CN106951913A - The method for carrying out data exchange, cloud platform and system - Google Patents
The method for carrying out data exchange, cloud platform and system Download PDFInfo
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Abstract
A kind of method for carrying out data exchange of the present invention, cloud platform and system.This method includes：Multiple data from multiple data sources are classified, and are stored as multiple categorical datas；Request data content is obtained from demand data side；The data in the categorical data matched with data category are taken out from multiple categorical datas as multiple matched datas, multiple respective matching degrees of matched data are calculated；According to the respective matching degree of multiple matched datas, multiple matched datas are ranked up；According to demand data amount and predetermined data redundancy rate, a part of matched data is taken out from multiple matched datas after sequence；De-redundancy processing is carried out to a part of matched data, so as to obtain the multiple request datas corresponding with request data content；Multiple request datas are sent to demand data side.
Description
Technical field
The present invention relates to method, cloud platform and the system for carrying out data exchange.
Background technology
Internet era, the focus that the commercial value using big data as core will fall over each other to develop as all trades and professions.
Big data transaction is increasingly becoming the model that a kind of big data produces commercial value as a kind of mode of data exchange.Data
Both parties are exchanged by data and cash, and each takes what he needs.
Common big data transaction is confined to man-to-man data trade, i.e. the data between single company and company are handed over
Easily, such transaction is easily caused the problems such as data statistics is unilateral, data type is single, data are on the low side.When some company, needs are more
During the data of individual data source, it will be solved：1. how to search out multiple companies for possessing class likelihood data and being ready transaction data；
2. how data fusion is carried out to the data from each side；3. redundant data (from multi-party identical data) is there is still a need for branch
The problems such as paying is used.
Data fusion technique has been widely studied, but existing Data fusion technique can not solve data redundancy and cause
Data trade billing issues, it is impossible to the problems such as data from different data sources are merged.
The content of the invention
For above mentioned problem of the prior art, the invention provides a kind of method for carrying out data exchange, cloud platform and
System.
A kind of method for carrying out data exchange, methods described includes：
Multiple data from multiple data sources are classified, and are stored as multiple categorical datas, the multiple data
In each data there is data source identifier；
The request data content from demand data side is obtained, the request data content at least includes data category, closed
Key word and data demand；
The data in the categorical data matched with the data category are taken out from the multiple categorical data as multiple
Matched data, and according to the keyword, calculate the multiple respective matching degree of matched data；
According to the respective matching degree of the multiple matched data, according to order from big to small to the multiple matched data
It is ranked up, multiple matched datas after being sorted；
According to the demand data amount and predetermined data redundancy rate, according to order from big to small after the sequence
A part of matched data is taken out in multiple matched datas；
De-redundancy processing is carried out to a part of matched data, multiple Redundancy Match data are deleted, so as to obtain and institute
State the corresponding multiple request datas of request data content；
The multiple request data is sent to the demand data side.
Further comprise：The expense of each matched data in a part of matched data is calculated, and is sent to described
Demand data side.
The contribution degree of each matched data in a part of matched data is calculated, and according to the contribution degree and accordingly
The unit price of matched data calculates the expense of each matched data, wherein, each matched data has described
Data source identifier, the demand data root enters according to the data source identifier of each matched data to corresponding data source
Row is paid.
Thus, can solve in de-redundancy processing procedure, the billing issues of each data source.
The product of the data volume sum of a part of matched data and the predetermined data redundancy rate is more than the number
According to demand.
A part of matched data at least includes the matched data of 3 data sources.
The multiple Redundancy Match data are identical with the component requests data in the multiple request data respectively.
A kind of cloud platform for carrying out data exchange, the cloud platform includes：
Classification memory cell, the classification memory cell is classified to multiple data from multiple data sources, and is deposited
Store up as multiple categorical datas, each data in the multiple data have data source identifier；
Acquiring unit, the acquiring unit obtains request data content from demand data side, and the request data content is extremely
Include data category, keyword and data demand less；
Matching degree computing unit, the matching degree computing unit takes out and the data class from the multiple categorical data
Data in the categorical data not matched calculate the multiple coupling number as multiple matched datas, and according to the keyword
According to respective matching degree；
Sequencing unit, the sequencing unit is according to the respective matching degree of the multiple matched data, according to from big to small
Order is ranked up to the multiple matched data, multiple matched datas after being sorted；
Extraction unit, the extraction unit according to the demand data amount and predetermined data redundancy rate, according to from greatly to
Small order extracts a part of matched data from multiple matched datas after the sequence；
De-redundancy unit, the de-redundancy unit carries out de-redundancy processing to a part of matched data, deletes multiple
Redundancy Match data, so as to obtain the multiple request datas corresponding with the request data content；
Send and payment unit, the multiple request data is sent to the demand data by the transmission and payment unit
Side.
A kind of system for carrying out data exchange, the system includes：
Multiple data sources, the multiple data source includes multiple data respectively；
Demand data side, the demand data side sends request data content；
Cloud platform as described above, the cloud platform is obtained according to the request data content from the multiple data
The multiple request datas corresponding with the request data content, demand data side is sent to by the multiple request data.
The present invention also provides a kind of non-volatile memory medium for having and instructing, when executed so that processing
The method that device performs data exchange, the instruction includes：
Classify store instruction, multiple data from multiple data sources classified, and be stored as multiple categorical datas,
Each data in the multiple data have data source identifier；
Instruction is obtained, the request data content from demand data side is obtained, the request data content at least includes number
According to classification, keyword and data demand；
Matching degree computations, takes out from the multiple categorical data in the categorical data matched with the data category
Data as multiple matched datas, and according to the keyword, calculate the multiple respective matching degree of matched data；
Ordering instruction, according to the respective matching degree of the multiple matched data, according to order from big to small to described many
Individual matched data is ranked up, multiple matched datas after being sorted；
Instruction is extracted, according to the demand data amount and predetermined data redundancy rate, according to order from big to small from institute
State in multiple matched datas after sequence and extract a part of matched data；
De-redundancy is instructed, and is carried out de-redundancy processing to a part of matched data, is deleted multiple Redundancy Match data, from
And obtain the multiple request datas corresponding with the request data content；
Send and payment instruction, the multiple request data is sent to the demand data side.
The present invention also provides a kind of device for being used to carry out data trade, and described device includes：Memory, with computer
Executable instruction, and processor, are coupled, and be configured as with the memory：
Multiple data from multiple data sources are classified, and are stored as multiple categorical datas, the multiple data
In each data there is data source identifier；
The request data content from demand data side is obtained, the request data content at least includes data category, closed
Key word and data demand；
The data in the categorical data matched with the data category are taken out from the multiple categorical data as multiple
Matched data, and according to the keyword, calculate the multiple respective matching degree of matched data；
According to the respective matching degree of the multiple matched data, according to order from big to small to the multiple matched data
It is ranked up, multiple matched datas after being sorted；
According to the demand data amount and predetermined data redundancy rate, according to order from big to small after the sequence
A part of matched data is taken out in multiple matched datas；
De-redundancy processing is carried out to a part of matched data, multiple Redundancy Match data are deleted, so as to obtain and institute
State the corresponding multiple request datas of request data content；
The multiple request data is sent to the demand data side.
According to the method for the carry out data exchange of the present invention, cloud platform and system can handle the number from different data sources
According to, that is to say, that the data of the different industries from multiple data sources can be handled, therefore are adapted to versatility and height
Property.The present invention can be ranked up according to matching degree to data, precision when can improve Data Matching.By to from different numbers
Merged according to the data in source, i.e. carry out de-redundancy processing, can effectively help demand data side to improve data exchange process
In capital efficiency, reduce data redudancy.In addition, this invention also solves in de-redundancy processing procedure, each data source
Billing issues.
Brief description of the drawings
Fig. 1 is the schematic diagram for the system for carrying out data exchange according to an embodiment of the invention；
Fig. 2 is the schematic diagram for the cloud platform for carrying out data exchange according to an embodiment of the invention；
Fig. 3 is the flow chart for the method for carrying out data exchange according to an embodiment of the invention；
Fig. 4 is the schematic diagram for the device for carrying out data exchange according to an embodiment of the invention.
Embodiment
Below in conjunction with accompanying drawing, book is described in detail to embodiments of the invention
Fig. 1 is the schematic diagram for the system 10 for carrying out data exchange according to an embodiment of the invention.As shown in figure 1, this is
System 1 includes multiple data source A1, A2......An, cloud platform 12 and data party in request 13.Multiple data source A1, A2......An
Including multiple data, demand data side 13 sends request data content to cloud platform 12.Demand data side 13 is, for example, client.
Fig. 2 is the schematic diagram for the cloud platform 12 for carrying out data exchange according to an embodiment of the invention, and the cloud platform 12 is wrapped
Include classification memory cell 121, acquiring unit 122, matching degree computing unit 123, sequencing unit 124, extraction unit 125, de-redundant
Remaining unit 126, sends and payment unit 127.Fig. 3 is the flow for the method for carrying out data exchange according to an embodiment of the invention
Figure.1-3 is described in detail below in conjunction with the accompanying drawings.
As shown in figure 3, in step S31,121 pairs of classification memory cell comes from multiple data source A1, and A2......An's is more
Individual data are classified, and each data being stored as in multiple categorical datas, multiple data have data source identifier.Its
In, each data source A1, A2......An has multiple different classes of data, by multiple data source A1, in A2......An
All data classified according to different classifications.These classifications can be the disaggregated classification in different industry or industry
Not.For example, classification can be clothes I, and men's clothing II, food III, beverage IV etc., that is to say, that multiple categorical datas refer to clothes
Dress I data, men's clothing II data, food III data, beverage IV data etc., and each categorical data includes one
Or multiple data.
In this example, for example, classification includes a for men's clothing II dataA1, bA1, cA1, aA2, cA2, dA2, cA3, fA3, gA3.Wherein
Subscript A1, A2, A3 are data source identifier respectively, represent which data source is the data come from.For example, data aA1Represent the data
A comes from data source A1.
In step s 32, acquiring unit 122 obtains request data content, the request data content from demand data side 10
At least include data category, keyword and data demand.Wherein, data category refers to above-mentioned clothes I, men's clothing II, food
III, beverage IV etc..Keyword can be the predetermined characteristic value in demand data side 13, including price range, height ranges, pin
Sell region etc..Demand data amount is the higher limit of the predetermined demand to data in demand data side 13.In this example, data class
Not Li Rushi men's clothing II, keyword be, for example, price be less than 500 yuan, demand data amount is, for example, 10G.
In step S33, matching degree computing unit 123 takes out the classification matched with data category from multiple categorical datas
Data in data calculate multiple respective matching degrees of matched data as multiple matched datas, and according to keyword.
In this example, matching degree computing unit 123 is from above-mentioned clothes I, men's clothing II, food III, is taken out in beverage IV etc.
Data in the categorical data matched with men's clothing II are used as multiple matched datas, i.e. it is the data a in men's clothing II to take out classificationA1,
bA1, cA1, aA2, bA2, dA2, cA3, fA3, gA3It is used as multiple matched datas.Then, the keyword in this example, for example：Price
For less than 500 yuan, data a is calculatedA1, bA1, cA1, aA2, cA2, dA2, cA3, fA3, gA3Respective matching degree.In this example, it can use
Existing sampling of data method etc. calculates the matching degree of each matched data.
In step S34, sequencing unit 124 is according to the respective matching degree of multiple matched datas, according to order from big to small
Multiple matched datas are ranked up, multiple matched datas after being sorted.
For example, the matching degree of obtained each matched data is calculated according to step S33, according to order from big to small to this
A little matched datas are ranked up, multiple matched data a after being sortedA1, cA1, aA2, gA3, cA2, dA2, cA3, bA1, fA3。
Then, in step S35, extraction unit 125 according to demand data amount and predetermined data redundancy rate, according to from greatly to
Small order extracts a part of matched data from multiple matched datas after sequence.
In this example, demand data amount is, for example, above-mentioned 10G, and predetermined data redundancy rate is rule of thumb to be directed to such
Not, i.e. men's clothing II, and obtain.In this example, e.g. empirical value 50%.
Wherein, the product of the total amount of data of a part of matched data and predetermined data redundancy rate is more than demand data amount,
In this example, for example, aA1, cA1, aA2, gA3, bA2, dA2Data volume sum T and the product of predetermined data redundancy rate be more than data
Demand, i.e. the ＞ 10G of T × 50%.That is, by aA1, cA1, aA2, gA3, cA2, dA2It is withdrawn as a part of coupling number
According to.
Further, if above-mentioned demand data amount very little, then the quantity of a part of matched data of taking-up will compare
It is few, according to order from big to small, it may can only take out first 3 data for coming from same data source (such as A1), then will
Appear in follow-up transmission and payment process, situation about only being paid to data source A1, so to remainder data source (for example
A2, A3) cause to pay inequitable situation.Therefore, in the present invention, above-mentioned a part of matched data at least includes 3 data
The matched data in source, that is to say, that extraction unit 125 can extract the matched data of at least three data source, such as aA1, aA2, gA3,
In this way, it is possible in follow-up transmission and payment process, to three data source A1, A2, A3 is paid accordingly.
In step S36, a part of matched data of 126 pairs of de-redundancy unit carries out de-redundancy processing, deletes multiple redundancies
Matched data, so as to obtain the multiple request datas corresponding with request data content.
In this example, de-redundancy unit 126 is to aA1, cA1, aA2, gA3, cA2, dA2De-redundancy processing is carried out, multiple redundancies are deleted
Matched data, for example, deleting Redundancy Match data aA2, cA2, so as to obtain the multiple number of requests corresponding with request data content
According to aA1, cA1, gA3, dA2.That is, after multiple Redundancy Match data in a part of matched data are deleted, it is remaining to be exactly
Multiple request datas.Wherein, multiple Redundancy Match data aA2, cA2Respectively with the component requests data a in multiple request datasA1,
cA1It is identical.
Above-mentioned de-redundancy processing, is exactly to carry out data fusion to the data from different data sources, deletes redundancy (weight
It is multiple) data.Carrying out the method for data fusion has a lot, for example, can be extracted in the prior art by large-scale machines study
Characteristic, by way of data clusters, data statistics, or the method for deep learning carries out data fusion.
In step S37, send and payment unit 127 is by multiple request data aA1, cA1, gA3, dA2It is sent to demand data
Side 13.Send and payment unit 127 calculates the expense for each matched data stated in a part of matched data, and be sent to data
Party in request 13.
The contribution degree that each matched data is calculated with payment unit 127 is sent, and according to contribution degree and corresponding coupling number
According to unit price calculate the expense of each matched data, each matched data has data source identifier, according to party in request 13
Corresponding data source is paid according to the data source identifier of each matched data.
Specifically, calculating each matched data a respectivelyA1, cA1, aA2, gA3, cA2, dA2Contribution degree, the contribution degree is one
Divide the inverse of the number of identical matched data in matched data, i.e. the redundancy rate (repetitive rate) of each matched data.For example,
For data aA1, the identical data a in a part of matched dataA1, aA2Number be 2, then data aA1Contribution degree be
0.5, similarly, data aA2Contribution degree be 0.5.Similarly, data cA1, cA2Respective contribution degree is 0.5, data gA3Contribution degree
It is 1, data dA2Contribution degree be 1.
Each matched data aA1, cA1, aA2, gA3, cA2, dA2Respective unit price is PaA1, PcA1, PaA2, PgA3, PcA2, PdA2,
The expense of each matched data is so calculated according to the contribution degree of each matched data and its unit price, i.e. data aA1Expense
It is PaA1× 0.5, data cA1Expense be PcA1× 0.5, data aA2Expense be PaA2× 0.5, data gA3Expense be PgA3
× 1, data cA2Expense be PcA2× 0.5, data dA2Expense be PdA2×1。
Each matched data aA1, cA1, aA2, gA3, cA2, dA2There is data source identifier, i.e. subscript A1, A1, A2 respectively,
A3, A2, A2.Corresponding data source is determined according to the data source identifier of each matched data, i.e. according to aA1Data source mark
Know symbol A1 and determine corresponding data source A1, by that analogy.Send and payment unit 127 make it that demand data side 13 will come from data
The reimbursement of expense of source A1 data gives corresponding data source A1, i.e. so that demand data side 13 is by the data from data source A1
aA1, cA1Expense PaA1× 0.5 and PcA1× 0.5 pays corresponding data source A1.Similarly, by the data from data source A2
aA2, cA2, dA2Expense PaA2× 0.5, PcA2× 0.5 and PdA2× 1 pays corresponding data source A2, will come from data source
A3 data gA3Expense PgA3× 1 pays corresponding data source A3.
In another embodiment of the present invention there is provided a kind of non-volatile memory medium for having and instructing, when execution institute
When stating instruction so that the method for computing device data exchange, the instruction includes：
Classify store instruction, multiple data from multiple data sources classified, and be stored as multiple categorical datas,
Each data in the multiple data have data source identifier；
Instruction is obtained, the request data content from demand data side is obtained, the request data content at least includes number
According to classification, keyword and data demand；
Matching degree computations, takes out from the multiple categorical data in the categorical data matched with the data category
Data as multiple matched datas, and according to the keyword, calculate the multiple respective matching degree of matched data；
Ordering instruction, according to the respective matching degree of the multiple matched data, according to order from big to small to described many
Individual matched data is ranked up, multiple matched datas after being sorted；
Instruction is extracted, according to the demand data amount and predetermined data redundancy rate, according to order from big to small from institute
State in multiple matched datas after sequence and extract a part of matched data；
De-redundancy is instructed, and is carried out de-redundancy processing to a part of matched data, is deleted multiple Redundancy Match data, from
And obtain the multiple request datas corresponding with the request data content；
Send and payment instruction, the multiple request data is sent to the demand data side.
In yet another embodiment of the present invention there is provided a kind of device for being used to carry out data trade, as shown in figure 4, should
Device 40 includes memory 41 and processor 42, and memory 41 has the instruction that computer can perform, processor 42 and memory
41 couplings, and be configured as：
Multiple data from multiple data sources are classified, and are stored as multiple categorical datas, the multiple data
In each data there is data source identifier；
The request data content from demand data side is obtained, the request data content at least includes data category, closed
Key word and data demand；
The data in the categorical data matched with the data category are taken out from the multiple categorical data as multiple
Matched data, and according to the keyword, calculate the multiple respective matching degree of matched data；
According to the respective matching degree of the multiple matched data, according to order from big to small to the multiple matched data
It is ranked up, multiple matched datas after being sorted；
According to the demand data amount and predetermined data redundancy rate, according to order from big to small after the sequence
A part of matched data is taken out in multiple matched datas；
De-redundancy processing is carried out to a part of matched data, multiple Redundancy Match data are deleted, so as to obtain and institute
State the corresponding multiple request datas of request data content；
The multiple request data is sent to the demand data side.
The present invention can classify to multiple data from multiple data sources, that is to say, that can handle come from it is many
The data of the different industries of individual data source, therefore with versatility and high degree of adaptability.The present invention is each according to multiple matched datas
From matching degree, when being ranked up according to order from big to small to multiple matched datas, therefore can improve Data Matching
Precision., can by carrying out de-redundancy processing to a part of matched data, i.e. the data from different data sources are merged
Effectively to help demand data side to improve the capital efficiency in data exchange process, data redudancy is reduced.
Further, the present invention is by calculating the expense of each matched data in a part of matched data, and is sent to number
According to party in request, it can solve in de-redundancy processing procedure, the billing issues of each data source.
All numerical value provided in this manual are merely illustrative, rather than for limiting the scope of the present invention.
Although by being described in conjunction with specific embodiments to the present invention, for the ordinary artisan of this area,
With changing will be apparent according to many replacements, modification made after mentioned above.Therefore, when such replacement, modification
When being fallen into change within the spirit and scope of appended claims, it should be included in the present invention.
Claims (13)
1. a kind of method for carrying out data exchange, it is characterised in that methods described includes：
Multiple data from multiple data sources are classified, and are stored as in multiple categorical datas, the multiple data
Each data have data source identifier；
The request data content from demand data side is obtained, the request data content at least includes data category, keyword
With data demand；
The data in the categorical data matched with the data category are taken out from the multiple categorical data as multiple matchings
Data, and according to the keyword, calculate the multiple respective matching degree of matched data；
According to the respective matching degree of the multiple matched data, the multiple matched data is carried out according to order from big to small
Sequence, multiple matched datas after being sorted；
According to the demand data amount and predetermined data redundancy rate, according to order from big to small from multiple after the sequence
A part of matched data is taken out in matched data；
De-redundancy processing is carried out to a part of matched data, multiple Redundancy Match data are deleted, so as to obtain asking with described
Seek multiple request datas that data content is corresponding；
The multiple request data is sent to the demand data side.
2. method for interchanging data as claimed in claim 1, it is characterised in that further comprise：Calculate the part matching
The expense of each matched data in data, and it is sent to the demand data side.
3. method for interchanging data as claimed in claim 2, it is characterised in that calculate in a part of matched data each
Contribution degree with data, and each matched data is calculated according to the unit price of the contribution degree and corresponding matched data
The expense,
Wherein, each matched data has the data source identifier, and the demand data root is according to each matching
The data source identifier of data is paid to corresponding data source.
4. method for interchanging data as claimed in claim 3, it is characterised in that the data volume sum of a part of matched data
It is more than the demand data amount with the product of the predetermined data redundancy rate.
5. method for interchanging data as claimed in claim 4, it is characterised in that a part of matched data at least includes 3
The matched data of data source.
6. method for interchanging data as claimed in claim 5, it is characterised in that the multiple Redundancy Match data respectively with it is described
Component requests data in multiple request datas are identical.
7. a kind of cloud platform for carrying out data exchange, it is characterised in that the cloud platform includes：
Classification memory cell, the classification memory cell is classified to multiple data from multiple data sources, and is stored as
Each data in multiple categorical datas, the multiple data have data source identifier；
Acquiring unit, the acquiring unit obtains the request data content from demand data side, and the request data content is extremely
Include data category, keyword and data demand less；
Matching degree computing unit, the matching degree computing unit takes out and the data category from the multiple categorical data
Data in the categorical data matched somebody with somebody calculate the multiple matched data each as multiple matched datas, and according to the keyword
From matching degree；
Sequencing unit, the sequencing unit is according to the respective matching degree of the multiple matched data, according to order from big to small
The multiple matched data is ranked up, multiple matched datas after being sorted；
Extraction unit, the extraction unit is according to the demand data amount and predetermined data redundancy rate, according to from big to small
Order extracts a part of matched data from multiple matched datas after the sequence；
De-redundancy unit, the de-redundancy unit carries out de-redundancy processing to a part of matched data, deletes multiple redundancies
Matched data, so as to obtain the multiple request datas corresponding with the request data content；
Send and payment unit, the multiple request data is sent to the demand data side by the transmission and payment unit.
8. cloud platform as claimed in claim 7, it is characterised in that the transmission and payment unit further calculate described one
The expense for each matched data divided in matched data, and it is sent to the demand data side.
9. cloud platform as claimed in claim 8, it is characterised in that the transmission and payment unit calculate each coupling number
According to contribution degree, and described according to the unit price of the contribution degree and corresponding matched data calculating each matched data
Expense,
Wherein, each matched data has the data source identifier, and the demand data root is according to each matching
The data source identifier of data is paid to corresponding data source.
10. cloud platform as claimed in claim 9, it is characterised in that the data volume sum of a part of matched data and institute
The product for stating predetermined data redundancy rate is more than the demand data amount.
11. cloud platform as claimed in claim 10, it is characterised in that a part of matched data at least includes 3 data
The matched data in source.
12. cloud platform as claimed in claim 11, it is characterised in that the multiple Redundancy Match data respectively with it is the multiple
Component requests data in request data are identical.
13. a kind of system for carrying out data exchange, the system includes：
Multiple data sources, the multiple data source includes multiple data respectively；
Demand data side, the demand data side sends request data content；
Cloud platform as described in claim 7-12, the cloud platform is according to the request data content, from the multiple data
It is middle to obtain the multiple request datas corresponding with the request data content, the multiple request data is sent to demand data
Side.
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