target data under data scarcity index；f4
For index value of the target data under data age index；T is the life parameters of target data.
Other than aforesaid way can determine the value parameter of target data, number of targets can also be determined in the following manner
According to value parameter:
It further include the incidence number of each application field of the target data association in the characteristic parameter that processor 11 determines
According to the degree of correlation with the target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
It wherein, can be the applicable field of the target data with the application field of target data association, with target data
The number of associated concrete application field and associated application field can be set according to actual scene, here without limitation.Its
In, application field may include the fields such as the communications field, meteorological field, entertainment field, agriculture field, field of traffic.
Associated data is the data in the application field that target data may be used on, and target data is generally with associated data
The data that same period generates, for example the generation time section of target data and associated data is in October, 2017 to November.
Such as, target data is temperature data, and the application field with target data association can be field of traffic, agriculture field, with target
The associated data of the field of traffic of data correlation can be for data such as speed, accident rates, the agricultural with target data association
The associated data in field can be the data such as crop yield, crops sales volume.Processor response is in the data service of user side
Request, obtains associated data, i.e., the described data service request is also used to obtain each of target data association and answers from database
With the associated data in field.
Degree of correlation between the data for the application field that degree of correlation characterization target data and target data may be used on, phase
Guan Du is higher, and the correlation degree characterized between target data and the associated data of associated application field is higher, target data pair
The influence of the associated data of application field is bigger.
Processor 11 is after getting target data and associated data, further, determines target data in each quality index
Under index value (described below), the life parameters (determination process is same as above) of target data are determined, for target data
Associated each application field determines that target data and the application are led according to the associated data of target data and the application field
The degree of correlation (described below) of the associated data in domain；Determine the degree of correlation of the associated data of target data and each application field and
Value；By described and value, the product of index value and the life parameters of the target data under each quality index, as target
The value parameter of data.
For example, user side it needs to be determined that the meteorological data in May, 2017 of meteorological field data value, with meteorological field
Associated application field includes agriculture field and field of traffic, and the associated data of agriculture field is the farm output in May, 2018
Data, the associated data of field of traffic are the traffic congestion data in May, 2018, wherein meteorological data is target data.
By data service system provided by the embodiments of the present application determine the data value of meteorological data in the way of are as follows:
After processor gets corresponding meteorological data, farm output data and traffic congestion data, meteorological data is determined
The life parameters and meteorological data of index value, meteorological data under each quality index respectively with farm output data, hand over
The degree of correlation between logical congestion data；Calculate each degree of correlation and under value, life parameters and each quality index index value
Product obtains the value parameter of meteorological data.
Wherein, processor 11 can determine the value parameter BV of target data according to the following formula:
Wherein, BV is the value parameter of target data；riBetween target data and the associated data of i-th of application field
The degree of correlation；f1For index value of the target data under data consistency index；f2It is target data under data integrity index
Index value；f3For index value of the target data under data scarcity index；f4It is target data under data age index
Index value；N is the number with the application field of target data association, generally positive integer.
The output device 13 exports the value parameter of the target data to the user side.
After obtaining the value parameter of target data, the value parameter of target data is sent to output device 13, is exported
Device 13 can show value parameter in display screen, touch screen etc., alternatively, output device 13 is by the value parameter of target data
It is sent to user side.
In the degree of correlation for the associated data for determining target data and each application field, can in the following way in appoint
It anticipates a kind of realization:
Mode one: for each application field with target data association, according to the pass of target data and the application field
Join data, calculates the degree of correlation of the target data Yu the application field.
In specific implementation, it for each application field, calculates target data according to the first relatedness computation formula and is somebody's turn to do
The degree of correlation of the associated data of application field, wherein the dimension of target data and the dimension of associated data are identical.
Specifically, the degree of correlation of target data and any application field is calculated by the first following relatedness computation formula
R:
Wherein, x is target data, and y is the associated data with the application field of target data association；COV (x, y) is target
The covariance of data and associated data, VAR [x] are the variance of target data, and VAR [y] is the variance of associated data.
Have detailed introduction to the first relatedness computation formula in the prior art, is not repeated herein.
Mode two: for each application field with target data association, each associated data pair of the application field is determined
The weight answered；
According to each associated data of the application field and the corresponding weight of each associated data, determine that the application field is corresponding
Predicted value；
According to the corresponding predicted value of the application field and the target data, determine that the target data and the application are led
The multiple correlation coefficient of the associated data in domain；
Using the multiple correlation coefficient as the degree of correlation of the target data and the associated data of the application field.
Wherein, it in the corresponding weight of each associated data for determining the application field, specifically includes:
It is returned using each associated data of the target data to the application field, obtains each association of the application field
The corresponding weight of data.
Here, weight characterizes the degree of correlation between each associated data and target data, each association in different application field
The corresponding weight of data can be different, can also be identical, and the corresponding weight of different associated datas in same application field can not
Together, can also be identical, here without limitation.The mode of recurrence includes linear regression, nonlinear regression etc., it is preferable that is passed through
Linear regression returns target data and associated data.
In specific implementation, after the associated data for obtaining target data and each application field, for each application field,
Regression analysis is carried out using target data and application field associated data of the same period, alternatively, leading using belonging to target data
The historical data in domain and application field historical context data of the same period carry out regression analysis, obtain each pass of the application field
Join the weight of data.Wherein, when carrying out regression analysis, the target data and associated data used should be the same period
, e.g., target data is in July, 2018 to 8 parts of data, and associated data is also in July, 2018 to 8 parts of data.
When it is implemented, each associated data of any application field is directed to, with target data y to associated data x1、x2、…、
xkIt returns, obtains fit object data yt:
yt=β0+β1x1+…+βkxk
Wherein, ytFor fit object data, the i.e. corresponding predicted value of the application field, x1、x2、…、xkFor the application field
Each associated data, β1To βkThe respectively weight of each associated data of the application field.
It is exemplified below with target data y to associated data x1、x2、…、xkDo the process returned, it is assumed that target data
Sequence is { y1, y2, y3 }, and the associated data with associated any application field of target data is { x1, x2, x3 }, respectively will
Associated data and target data, which are input to regression analysis formula, to be had:
y1=β0+β1x1+…+β3x3
y2=β0+β1x1+…+β3x3
y3=β0+β1x1+…+β3x3
The weight of associated data is obtained to above three solving simultaneous equation.Here regression analysis formula is only for convenience of explanation
It is only identical as above situation in target data to include the case where three data instances are illustrated, and multiple weights solve, here
It does not repeat them here.
After the weight for obtaining each associated data, each associated data of the application field is updated to above-mentioned relation formula, is obtained
To the corresponding predicted value of the application field.
For example, continuing the example that a upper target data sequence is { y1, y2, y3 }, β is being obtained0、β1……β3Afterwards, it will close
Joining data is that { x1, x2, x3 } substitutes into yt=β0+β1x1+…+β3x3, obtain the corresponding predicted value of the application field.
It is obtaining being input to predicted value and target data with after the predicted value of any application field of target data association
Following formula obtains the coefficient of multiple correlation R of target data and the associated data of the application field:
Wherein, R is the multiple correlation coefficient of target data and the associated data of the application field；yiFor in target data
I data；ysFor the average value of data each in target data；ytFor the obtained prediction of the application field with target data association
Value；N is positive integer, the number for the data entry for generally including in target data.
For example, target data is { y1, y2 ... ... y30 }, then the average value y of target datas=(y1+y2+ ...+y30)/
30。
Index value of the target data under each quality index is determined according to the following contents:
Quality index includes data consistency index, and the data consistency index is for characterizing the data that target data includes
The degree of consistency of entry and target data description information, processor 11 determine data content that the target data is included with
The target data corresponds to the degree of consistency of description information；Determine that the data of the target data are consistent based on the degree of consistency
The index value of property index, and the degree of consistency is higher, characterizes the index value of the data consistency index of the target data
It is higher.
Specifically, it according to the degree of consistency between following one or more data contents and corresponding description information, determines
The degree of consistency of the data content that the target data is included description information corresponding with the target data, wherein data
The degree of consistency between content and corresponding description information is higher, and the index value of the data consistency index of the target data is got over
It is high:
First: data volume described in the description information of data volume and the target data that the target data includes；
The data content of target data is carried in the file of a certain format；Target data can be by a plurality of data strip
Mesh is constituted, and every data entry is made of multiple data elements, wherein data element is the most basic data sheet for constituting target data
Position.
For example, when target data is commodity price data, the data element of a target data can be with are as follows: product name, quotient
Product manufacturer, the place of production, production time, shelf-life, net content, nutritional ingredient, product batch number, on-sale date.
That is, target data is preferably the form of data entry, for the number with determining value parameter demand
The case where according to for text data, can carry out text data key message before carrying out value parameter and determining in advance and extract behaviour
Make, generates the data of data entry form.Such as: having the data for determining value parameter demand is buyer's guide text, can be with
Before the computation according to keyword extractions such as product name, commodity production quotient, the place of production, production times at the form of data entry,
Using the data entry of extraction as target data.
The data volume that target data is included, the data volume for the valid data member that can contain for target packet can also be with
For comprising data element quantity, or the quantity of data entry is shown by taking the data volume of valid data member as an example above-mentioned
In example, the quantity for the data element that a complete target data includes is 9, then the corresponding data volume of every data entry is 9, if
Target data includes 100 data entries, then its data volume that should have is 900, that is to say, that the data of target data
Amount is 900；In practical applications, it is understood that there may be certain data elements are sky, do not have actual content for empty data element, cause mesh
The actual amount of data of data is marked less than data volume described in description information；By taking the quantity of data entry as an example, it can compare here
Data entry quantity described in the description information of the data entry quantity and the target data that include compared with target data.
It therefore, can be by determining data described in the description information of the target data data volume and target data that include
The degree of consistency of amount characterizes the data content of target data and the degree of consistency of description information.
Second: the size of target data described in the description information of the size of target data and the target data；
Herein, the size of target data can actually regard the file size of the file of carrying target data as.Example
Such as, the data element missing (i.e. data element is sky) of data entry will also result in the authentic document of the file data of carrying target data
It is not of uniform size described in size and description information.
Therefore, the consistency of the size of the description information description of the size and target data of determining target data can be passed through
Degree characterizes the data content of target data and the degree of consistency of description information.
Third: data format described in the data format of target data and the description information of target data；
Herein, the data format of target data, and carry the file format of the file of target data.Carry target data
File format may be different from file format described in description information.
It therefore can be by determining description described in the data format of target data and the description information of the target data
The degree of consistency of data format characterizes the data content of target data and the degree of consistency of description information.
It being understood, however, that the data content that target data is included can be but not limited to data volume, size and data
Format etc.；The corresponding description information of target data is generally used for the information of description target data, the corresponding description of target data
Information also includes the contents such as data volume, size and data format.
In specific implementation, data described in the description information of data volume and target data that target data includes are calculated
The first absolute difference (namely absolute value) of amount；It calculates described in the size of target data and the description information of target data
The second absolute difference (namely absolute value) of the size of target data；If the data format of target data and retouching for target data
It is consistent to state data format described in information, it is determined that the consistent degree D of target data is the first preset value, and otherwise, D is second pre-
If value；According to the first absolute difference, the second absolute difference and consistent degree, finger of the target data under data consistency index is calculated
Scale value.Wherein, the first preset value is generally 0, and the second preset value is generally 1, and the first preset value and the second preset value may be
Other values, can determines according to actual conditions, generally, and the second preset value is greater than the first preset value.
It is calculated using the following equation the first absolute difference L1:
L1=| La-Lm|
Wherein, L1 is the first absolute difference of target data, LaThe data volume for including by target data, LmFor number of targets
According to the description information data volume that is included.
It is calculated using the following equation the second absolute difference L2:
L2=| Sa-Sm|
Wherein, L2 is the second absolute difference of target data, SaFor the size of target data, SmFor the description of target data
The size of information.
It is calculated using the following equation the index value f of data consistency index1:
Wherein, f1For index value of the target data under data consistency index, L1 is the first absolute difference of target data
Value, L2 are the second absolute difference of target data, and α is the positive real number no more than 1, and preferably 1/3, D is the one of target data
Cause degree.
f1Value range is generally [0,1], f1Value is bigger, illustrates that the degree of consistency of target data is higher, then, the mesh
The data application value for marking data is also higher.
Quality index includes data integrity index, and the data integrity index is for characterizing valid data in target data
Shared specific gravity, wherein specific gravity shared by valid data is bigger, and the data integrity of the target data is better.
It in specific implementation, will be the assignment of every data entry in target data as the effective of the data entry
Degree successively detects whether the data element in target data in each data entry is empty；Data element is if it is empty, then corresponding complete
Property degree be determined as 0, if data element be not it is empty, corresponding integrity degree is determined as 1；By the sum of the integrity degree of all data elements and number
According to the ratio of first quantity, as specific gravity shared by valid data, for example, having 10 rows, 10 column datas in target data, target is traversed
Each data element in data, if the data element of the i-th row jth column is sky, at this point, the availability of the i-th row jth column is 0, if i-th
The data element of row jth column is not empty, at this point, the availability of the i-th row jth column is 1.The number of target data is calculated using following formula
According to integrity metrics f2Index value:
Wherein, f2For index value of the target data under data integrity index, aijFor the i-th row jth in target data
The availability of the data element of column, S are the line number in the data entry in target data, and T is the data entry in target data
Columns, N are the sum of data entry in target data, wherein N=S × T.
f2Value range be [0,1], f2Value is bigger, indicates that the data integrity of target data is better.
Further, accounting of the invalid data entry in data entry sum in target data can also be determined.1 is subtracted
Go accounting of the resulting result of invalid data accounting as valid data in target data, wherein will determine for empty data element
For invalid data.
Quality of data index includes data scarcity index, and processor 11 determines target data and similar to target data
Set of metadata of similar data default platform frequency of occurrence；And the finger of the data scarcity index of target data is determined based on frequency of occurrence
Scale value, and frequency of occurrence is fewer, the scarcity for characterizing target data is higher.
Here, the data that similar (or similar) is belonged to target data are more, and scarcity is lower；Belong to together with target data
Class (or similar) data are fewer, and scarcity is higher；The value of the higher target data of scarcity is also corresponding higher.
In specific implementation, similarity of the target data respectively with the data of each default platform is calculated separately, target is calculated
The similarity with the data of each default platform, statistics are greater than the similarity pair of setting similarity threshold to the set of metadata of similar data of data respectively
The ratio of the total number of the number for the default platform answered, the number of counting statistics and default platform calculates the ratio of natural number
The inverse of side calculates the index value of data scarcity index according to the inverse of calculating.
For example, crawling a large amount of data from each default platform after obtaining target data and set of metadata of similar data, preset from each
The data that platform crawls can be transaction data, and final each corresponding one of default platform includes the data set of mass data, needle
To each default platform, calculated by calculating formula of similarity similar between target data and the data set of the default platform
Degree can also calculate the set of metadata of similar data quantity between the set of metadata of similar data of target data and the data set of the default platform, from final
In obtained a large amount of similarities, statistics is greater than the similarity of setting similarity threshold, and the number of the similar data set of counting statistics
The ratio of mesh and default platform total number, the finger of the data scarcity index of the above-mentioned ratio calculation data to be assessed based on calculating
Scale value.
It is calculated using the following equation the data scarcity index f of data to be assessed3Index value:
f3=1-e-x/y
Wherein, f3For index value of the target data under data scarcity index, x is the phase of target data and target data
For likelihood data in the frequency of occurrence of default platform, y is the sum of default platform.
Furthermore it is also possible to be calculated using the following equation index value f of the data to be assessed under data scarcity index3:
Wherein, x is the set of metadata of similar data of target data and target data in the frequency of occurrence of default platform, and y is to crawl
The total quantity of data set.
f3Value range be [0,1], work as f3Close to 1, illustrate that each default platform has set of metadata of similar data, number to be assessed
According to scarcity it is lower, f3Equal to 0, show each default platform there is no set of metadata of similar data, the scarcity of data to be assessed is higher.
Quality index includes data age index, and processor 11 determines the initial time for generating target data and generates mesh
The data of the period of end time composition of mark data and the generation time of target data and target data provide the time
Time difference；Index value of the target data under data age index is determined based on the period and the time difference: where the time
In the case that the corresponding duration of section is longer, the time difference is smaller, index value of the target data under data age index is higher.
It wherein, can be by rising in the description information of target data if above-mentioned initial time and end time can not be determined
Time beginning and end time are respectively as the initial time for generating target data and the end time of generation target data；Number of targets
According to generation time can be any time in the above-mentioned period, preferably, the generation time of target data is to generate target
The initial time of data.
In specific implementation, the time for generating the initial time of target data and generating the end time of target data is determined
Difference, as poor at the first time；Determine that the data of target data provide the time difference between time and the generation time of target data,
As the second time difference, using poor at the first time and the second time difference ratio as target data under data age index
Index value.
Wherein, the unit of time can according to the actual situation depending on, such as: if the length of time interval be 1 day, by the time
The unit in section is set as minute；If the length of time interval is 2 months, day is set by the unit of time interval；If should
The length of time interval is 3 years, then this can be for week by the unit of time interval.It should be noted that above-mentioned setting time
The unit in section is only example provided by the embodiment of the present application, cannot be considered as being the limit to technical scheme
It is fixed.
The data of target data provide the time for the time referring to acquisition target data.It is noted herein that due to mesh
Marking data has certain data volume, is actually not easy to obtain whole number of targets from scratch at some time point
According to therefore, the data of target data are provided at the beginning of the time can be and start to obtain target data, are also possible to complete to obtain
Take the end time of target data；In addition, due to after obtaining target data, not necessarily at the first time to target data into
Row processing, therefore, at the beginning of obtaining target data or end time distance calculates target data under timeliness index
Index value time it is closer when, the time that can also will calculate index value of the target data under data age index is made
The time is provided for data.
For example, including 100 data entries in target data；In 100 data entries, the production of earliest data entry
Raw time (namely initial time of target data) is on January 1st, 2018；Generation time (namely the mesh of data entry the latest
Mark the termination time of data) it is on January 30th, 2018；The time interval that then target data generation time is crossed over is (namely most
Big time span) it is 30 days.If it is on April 1st, 2018 that target data, which provides the time, target data provides time and number of targets
According to the time difference between generation time (can be the initial time of target data), January 1 in as 1 day to 2018 April in 2018
Time difference between day.
It is calculated using the following equation data age index f4Index value:
Wherein, f4For index value of the target data under data age index；TfAt the end of generating target data
Between；TsAt the beginning of generating target data；TnTime, optionally, T are provided for the data of target datanTo start to obtain mesh
At the beginning of marking data, or complete to obtain the end time of target data, can also be to start to calculate target data
The time of index value under timeliness index；TpFor the generation time of target data, e.g., TpFor the beginning for generating target data
Time, or, the end time of target data is generated, alternatively, to generation target data at the beginning of to generate target data
Any time in end time, it is preferable that TpAt the beginning of generating target data.
f4Value range is [0,1], f4Value it is bigger, indicate that the timeliness of target data is stronger.
Data service system provided by the embodiments of the present application obtains corresponding according to the data service request that user side is sent
Target data, determine that the characteristic parameter of target data, characteristic parameter include index value of the target data under each quality index
The value parameter of target data is determined according to the characteristic parameter of target data with the life parameters of target data.In this way, comprehensive
The life parameters of the consideration quality of data and data determine the value parameter of target data, so that the valence of the target data determined
Be worth it is relatively reliable, using determining value parameter may be target data application provide decision recommendation.
The embodiment of the present application provides a kind of data service method, as shown in Fig. 2, this method comprises:
S201, the data service request that processor response is sent in user side send data acquisition request, institute to database
State data acquisition request for obtain corresponding with data service request target data；
S202, the database root send the target data according to the data acquisition request, Xiang Suoshu processor；
S203, the processor determine that the characteristic parameter of the target data, the characteristic parameter include the number of targets
According to the life parameters of index value and the target data under each quality index；
S204, the processor determine the value parameter of the target data according to the characteristic parameter of the target data；
The value parameter of the target data is sent to output device by S205, the processor；
S206, the output device export the value parameter of the target data to the user side.
Optionally, according to the characteristic parameter of the target data, the value parameter of the target data is determined, it is specific to wrap
It includes:
By the product of the life parameters and index value of the target data under each quality index, as the target
The value parameter of data.
Optionally, the characteristic parameter further includes associated data and the institute of each application field of the target data association
State the degree of correlation of target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
Optionally, according to the life parameters, the index value and the degree of correlation, the valence of the target data is determined
Value parameter specifically includes:
Determine the degree of correlation of the associated data of the target data and each application field and value；
By described and value, the product of index value and the life parameters of the target data under each quality index, make
For the value parameter of the target data.
Optionally, according to the degree of correlation as described in determining under type:
For each application field with the target data association, determine that each associated data of the application field is corresponding
Weight；
According to each associated data of the application field and the corresponding weight of each associated data, determine that the application field is corresponding
Predicted value；
According to the corresponding predicted value of the application field and the target data, determine that the target data and the application are led
The associated data multiple correlation coefficient in domain；
Using the multiple correlation coefficient as the degree of correlation of the target data and the associated data of the application field.
Optionally it is determined that the corresponding weight of each associated data of the application field, specifically includes:
It is returned using each associated data of the target data to the application field, obtains each association of the application field
The corresponding weight of data.
Optionally, the quality index is included at least with the next item down:
Data consistency index, data integrity index, data scarcity index and data age index.
In embodiment provided herein, it should be understood that disclosed system and method, it can be by others side
Formula is realized.System embodiment described above is only schematical, for example, the division of the unit, only one kind are patrolled
Function division is collected, there may be another division manner in actual implementation, in another example, multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some communication interfaces, system or unit
It connects, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
In addition, each functional unit in embodiment provided by the present application can integrate in one processing unit, it can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units.
It, can be with if the function is realized in the form of SFU software functional unit and when sold or used as an independent product
It is stored in a computer readable storage medium.Based on this understanding, the technical solution of the application is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be embodied in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be a
People's computer, server or network equipment etc.) execute each embodiment the method for the application all or part of the steps.
And storage medium above-mentioned includes: that USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic or disk.
It should also be noted that similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing, in addition, term " the
One ", " second ", " third " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
Finally, it should be noted that embodiment described above, the only specific embodiment of the application, to illustrate the application
Technical solution, rather than its limitations, the protection scope of the application is not limited thereto, although with reference to the foregoing embodiments to this Shen
It please be described in detail, those skilled in the art should understand that: anyone skilled in the art
Within the technical scope of the present application, it can still modify to technical solution documented by previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of the embodiment of the present application technical solution.The protection in the application should all be covered
Within the scope of.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
Claims (10)
1. a kind of data service system characterized by comprising processor, database, output device, in which:
The data service request that the processor response is sent in user side, Xiang Suoshu database send data acquisition request, institute
State data acquisition request for obtain corresponding with data service request target data；
The database root sends the target data according to the data acquisition request, Xiang Suoshu processor；
The processor determines that the characteristic parameter of the target data, the characteristic parameter include the target data in each quality
The life parameters of index value and the target data under index；According to the characteristic parameter of the target data, determine described in
The value parameter of target data；The value parameter of the target data is sent to the output device；
The output device exports the value parameter of the target data to the user side.
2. the system as claimed in claim 1, which is characterized in that according to the characteristic parameter of the target data, determine the mesh
The value parameter for marking data, specifically includes:
By the product of the life parameters and index value of the target data under each quality index, as the target data
Value parameter.
3. the system as claimed in claim 1, which is characterized in that the characteristic parameter further includes each of the target data association
The degree of correlation of the associated data of a application field and the target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
4. system as claimed in claim 3, which is characterized in that according to the life parameters, the index value and the phase
Guan Du determines the value parameter of the target data, specifically includes:
Determine the degree of correlation of the associated data of the target data and each application field and value；
By described and value, the product of index value and the life parameters of the target data under each quality index, as institute
State the value parameter of target data.
5. system as described in claim 3 or 4, which is characterized in that according to the degree of correlation as described in determining under type:
For each application field with the target data association, the corresponding power of each associated data of the application field is determined
Weight；
According to each associated data of the application field and the corresponding weight of each associated data, determine that the application field is corresponding pre-
Measured value；
According to the corresponding predicted value of the application field and the target data, the target data and the application field are determined
Associated data multiple correlation coefficient；
Using the multiple correlation coefficient as the degree of correlation of the target data and the associated data of the application field.
6. system as claimed in claim 5, which is characterized in that determine the corresponding weight of each associated data of the application field,
It specifically includes:
It is returned using each associated data of the target data to the application field, obtains each associated data of the application field
Corresponding weight.
7. the system as described in claim 1-4 is any, which is characterized in that the quality index is included at least with the next item down:
Data consistency index, data integrity index, data scarcity index and data age index.
8. a kind of data service method, which is characterized in that this method comprises:
The data service request that processor response is sent in user side sends data acquisition request to database, and the data obtain
Take request for obtaining target data corresponding with the data service request；
The database root sends the target data according to the data acquisition request, Xiang Suoshu processor；
The processor determines that the characteristic parameter of the target data, the characteristic parameter include the target data in each quality
The life parameters of index value and the target data under index；According to the characteristic parameter of the target data, determine described in
The value parameter of target data；The value parameter of the target data is sent to output device；
The output device exports the value parameter of the target data to the user side.
9. method according to claim 8, which is characterized in that according to the characteristic parameter of the target data, determine the mesh
The value parameter for marking data, specifically includes:
By the product of the life parameters and index value of the target data under each quality index, as the target data
Value parameter.
10. method according to claim 8, which is characterized in that the characteristic parameter further includes the target data association
The degree of correlation of the associated data of each application field and the target data；
Then, according to the characteristic parameter of the target data, the value parameter of the target data is determined, comprising:
According to the life parameters, the index value and the degree of correlation, the value parameter of the target data is determined.
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| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 101-8, 1st floor, building 31, area 1, 188 South Fourth Ring Road West, Fengtai District, Beijing Applicant after: Guoxin Youyi Data Co.,Ltd. Address before: 100070, No. 188, building 31, headquarters square, South Fourth Ring Road West, Fengtai District, Beijing Applicant before: SIC YOUE DATA Co.,Ltd. |
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| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190423 |