CN109033457A - The associated auditing method of Various database and system - Google Patents
The associated auditing method of Various database and system Download PDFInfo
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Abstract
The present invention provides a kind of associated auditing systems of Various database, including being stored in several discrete texts of first database and being stored in several mappings of the second database herein, personnel are repeatedly associated with one or more mapping text by a different discrete texts, wherein each discrete text and mapping respectively correspond different business herein.The present invention is by discrete text and its corresponding business and mapping text and its corresponding business phase mapping, it carries out forming a persistence framework, it is able to solve the problem of business object directly accesses data source method or uses layered architecture method, the relatively convenient so that maintenance and upgrade works, and reduce production cost, management cost and the controllable ability for improving software project.
Description
Technical field
The present invention relates to communication network technical fields, and in particular to a kind of associated auditing method of Various database and is
System.
Background technique
Currently, data access mainly has following methods:
1, business object directly accesses data source.
SQL code can appear in each class, and the advantage of the strategy is that coding is simple and fast, and operational efficiency is high, disadvantage
It is to be directly coupled together business processing and data processing, when system scale is bigger, maintenance and upgrade works all very
Complexity may finally cause whole system out of control.
2, using layered architecture.
Data processing business is separated from business object, forms data access layer.The advantages of this method is drop
The low coupling of business processing and data processing becomes relatively to be easy in system maintenance and upgrading.But this method
There are problems, when the mode configuration of database is changed, it is necessary to recompilate source program and issue.So carrying out greatly
It is this because variation bring complexity will also bring very high production cost and management cost when type system development, it reduces soft
The controllable ability of part project.
3, service class is mapped to a persistence framework.
It solves how to realize Object Persistence by one persistence framework of construction.The strategy further shields service class
Dependence to data class, the change of Gree application program and database.When having carried out simple variation, whole system to database
It does not need to change existing coding and issue again.The strategy can substantially reduce coding and the maintenance work of data access,
And the skill requirement to developer.The disadvantage is that this method can generate certain influence to the performance of application program.
In conclusion service class, which is mapped to a this method of persistence framework, has better data access effect,
But business can be mapped and form a frame by a kind of method or system not yet.
Summary of the invention
The present invention provides a kind of associated auditing method of Various database, including being stored in the several discrete of first database
Text repeatedly reflects a different discrete texts with one or more with several mappings this paper of the second database, personnel are stored in
It is associated to penetrate text, comprising the following steps:
Reception staff divides repeatedly by different discrete texts data associated with one or more mapping text
It Huo Qu not discrete text and mapping text；
First policy calculation similarity, output and the discrete text similarity are passed through to the discrete text and mapping text
Lower than the mapping text of the first preset value；
A random text is received, it, will be with to the random text and discrete text by the second policy calculation similarity
Random text similarity is higher than mapping text output associated by the discrete text of the second preset value.
Further,
It is similar by the second policy calculation with discrete text to the random text in one random text of the reception
Degree, after the step of being higher than mapping text output associated by the discrete text of the second preset value with random text similarity, also
Include:
Each mapping text is obtained, the similarity of text is mapped by third policy calculation every two, by similarity height
It is exported respectively in two mapping texts of third preset value；
The mapping text that the similarity of all mapping texts between any two is below third preset value is subjected to statistics to reflect
Firing table list is simultaneously stored in first database.
Further,
It unites in the mapping text that the similarity of all mapping texts between any two is below third preset value
After the step of being calculated as mapping list and being stored in first database, further includes:
The discrete text of the first preset value will be higher than with mapping text similarity, with mapping text relationship maps one by one；
The random text of the second preset value will be higher than with discrete text similarity, with the associated mapping text one of discrete text
One relationship maps.
Further,
First strategy, the second strategy and third strategy are same policy.
Further,
It is described to include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,
L,An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjTable
Show that wherein j-th of mapping text, m are the quantity for mapping text；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| table
Show discrete text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjIt is all
Number of characters；
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text
This Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map
Text BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmIt is default
Critical value.
A kind of associated auditing system of Various database, several discrete texts and storage including being stored in first database
Herein in several mappings of the second database, personnel are repeatedly related to one or more mapping text by a different discrete texts
Connection, characterized in that further include with lower module:
Input interface module: for reception staff repeatedly by different a discrete text and one or more mapping texts
Associated data, and discrete text and mapping text are obtained respectively；
Data processing module: for passing through the first policy calculation similarity, output to the discrete text and mapping text
It is lower than the mapping text of the first preset value with the discrete text similarity；
First computing module: for receiving a random text, the second plan is passed through to the random text and discrete text
Approximation calculates similarity, and mapping text output associated by the discrete text of the second preset value will be higher than with random text similarity.
Further,
Auditing system further include:
Mapping list supplementary module: for obtaining each mapping text, text is mapped by third policy calculation every two
This similarity exports two mapping texts that similarity is higher than third preset value respectively；
The mapping text that the similarity of all mapping texts between any two is below third preset value is subjected to statistics to reflect
Firing table list is simultaneously stored in first database.
Further,
Auditing system further include:
Generation module: for the discrete text of the first preset value will to be higher than with mapping text similarity, with mapping text one
One relationship maps；
The random text of the second preset value will be higher than with discrete text similarity, with the associated mapping text one of discrete text
One relationship maps.
Further,
First strategy, the second strategy and third strategy are same policy.
Further,
It is described to include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,
L,An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjTable
Show that wherein j-th of mapping text, m are the quantity for mapping text；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| table
Show discrete text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjIt is all
Number of characters；
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text
This Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map
Text BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmIt is default
Critical value.
The present invention has the effect that
By the mapping framework of setting one at least one mapping text corresponding with discrete text, when needs are to random text
When this is found, can directly find discrete text corresponding with random text can find with one of its phase mapping or
Multiple mapping texts are quickly realized and find information corresponding with random text, and each mapping text can correspond to
Several different data, can be a website, can be a series of source code etc..
Other features and advantages of the present invention will be illustrated in the following description, also, partly becomes from specification
It obtains it is clear that understand through the implementation of the invention.The objectives and other advantages of the invention can be by written explanation
Specifically noted structure is achieved and obtained in book, claims and attached drawing.
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention
It applies example to be used to explain the present invention together, not be construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the flow diagram of the associated auditing method of Various database；
Fig. 2 is the structural schematic diagram of the associated auditing system of Various database.
Specific embodiment
Hereinafter, preferred embodiments of the present invention will be described with reference to the accompanying drawings, it should be understood that preferred reality described herein
Apply example only for the purpose of illustrating and explaining the present invention and is not intended to limit the present invention.
A kind of associated auditing method of Various database, several discrete texts and storage including being stored in first database
Herein in several mappings of the second database, wherein each discrete text or mapping text are corresponding with different business, Ren Yuanduo
It is secondary that a different discrete texts is associated with one or more mapping text, pass through manual typing or machine learning typing
Mode respectively to first database and the second database difference typing, storage discrete text and mapping herein, wherein discrete text
This and mapping can be arbitrary title, words and phrases, article, the lyrics, Tool-file, programming code etc. herein.Then special
Discrete text and mapping herein by manual association or are arbitrary machine algorithm and are associated by personnel according to various demands
Mapping.
Including following steps as shown in Figure 1.S1, obtaining step: reception staff is repeatedly by a different discrete texts
Data associated with one or more mapping text, and discrete text and mapping text are obtained respectively.Obtaining step is for obtaining
Take special personnel that discrete text and mapping herein by manual association or are arbitrary machine algorithm according to various demands
It is associated mapping data.S2, first calculate step: the first policy calculation similarity is passed through to discrete text and mapping text,
Output is lower than the mapping text of the first preset value with the discrete text similarity, and the main purpose of the step is to be lower than similarity
First preset value discrete text and mapping text are sent to personnel and see, because similarity is low it is possible that being because personnel are associated with out
Now fault causes, so personnel is needed to carry out being confirmed whether that there are mistakes.
S3, search step: receiving a random text, passes through the second policy calculation to the random text and discrete text
Similarity will be higher than mapping text output associated by the discrete text of the second preset value with random text similarity.After acquisition
After step, when personnel need to search for the corresponding mapping text of a random text, by inputting random text, find with
Its corresponding discrete text can quickly search out mapping text associated there in turn.
The working principle of above scheme is as follows:
Such as discrete text is entitled " similarity programming code ", potential business corresponding to discrete text is that calculating is similar
There is mapping text entitled " the first similarity programming code ", " second of similarity programming generation at this time in the programming source code of degree
Code " and " the first statistics programming code ", " the first similarity programming code " the first corresponding calculating calculate the volume of similarity
Journey source code, the programming source code of " second of similarity programming code " corresponding second of calculating similarity, " the first statistics volume
Range code " corresponds to the first programming source code for statistics, calculates above-mentioned each mapping text and discrete text automatically at this time
Similarity, similarity be greater than the first preset value the discrete text phase mapping with entitled " similarity programming code ", be added name
It is pre- to be greater than first for the similarity of " the first similarity programming code " and " second of similarity programming code " and discrete text
If value, entitled at this time " similarity programming code " and the first entitled similarity programming code " and " second of similarity programming generation
The discrete text of code " has mapping relations.When user is during first use, need to find the corresponding industry in relation to similarity
Business, file etc. input the random text of entitled " similarity ", at this time by finding the random text with " similarity " in discrete text
This has the discrete text of highest similarity, and addition searches out highest discrete with the random text similarity of entitled " similarity "
Text is entitled " similarity programming code ", then user can the discrete text institute group of " similarity programming code " entitled by this build up
The persistence framework about mapping in find entitled " the first similarity programming code " and " second similarity programs generation
The mapping text of code " etc..
The working effect of above scheme is as follows:
By be arranged in advance one it is corresponding with discrete text at least one mapping text mapping framework, when need to
When machine text is found, can directly find discrete text corresponding with random text can find with its phase mapping one
A or multiple mapping texts quickly realize and find corresponding with random text information, and each map text can be with
Corresponding several different data, can be a website, can be a series of source code etc..And works as and need to the first number
When being safeguarded according to library, the second database, system can will mapping text be carried out respectively with discrete text it is corresponding, mapping text
When carrying out data update, with bracket typing and corresponding discrete text can be found, compared to traditional typing new mappings text
This when, is then simpler.The present invention by discrete text and its corresponding business and mapping text and its corresponding business phase mapping,
It carries out forming a persistence framework, is able to solve business object and directly accesses data source method or use layered architecture side
The problem of method, the relatively convenient so that maintenance and upgrade works, and reduce production cost, management cost and improve software project
Controllable ability.
In one embodiment, in one random text of the reception, the is passed through to the random text and discrete text
Two policy calculation similarities, by being higher than with random text similarity, mapping text associated by the discrete text of the second preset value is defeated
After out the step of, further includes: each mapping text is obtained, the similarity of text is mapped by third policy calculation every two,
Two mapping texts that similarity is higher than third preset value are exported respectively；All by the similarity of all mapping texts between any two
Mapping text lower than third preset value carries out statistics as mapping list and is stored in first database.It can be with by above step
The purpose automaticly inspected to mapping text is played, if two mapping texts are higher than third preset value, such as says that third is pre-
If value is 0.99, then the two pre-set texts, which have, belongs to identical or especially approximate text in very big situation, that is, has
The two very big possible pre-set texts are corresponding with identical business, and database is caused to save when saving to this kind of business
Twice, the memory of data is wasted, so being shown.If the similarity of all mapping texts between any two is below third
Preset value then proves that big probability at this time is not in the possibility that business repeats storage, all mapping lists can be stored and the
One database can quickly be transferred when there is personnel to need and search mapping text by discrete text and be stored in first database
Map list.
In one embodiment, the similarity of all mapping texts between any two is below third preset value described
After the step of mapping text carries out statistics to map list and being stored in first database, further includes: will be similar to mapping text
Degree is higher than the discrete text of the first preset value, with mapping text relationship maps one by one；Second will be higher than with discrete text similarity
The random text of preset value, with the associated mapping text of discrete text relationship maps one by one.By the way that discrete text is literary in mapping
This association, random text facilitate user quickly to transfer the business corresponding to mapping text in mapping textual association.Wherein first
Strategy, the second strategy and third strategy are same policy.
In one embodiment, include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,
L,An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjTable
Show that wherein j-th of mapping text, m are the quantity for mapping text；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| table
Show discrete text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjIt is all
Number of characters can effectively calculate discrete text and map the similarity between text by above formula, and be with word
Symbol is foundation, so that similarity calculation between the two is more objective.
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text
This Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map
Text BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmIt is default
Critical value.By above formula, the similarity between two texts is not only allowed for, but also with reference to the mapping number of history
Etc. because some high mapping texts of mapping numbers then prove that its generality is higher, compared to other mapping texts because
This is with the higher potential similarity that is more than and can show between character, so that when calculating similarity therebetween more
It is objective.
A kind of associated auditing system of Various database, its structural schematic diagram as shown in Figure 2, including it is stored in the first data
Several discrete texts in library and be stored in several mappings of the second database herein, personnel repeatedly by a different discrete text and
One or more mapping text is associated, characterized in that further includes with lower module:
Input interface module: for reception staff repeatedly by different a discrete text and one or more mapping texts
Associated data, and discrete text and mapping text are obtained respectively；
Data processing module: for passing through the first policy calculation similarity, output to the discrete text and mapping text
It is lower than the mapping text of the first preset value with the discrete text similarity；
First computing module: for receiving a random text, the second plan is passed through to the random text and discrete text
Approximation calculates similarity, and mapping text output associated by the discrete text of the second preset value will be higher than with random text similarity.
The working principle of above scheme is as follows:
Such as discrete text is entitled " similarity programming code ", potential business corresponding to discrete text is that calculating is similar
There is mapping text entitled " the first similarity programming code ", " second of similarity programming generation at this time in the programming source code of degree
Code " and " the first statistics programming code ", " the first similarity programming code " the first corresponding calculating calculate the volume of similarity
Journey source code, the programming source code of " second of similarity programming code " corresponding second of calculating similarity, " the first statistics volume
Range code " corresponds to the first programming source code for statistics, calculates above-mentioned each mapping text and discrete text automatically at this time
Similarity, similarity be greater than the first preset value the discrete text phase mapping with entitled " similarity programming code ", be added name
It is pre- to be greater than first for the similarity of " the first similarity programming code " and " second of similarity programming code " and discrete text
If value, entitled at this time " similarity programming code " and the first entitled similarity programming code " and " second of similarity programming generation
The discrete text of code " has mapping relations.When user is during first use, need to find the corresponding industry in relation to similarity
Business, file etc. input the random text of entitled " similarity ", at this time by finding the random text with " similarity " in discrete text
This has the discrete text of highest similarity, and addition searches out highest discrete with the random text similarity of entitled " similarity "
Text is entitled " similarity programming code ", then user can the discrete text institute group of " similarity programming code " entitled by this build up
The persistence framework about mapping in find entitled " the first similarity programming code " and " second similarity programs generation
The mapping text of code " etc..
The working effect of above scheme is as follows:
By be arranged in advance one it is corresponding with discrete text at least one mapping text mapping framework, when need to
When machine text is found, can directly find discrete text corresponding with random text can find with its phase mapping one
A or multiple mapping texts quickly realize and find corresponding with random text information, and each map text can be with
Corresponding several different data, can be a website, can be a series of source code etc..And works as and need to the first number
When being safeguarded according to library, the second database, system can will mapping text be carried out respectively with discrete text it is corresponding, mapping text
When carrying out data update, with bracket typing and corresponding discrete text can be found, compared to traditional typing new mappings text
This when, is then simpler.The present invention by discrete text and its corresponding business and mapping text and its corresponding business phase mapping,
It carries out forming a persistence framework, is able to solve business object and directly accesses data source method or use layered architecture side
The problem of method, the relatively convenient so that maintenance and upgrade works, and reduce production cost, management cost and improve software project
Controllable ability.
In one embodiment, auditing system further include: mapping list supplementary module: for obtaining each mapping text
This, the similarity of text is mapped by third policy calculation every two, and similarity is higher than to two mappings text of third preset value
This is exported respectively；The mapping text that the similarity of all mapping texts between any two is below third preset value, which is carried out statistics, is
Mapping list is simultaneously stored in first database.The purpose automaticly inspected to mapping text can be played by above step,
If two mapping texts are higher than third preset value, such as say that third preset value is 0.99, then the two pre-set texts have very big
In the case of belong to identical or especially approximate text, that is, there is a strong possibility that the two pre-set texts are corresponding with is identical for tool
Business causes database to save the memory for wasting data twice when saving to this kind of business, so being shown
Show.Prove that big probability is not in industry at this time if the similarity of all mapping texts between any two is below third preset value
Business repeats the possibility of storage, all mapping lists can be stored and first database, when there is personnel to need through discrete text
This lookup can quickly transfer the mapping list stored in first database when mapping text.
In one embodiment, auditing system further include: generation module: for first will to be higher than with mapping text similarity
The discrete text of preset value, with mapping text relationship maps one by one；By with discrete text similarity be higher than the second preset value with
Machine text, with the associated mapping text of discrete text relationship maps one by one.By by discrete text in mapping textual association, random
Text facilitates user quickly to transfer the business corresponding to mapping text in mapping textual association.First strategy, second strategy with
And third strategy is same policy.
In one embodiment, include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,
L,An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjTable
Show that wherein j-th of mapping text, m are the quantity for mapping text；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| table
Show discrete text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjIt is all
Number of characters；By above formula, it can effectively calculate discrete text and map the similarity between text, and be with word
Symbol is foundation, so that similarity calculation between the two is more objective.
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text
This Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map
Text BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmIt is default
Critical value.By above formula, the similarity between two texts is not only allowed for, but also with reference to the mapping number of history
Etc. because some high mapping texts of mapping numbers then prove that its generality is higher, compared to other mapping texts because
This is with the higher potential similarity that is more than and can show between character, so that when calculating similarity therebetween more
It is objective.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of associated auditing method of Various database, including being stored in several discrete texts of first database and being stored in
Herein, personnel are repeatedly related to one or more mapping text by a different discrete texts for several mappings of second database
Connection, characterized in that the following steps are included:
Reception staff obtains respectively repeatedly by different discrete texts data associated with one or more mapping text
Take discrete text and mapping text；
To the discrete text and mapping text by the first policy calculation similarity, output is lower than with the discrete text similarity
The mapping text of first preset value；
Receive a random text, to the random text and discrete text by the second policy calculation similarity, will with it is random
Text similarity is higher than mapping text output associated by the discrete text of the second preset value.
2. auditing method according to claim 1, characterized in that
In one random text of the reception, the second policy calculation similarity is passed through to the random text and discrete text, it will
After the step of being higher than mapping text output associated by the discrete text of the second preset value with random text similarity, further includes:
Each mapping text is obtained, the similarity of text is mapped by third policy calculation every two, similarity is higher than the
Two mapping texts of three preset values export respectively；
The mapping text that the similarity of all mapping texts between any two is below third preset value count as mapping table
List is simultaneously stored in first database.
3. auditing method according to claim 2, characterized in that
Carrying out statistics in the mapping text that the similarity of all mapping texts between any two is below third preset value is
After the step of mapping list and being stored in first database, further includes:
The discrete text of the first preset value will be higher than with mapping text similarity, with mapping text relationship maps one by one；
The random text of the second preset value will be higher than with discrete text similarity, is closed one by one with the associated mapping text of discrete text
Connection mapping.
4. according to the method described in claim 2, it is characterized in that,
First strategy, the second strategy and third strategy are same policy.
5. according to the method described in claim 4, it is characterized in that,
It is described to include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,L,
An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjIndicate it
In j-th of mapping text, m be map text quantity；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| it indicates discrete
Text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjAll characters
Number；
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map text
BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmPreset threshold
Value.
6. a kind of associated auditing system of Various database, including being stored in several discrete texts of first database and being stored in
Herein, personnel are repeatedly related to one or more mapping text by a different discrete texts for several mappings of second database
Connection, characterized in that further include with lower module:
Input interface module: repeatedly that a different discrete texts is related to one or more mapping text for reception staff
The data of connection, and discrete text and mapping text are obtained respectively；
Data processing module: it for passing through the first policy calculation similarity to the discrete text and mapping text, exports and is somebody's turn to do
Discrete text similarity is lower than the mapping text of the first preset value；
First computing module: for receiving a random text, the random text and discrete text are counted by the second strategy
Similarity is calculated, mapping text output associated by the discrete text of the second preset value will be higher than with random text similarity.
7. auditing system according to claim 6, characterized in that
Auditing system further include:
Mapping list supplementary module: for obtaining each mapping text, text is mapped by third policy calculation every two
Similarity exports two mapping texts that similarity is higher than third preset value respectively；
The mapping text that the similarity of all mapping texts between any two is below third preset value count as mapping table
List is simultaneously stored in first database.
8. auditing system according to claim 7, characterized in that
Auditing system further include:
Generation module: it for the discrete text of the first preset value will to be higher than with mapping text similarity, is closed one by one with mapping text
Connection mapping；
The random text of the second preset value will be higher than with discrete text similarity, is closed one by one with the associated mapping text of discrete text
Connection mapping.
9. system according to claim 7, characterized in that
First strategy, the second strategy and third strategy are same policy.
10. system according to claim 9, characterized in that
It is described to include: by the first policy calculation similarity to the discrete text and mapping text
Obtain several discrete text A having in first databasei, discrete text AiCollection be combined into θ1=(A1,A2,L,Ai,L,
An), AiIndicate that wherein i-th of discrete text, n are the quantity of discrete text；
The collection of several mapping texts of the preparatory typing of second database is combined into θ2=(B1,B2,L,Bj,L,Bm), BjIndicate it
In j-th of mapping text, m be map text quantity；
According to following formula, each discrete text A is calculated separatelyiWith mapping text BjSimilarity:
Wherein, AiIndicate i-th of the discrete text currently calculated, BjIndicate wherein j-th of mapping text；|Ai∩Bj| it indicates discrete
Text AiWith mapping text BjBetween identical characters number, | Ai∪Bj| indicate discrete text AiWith mapping text BjAll characters
Number；
Determine the mapping text B for meeting the following conditionsj, the mapping text B of the condition will be metjAs effective mapping text Bm,
The condition are as follows:
Jδ(A,Bi)p≤|Jδ(Ai,Bj)|≤k；
Determine effective mapping text BmComprehensive similarity Jm, and:
Wherein, p is the default lowest critical value of similarity, and k is the default maximum critical value of similarity, QmEffectively to map text
BmHistory map number；C is similarity Jδ(Ai,Bm) weight coefficient；D is QmWeight coefficient；Q0For QmPreset threshold
Value.
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