CN109345301A - A kind of data price-determining system and determining method - Google Patents
A kind of data price-determining system and determining method Download PDFInfo
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Abstract
This application provides a kind of data price-determining system and determine method, wherein the system includes: set of metadata of similar data determining module, for determining similar data set similar with target data set；Price obtains module, for obtaining the price in a period and the price of the similar data set on the target data set；Price determination module determined model using data price trained in advance, and determined the target data set in the price of current period for price, the price of the similar data set according to upper period of the target data set.The embodiment of the present application can stress the valuation that target data set is comprehensively considered from target data set itself and Data Market angle, so that the price for the target data set that estimation obtains has higher accuracy.
Description
Technical field
This application involves data to determine technical field, in particular to a kind of data price-determining system and determination
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, the specific gravity that intangible asset occupies is increasing, in addition to patent, software copyright, trade mark etc.
The importance of the intangible assets such as intellectual property, this intangible asset of business datum should not be underestimated.The value of data is directly determined sometimes
Determine the value of enterprise.
When the value to data is determined, it is normally based on the correlation properties of data, such as integrality, rare
Property, consistency, redundancy, timeliness etc. carry out.But continuity and generation speed that data are generated due to it, it can week
Phase irregular is updated.The data price in different update period is not also identical.Therefore, based on the correlated characteristic of data
The value of data is determined, obtained definitive result error is larger.
Summary of the invention
In view of this, the embodiment of the present application is designed to provide a kind of data price-determining system and determining method,
It can be obtained based on target data set in the price of history update cycle and the price of set of metadata of similar data similar with target data set
Target data is obtained in the more accurate price of current period.
In a first aspect, the embodiment of the present application provides a kind of data price-determining system, which includes:
Set of metadata of similar data determining module, for determining similar data set similar with target data set；
Price obtains module, for obtaining the price in a period and the similar data set on the target data set
Price；
Price determination module, for according to the price in upper period of the target data set, the similar data set
Price determines model using data price trained in advance, determines the target data set in the price of current period.
In a kind of optional embodiment, the set of metadata of similar data determining module, for passing through following manner determination and target
The similar similar data set of data set:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
In a kind of optional embodiment, the set of metadata of similar data determining module, for by following manner from multiple described
The similar data set of the target data set is determined in data set
There to be shared number of attributes at most in the multiple data set with the target data or text similarity highest
Data set be determined as the similar data set of the target data.
In a kind of optional embodiment, the price in a upper period for the target data set is upper period number of targets
According to the realized price of collection；And/or
The initial prices of the target data set are determined according to its cost and profit margin.
In a kind of optional embodiment, further includes: training module, for according to the target data set in current period
Realized price, model, which is trained adjustment, to be determined to the data price.
Second aspect, the embodiment of the present application also provide a kind of data price and determine method, this method comprises:
Determine similar data set similar with target data set；
Obtain the price in a period and the price of the similar data set on the target data set；
According to the price in a upper period for the target data set, the price of the similar data set, preparatory training was used
Data price determine model, determine the target data set in the price of current period.
In a kind of optional embodiment, determination similar data set similar with target data set, comprising:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
In a kind of optional embodiment, the similarity number that the target data set is determined from multiple data sets
According to collection, comprising:
There to be shared number of attributes at most in the multiple data set with the target data or text similarity highest
Data set be determined as the similar data set of the target data.
In a kind of optional embodiment, the price in a upper period for the target data set is upper period number of targets
According to the realized price of collection；And/or
The initial prices of the target data set are determined according to its cost and profit margin.
In a kind of optional embodiment, further includes: according to the target data set current period practical knock-down price
Lattice determine that model is trained adjustment to the data price.
The embodiment of the present application is by the price and target data set of similar data set similar with target data set upper
The price in one period, and data price trained in advance determine model, determine target data set in the price of current period, energy
Enough stress the valuation that target data set is comprehensively considered from target data set itself and Data Market angle, so that estimation obtains
Target data set price have higher accuracy.
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 shows a kind of schematic diagram of data price-determining system provided by the embodiment of the present application；
Fig. 2 shows the flow charts that data price provided by the embodiment of the present application determines method；
Fig. 3 shows a kind of schematic diagram of computer equipment provided by the embodiment 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.
Current data are generally based on characteristic possessed by data itself when carrying out value assessment to carry out,
For the data with certain renewal frequency, resultant error determined by this appraisal procedure is larger for this, is based on this, this Shen
A kind of data that please be provide determine system and determine method, can data and set of metadata of similar data based on the previous period valence
Lattice assess the price of current period, have higher accuracy.
It is to be determined to a kind of data price disclosed in the embodiment of the present application first convenient for understanding the present embodiment
System describes in detail, data price-determining system provided by the embodiments of the present application be used for for certain update cycle data into
Row assessment, such as use data etc. that survey data caused by transaction data, periodical progress market survey, software generate.
Shown in Figure 1, data price-determining system provided by the embodiments of the present application includes: set of metadata of similar data determining module
10, price obtains module 20 and price determination module 30.
I: set of metadata of similar data determining module 10, for determining similar data set similar with target data set.
Herein, the similar data set which data set is target data set can be measured by multiple standards, for example, producing
Raw platform it is identical or generate data main body it is identical, based on identical behavior generate data set, or based on data whether
Whether there is the same or similar data content etc. with the same or similar data attribute, or based on data, herein not
Limit the determination method of set of metadata of similar data.
Specifically, multiple data sets can be obtained by presetting platform from least one, and from multiple data sets really
Set the goal the similar data set of data set.
For example, target data set is the article sales data collection that some shopping platform generates；Then presetting platform can be it
His shopping platform, and the article sales data with other each shopping platforms is obtained from other shopping platforms, other are each
Article sales data acquired in shopping platform forms at least one data set corresponding with other shopping platforms, other are all
Similar data set similar with target data set is determined in the corresponding data set of shopping platform.
In another example will directly list in data trade platform using data trade platform as default platform
Data set or similar data set of the data set as target data set of having traded to obtain.
After obtaining data set, using any one in following similar data set methods of determination or it can combine from number
Similar data set similar with target data set is determined according to concentrating:
Similar data set method of determination 1: will there is shared number of attributes with the target data in the multiple data set
Most data sets is determined as the similar data set of the target data.Herein, attribute can be characterized by attribute tags, be belonged to
Property label can according to need and set, such as generate the behavior of data, the platform that generates data, the main body for generating data,
Field belonging to data etc..
It specifically can be by following manner according to will have shared attribute with the target data in the multiple data set
The most data set of quantity is determined as the similar data set of the target data:
Determine the attribute tags of the target data set and the multiple data set；
According to the attribute tags of the target data set and multiple data sets, determine the target data set and
All properties label possessed by multiple data sets；
For each data set, according to the quantity for the attribute tags that the data set and the target data set all have, with
And the quantity of all properties label possessed by the target data set and multiple data sets, determine the data set and institute
State the similarity of target data；
The highest data set of similarity is determined as to the set of metadata of similar data of the target data set.
When specific implementation, attribute tags information, which can be target data set and multiple data sets itself, have been had
, it is also possible in the attribute according to target data set and multiple data sets, is immediately its addition.It is with target data set
Example, attribute tags information are used to identify the attribute of target data set, the higher data of similarity, and attribute should also be as being more similar
, therefore the similarity between the attribute tags information of target data set and the attribute tags information of each data set can be passed through
Characterize the similarity between target data set and each data set.
Similar data set method of determination 2: will be highest with the target data text similarity in the multiple data set
Data set is determined as the similar data set of the target data.
Specifically, the target data set and the multiple data set can be parsed respectively, determines the mesh
Mark the lexical feature of data set and each data set；The lexical feature of the target data set is special with the vocabulary of each data set respectively
Sign carries out text similarity matching；The data set that text similarity reaches default similarity threshold is determined as the target data
The set of metadata of similar data of collection.
When specific implementation, word segmentation processing can be obtained by carrying out acne processing to multiple data sets of acquisition
The first lexical data afterwards；The frequency of occurrence concentrated according to each first lexical data after word segmentation processing in corresponding data is by height
To low sequence, the first lexical data of preset quantity, each data for data sets are each according to what is filtered out before filtering out
The frequency that first lexical data occurs in the data set determines the lexical feature of the data.
Word segmentation processing is carried out to target data set, the second lexical data after obtaining word segmentation processing；After word segmentation processing
The frequency of occurrence concentrated in target data of each second lexical data sequence from high to low, preset quantity the before filtering out
Two lexical datas, for each data that target data is concentrated, according to each second lexical data filtered out in the target data
The frequency occurred is concentrated, determines the lexical feature of the data.
For each lexical feature in each data set, calculate the lexical feature in the data set respectively with it is to be determined
The text similarity between lexical feature in data.Text similarity is greater than or equal to the data set of default similarity threshold
It is determined as the set of metadata of similar data of data to be determined.
II: price obtains module 20, for obtaining the price in a period and the similarity number on the target data set
According to the price of collection.
When specific implementation, for target data set in this period and previous period, specific data content can phase
Together, it can also be different.For example, for the target data set that can be periodically updated, such as the purchase data of user, passenger go out
Row data will change with the variation of time, therefore the update of meeting generating period, and this data are as target data
When collection, the data content in this period and previous period is generally different；For will not update in the period, but can repeatability make
For target data set, such as the basis instrument data of database operation, these data can repeat to utilize,
Its update is very slow, can be regarded as the data that will not periodically update, when this data are as target data set,
Data content in this period and previous period is usually identical.
The price in a period on target data set, for the realized price of the target data set of the upper period.
It is the case where being traded for the first time for target data set, it can be according to the cost and profit margin of target data set
To determine its initial prices.
For example, can determine target data set initial prices using following formula:
P0=C × (1+r)；
P0Indicate the initial prices of target data set；The cost of C expression target data set；The benefit of r expression target data set
Profit rate.
Wherein, cost can be at least one of the compiling costs of target data set, processing cost, carrying cost.
It not trades for target data set, but the case where a upper period does not strike a bargain actually, can use for the first time
Data price-determining system provided by the embodiments of the present application, and according to the previous realized price of target data set to upper one week
The price of phase is estimated.
III: price determination module 30, for price, the set of metadata of similar data according to upper period of the target data set
The price of collection determines model using data price trained in advance, determines the target data set in the price of current period.
When specific implementation, further includes: training module 40, for obtaining data trained in advance using following manner
Price determines model:
Building carries out the basis that data price determines and determines model；The basis determines model by target data set upper one
The price in period, and with the price of the similar data set as explanatory variable, using the price of the target data set as
Explained variable.
Obtain training dataset；Training data concentration includes: multiple groups training dataset, training dataset described in every group
Similar data set, every group of training dataset two adjacent periods activity price and the similar data set
Transaction value in two adjacent periods in the latter period；
Using transaction value of the every group of training dataset in two adjacent periods in the latter period as being solved
The value for releasing variable, using transaction value of the every group of training dataset in two adjacent periods in the previous period as
The value of one explanatory variable, by the transaction with training dataset described in every group in two adjacent periods in the latter period
Value of the price as the second explanatory variable, substitutes into the basis and determines model, determines that model is trained to the basis, and will
The basis that training obtains determines model, determines model as the data price.
Herein, model can be determined using the following manner building basis:
Using the price of the similar data set as the first explanatory variable, the price by target data set in a upper period is made
For the second explanatory variable, the first parameter is added for first explanatory variable, and is the second ginseng of second explanatory variable addition
Number；
And determine that model adds third parameter for the basis；
Using the price of the target data set as explained variable, constructs the basis and determine model.
For example, identified basis determines that model can indicate are as follows: Pt=alpha+beta × St+γ×Pt-1+ut；
Wherein, PtIndicate target data set in the price in this period；Pt-1Indicated target data set in the valence in a upper period
Lattice；β indicates the first parameter；γ indicates the second parameter；α+utIndicate third parameter, and α indicates the original value of target data set,
Even if characterizing the target data set not trade, target data set should also be as being that tool is valuable.
After being determined that data price determines model, it will be able to by the price in a upper period for target data set, similar
The price of data set substitutes into the data price and determines model, obtains target data set in the price of current period.
In addition, training module 40 provided by the embodiments of the present application, is also used to: according to the target data set in current period
Realized price, model, which is trained adjustment, to be determined to the data price.
Specifically, due to the variation of continuous renewal and exterior market environment with data, data price determines model
Accuracy can be declined, so that target data set determines model in the realized price of current period and according to data price
There are biggish errors between the price estimated.It therefore, can be in mesh in order to guarantee that data price determines the accuracy of model
Mark data set current period it is practical conclusion of the business after, according to target data set current period realized price, to data valence
Lattice determine that model is trained adjustment.
When being trained adjustment to model, all parameters of model can be adjusted simultaneously, can also only be adjusted
Partial parameters therein, so that having adjusted the data price after parameter determines that model is target data set firm price and target
The error between the realized price of current period of data set is in preset error range.
The embodiment of the present application is by the price and target data set of similar data set similar with target data set upper
The price in one period, and data price trained in advance determine model, determine target data set in the price of current period, energy
Enough stress the valuation that target data set is comprehensively considered from target data set itself and Data Market angle, so that estimation obtains
Target data set price have higher accuracy.
Based on the same inventive concept, data valence corresponding with data price-determining system is additionally provided in the embodiment of the present application
Lattice determine method, and the principle and the above-mentioned data price of the embodiment of the present application solved the problems, such as due to the method in the embodiment of the present application is true
Determine that system is similar, because the implementation of the method may refer to the implementation of system, overlaps will not be repeated.
Shown in Figure 2, data price provided by the embodiments of the present application determines that method includes:
S201: similar data set similar with target data set is determined；
S202: the price in a period and the price of the similar data set on the target data set are obtained；
S203: according to the price in a upper period for the target data set, the price of the similar data set, using preparatory
Trained data price determines model, determines the target data set in the price of current period.
The embodiment of the present application is by the price and target data set of similar data set similar with target data set upper
The price in one period, and data price trained in advance determine model, determine target data set in the price of current period, energy
Enough stress the valuation that target data set is comprehensively considered from target data set itself and Data Market angle, so that estimation obtains
Target data set price have higher accuracy.
In a kind of optional embodiment, determination similar data set similar with target data set, comprising:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
In a kind of optional embodiment, the similarity number that the target data set is determined from multiple data sets
According to collection, comprising:
There to be shared number of attributes at most in the multiple data set with the target data or text similarity highest
Data set be determined as the similar data set of the target data.
In a kind of optional embodiment, the price in a upper period for the target data set is upper period number of targets
According to the realized price of collection；And/or
The initial prices of the target data set are determined according to its cost and profit margin.
In a kind of optional embodiment, further includes: according to the target data set current period practical knock-down price
Lattice determine that model is trained adjustment to the data price.
Method is determined corresponding to the data price in Fig. 2, and the embodiment of the present application also provides a kind of computer equipments, such as scheme
It is 30 schematic diagram of computer equipment provided by the embodiments of the present application, including processor 31, memory 32 and bus 33 shown in 3；
Memory 32 is executed instruction for storing, including memory 321 and external memory 322；Here the also referred to as interior storage of memory 321
Device is handled for temporarily storing the operational data in processor 31, and the data exchanged with external memories 322 such as hard disks
Device 31 carries out data exchange by memory 321 and external memory 322, when the user equipment 60 operation, the processor
It is communicated between 31 and the memory 32 by bus 33, so that the processor 31 is executed in User space to give an order:
Determine similar data set similar with target data set；
Obtain the price in a period and the price of the similar data set on the target data set；
According to the price in a upper period for the target data set, the price of the similar data set, preparatory training was used
Data price determine model, determine the target data set in the price of current period.
Optionally, in the instruction that processor 31 executes, determination similar data set similar with target data set, packet
It includes:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
Optionally, described that the target data set is determined from multiple data sets in the instruction that processor 31 executes
Similar data set, comprising:
There to be shared number of attributes at most in the multiple data set with the target data or text similarity highest
Data set be determined as the similar data set of the target data.
Optionally, in the instruction that processor 31 executes, the price in a upper period for the target data set was a upper period
The realized price of the target data set；And/or
The initial prices of the target data set are determined according to its cost and profit margin.
Optionally, processor 31 execute instruction in, further includes: according to the target data set current period reality
Concluded price determines that model is trained adjustment to the data price.
Method is determined corresponding to the data price in Fig. 2, and the embodiment of the present application also provides a kind of computer-readable storages
Medium is stored with computer program on the computer readable storage medium, executes when which is run by processor
State the step of data price determines method.
Specifically, which can be general storage medium, such as mobile disk, hard disk, on the storage medium
Computer program when being run, be able to carry out above-mentioned data price and determine method, to solve to be had based on data itself
Characteristic carry out the larger problem of error caused by data value assessment, and then reach and can stress from target data set itself
The valuation of target data set is comprehensively considered with Data Market angle, so that the price for the target data set that estimation obtains has
The effect of higher accuracy.
Data price-determining system provided by the embodiment of the present application and the computer program product for determining method, including
The computer readable storage medium of program code is stored, the instruction that said program code includes can be used for executing previous methods reality
Method described in example is applied, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
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.
The above, the only specific embodiment of the application, but the protection scope of the application is not limited thereto, it is any
Those familiar with the art within the technical scope of the present application, can easily think of the change or the replacement, and should all contain
Lid is within the scope of protection of this application.Therefore, the protection scope of the application shall be subject to the protection scope of the claim.
Claims (10)
1. a kind of data price-determining system, which is characterized in that the system includes:
Set of metadata of similar data determining module, for determining similar data set similar with target data set；
Price obtains module, for obtaining the valence of the price in a period and the similar data set on the target data set
Lattice；
Price determination module, for price, the price of the similar data set according to upper period of the target data set,
Model is determined using data price trained in advance, determines the target data set in the price of current period.
2. system according to claim 1, which is characterized in that the set of metadata of similar data determining module, for passing through following sides
Formula determines similar data set similar with target data set:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
3. system according to claim 2, which is characterized in that the set of metadata of similar data determining module, for passing through following sides
Formula determines the similar data set of the target data set from multiple data sets:
There to be shared number of attributes most in the multiple data set with the target data or the highest number of text similarity
It is determined as the similar data set of the target data according to collection.
4. system according to claim 1, which is characterized in that the price in a upper period for the target data set is upper one
The realized price of the period target data set；And/or the initial prices of the target data set are according to its cost and profit
Rate determines.
5. system according to claim 1, which is characterized in that further include: training module, for according to the target data
Collect the realized price in current period, model, which is trained adjustment, to be determined to the data price.
6. a kind of data price determines method, which is characterized in that this method comprises:
Determine similar data set similar with target data set；
Obtain the price in a period and the price of the similar data set on the target data set；
According to the price in a upper period for the target data set, the price of the similar data set, number trained in advance was used
Model is determined according to price, determines the target data set in the price of current period.
7. according to the method described in claim 6, it is characterized in that, determination set of metadata of similar data similar with target data set
Collection, comprising:
Multiple data sets are obtained from default platform；And
The similar data set of the target data set is determined from multiple data sets.
8. the method according to the description of claim 7 is characterized in that described determine the number of targets from multiple data sets
According to the similar data set of collection, comprising:
There to be shared number of attributes most in the multiple data set with the target data or the highest number of text similarity
It is determined as the similar data set of the target data according to collection.
9. according to the method described in claim 6, it is characterized in that, the price in a upper period for the target data set is upper one
The realized price of the period target data set；And/or
The initial prices of the target data set are determined according to its cost and profit margin.
10. according to the method described in claim 6, it is characterized by further comprising: according to the target data set in current period
Realized price, model, which is trained adjustment, to be determined to the data price.
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| US11094015B2 (en) | 2014-07-11 | 2021-08-17 | BMLL Technologies, Ltd. | Data access and processing system |
| CN113988923A (en) * | 2021-10-27 | 2022-01-28 | 北京百度网讯科技有限公司 | Method, device, equipment and storage medium for determining information |
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