CN109165268A - The measurement model construction method of data and data map based on architecture dimension - Google Patents
The measurement model construction method of data and data map based on architecture dimension Download PDFInfo
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- CN109165268A CN109165268A CN201811163659.3A CN201811163659A CN109165268A CN 109165268 A CN109165268 A CN 109165268A CN 201811163659 A CN201811163659 A CN 201811163659A CN 109165268 A CN109165268 A CN 109165268A
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Abstract
The present invention is the measurement model construction method of data and data map based on architecture dimension；The present invention is based on data maps to be decomposed into time, space, structure, usage frequency, association frequency and variation frequency for entity elements are abstract, so that convenient for comparing between each entity elements, difference between when entity elements Self-variation each state is convenient for comparing；The invention belongs to distributed computings and soft project crossing domain.
Description
Technical field
The present invention is the measurement model construction method of data and data map based on architecture dimension, belongs to distributed meter
It calculates and soft project crossing domain.
Background technique
The collection and use of entity elements can generate value, however management is but not yet received in the protection of information security, right
During the difference between entity elements, it will be considered that many factors；Therefore, existing method is between comparison entity element
Difference, and consider that the cost that is spent of transfer entity elements is difficult；
The present invention is the measurement model construction method of data and data map based on architecture dimension；The present invention is based on datagrams
Entity elements are abstracted and are decomposed into time (T) by spectrum, space (I), structure (S), usage frequency (UP), it is associated with frequency (RP) and variation
Frequency (CP)；Wherein usage frequency, association frequency and variation frequency can be obtained again by time, space and structure.
Summary of the invention
Technical problem: existing method is for the difference between comparison entity element, and considers transfer entity elements institute
The cost of cost is difficult.
Technical solution: method of the invention is a kind of tactic method, and the present invention is based on data maps, and entity elements are taken out
As being decomposed into time (T), space (I), structure (S), usage frequency (UP), it is associated with frequency (RP) and variation frequency (CP)；Wherein make
With frequency, being associated with frequency and variation frequency can be obtained again by time, space and structure；For comparing the difference between entity elements
It need to only can consider by time (T), space (I), structure (S), usage frequency when the cost that different and transfer entity elements are spent
(UP), it is associated with frequency (RP) and variation frequency (CP) an obtained integrated value (Value)；By comparing each entity elements
Value considers the difference between entity elements and shifts the cost that is spent of entity elements.
Architecture:
Entity elements are abstracted the present invention is based on data map and are decomposed into time (T), space (I), structure (S), usage frequency
(UP), it is associated with frequency (RP) and variation frequency (CP), considered by calculating the Value value of each entity elements entity elements it
Between difference and the cost that is spent of transfer entity elements；It is given below data map, the time, space, structure, usage frequency,
It is associated with the definition of frequency and the frequency that makes a variation:
Data map (DG)DIK: (DG)DIK:=collection{array,list,stack,queue,tree,graph}
(DG)DIKIt is that various data structures include array (array), chained list (list), stack (stack), queue (queue), set
(tree) and the set of figure (graph) etc.；Data map can recorde the basic structure of entity, in addition, data map can be with
Record the frequency of time and Space expanding；
Time (T): unit general name of the entity elements in different time；It is obtained by data training, Fig. 2 illustrates a number factually
Body is in 12:00 in the position that coordinate is (1,4,3)；Moment t=12:00 shown in Fig. 2 is a T；
Space (I): unit general name of the entity elements in different location；Node location i(x, y, z are obtained by data training), so
I(x, y, z) is obtained by projecting to the one-dimensional space afterwards, i as shown in Figure 2=(1(4,3)) (difference of the value of i because of projection function
And different)；
Structure (S): there is increasing and add deduct in the unit general name of the variation on entity elements recurring structure, such as entity elements interior element
It is few；It is added to obtain by calculating the degree of association size of each node, and by degree of association result s, the degree of association (s) calculates public
Formula is as shown in Equation (1), wherein deg+Represent the out-degree of node, deg-Represent the in-degree of node:
(1)
As shown in Fig. 2,=(sA+sB+sC+sD+sE+sF+sG)=9；
Usage frequency (UP): the calculation formula of frequency used in entity elements, usage frequency is as shown in Equation (2):
(2)
It is associated with frequency (RP): the association frequency of entity elements internal structure, the calculation formula for being associated with frequency are as shown in formula (3):
(3)
Make a variation frequency (CP): the frequency of essential change, the calculation formula for the frequency that makes a variation such as formula occur for entity elements internal structure
(4) shown in:
(4)
Wherein, time, space are indicated, weight of the structure between different entities element indicates the variation function of time, indicates empty
Between change function, indicate that structure change function, specific formula for calculation are as shown in formula (5):
(5)
1) as a=1, b=0,；
2) as a=0, b=1,；
3) as a=0, b=0,；
WhereinExpression changes to obtain difference；
The calculation formula of the integrated value Value of entity elements is as shown in formula (6):
(6)
Wherein, m, n, k indicate usage frequency, are associated with frequency, and make a variation weight of the frequency between different entities element.
The utility model has the advantages that
The method of the present invention proposes the measurement model construction method of data and data map based on architecture dimension, this method tool
It has the advantage that
1) entity elements are abstracted on datagram music stand structure and are decomposed into the time by the present invention, space, structure, usage frequency, association
Frequency, make a variation frequency, easily facilitates entity elements and compares, and inquires;
2) present invention is by total Value value of computational entity element so that entity elements are abstract to be converted to a numerical value, for than
The variation of difference and entity elements itself between more each entity elements has one to compare scheme well.Optimize entity
The storage and conversion of element.
Detailed description of the invention
Fig. 1 is the abstract decomposition entity member of the measurement model construction method of data and data map based on architecture dimension
Plain dendrogram;
Fig. 2 is an instance graph of the measurement model construction method of data and data map based on architecture dimension;
Fig. 3 is the specific flow chart of the measurement model construction method of data and data map based on architecture dimension.
Specific embodiment
The detailed process of the measurement model construction method of data and data map based on architecture dimension is as follows:
Shown in 001 in step 1) corresponding diagram 3, input the typing entity elements of ownership goal, construct can automatically abstracting data
Map framework；
Data map (DG)DIK: (DG)DIK:=collection{array,list,stack,queue,tree,graph}
(DG)DIKIt is that various data structures include array (array), chained list (list), stack (stack), queue (queue), set
(tree) and the set of figure (graph) etc.；Data map can recorde the basic structure of entity, in addition, data map can be with
Record the frequency of time and Space expanding；
Shown in 002 in step 2 corresponding diagram 3, the time change attribute of typing entity elements is calculated, can be trained by data
It arrives, t=12:00 at the time of as shown in Figure 2；
Shown in 003 in step 3) corresponding diagram 3, the spatial variations attribute of typing entity elements is calculated, can be trained by data
To node location i(x, y, z), then i(x, y, z) is obtained by projecting to the one-dimensional space, i as shown in Figure 2=(1(4,3)) (i
Value it is different due to the difference of projection function)；
Shown in 004 in step 4) corresponding diagram 3, the structure change attribute of typing entity elements is calculated, by calculating each section
The degree of association size of point, and degree of association result s is added to obtain, the degree of association (s) calculation formula is as shown in Equation (1), wherein
deg+Represent the out-degree of node, deg-Represent the in-degree of node:
(1)
As shown in Fig. 2,=(sA+sB+sC+sD+sE+sF+sG)=9；
Shown in 005 in step 5) corresponding diagram 3, the usage frequency of typing entity elements is calculated, the calculation formula of usage frequency is such as
Shown in formula (2):
(2)
Shown in 006 in step 6) corresponding diagram 3, the association frequency of typing entity elements is calculated, is associated with the calculation formula of frequency such as
Shown in formula (3):
(3)
Shown in 007 in step 7) corresponding diagram 3, the variation frequency of typing entity elements is calculated, the calculation formula for the frequency that makes a variation is such as
Shown in formula (4):
(4)
Wherein, time, space are indicated, weight of the structure between different entities element indicates the variation function of time, indicates empty
Between change function, indicate that structure change function, specific formula for calculation are as shown in formula (5):
(5)
1) as a=1, b=0,；
2) as a=0, b=1,；
3) as a=0, b=0,；
WhereinExpression changes to obtain difference；
Shown in 008 in step 8) corresponding diagram 3, the integrated value Value of typing entity elements, the calculating of integrated value Value are calculated
Formula is as shown in formula (6):
(6)
Wherein, m, n, k indicate usage frequency, are associated with frequency, and make a variation weight of the frequency between different entities element；
Shown in 009 in step 9) corresponding diagram 3, by the integrated value Value of the obtained typing entity elements of step before, than
The difference of each state when difference and entity elements Self-variation between more each entity elements.
Claims (1)
1. the measurement model construction method that the present invention is data and data map based on architecture dimension；The present invention is based on data
Entity elements are abstracted by map is decomposed into time, space, structure, usage frequency, association frequency and variation frequency, so that each reality
Convenient for comparing between element of volume, difference between when entity elements Self-variation each state is convenient for comparing；Detailed process is as follows:
Shown in 001 in step 1) corresponding diagram 3, input the typing entity elements of ownership goal, construct can automatically abstracting data
Map framework；
Data map (DG)DIK: (DG)DIK:=collection{array,list,stack,queue,tree,graph}
(DG)DIKIt is that various data structures include array (array), chained list (list), stack (stack), queue (queue), set
(tree) and the set of figure (graph) etc.；Data map can recorde the basic structure of entity, in addition, data map can be with
Record the frequency of time and Space expanding；
Shown in 002 in step 2 corresponding diagram 3, the time change attribute of typing entity elements is calculated, can be trained by data
It arrives, t=12:00 at the time of as shown in Figure 2；
Shown in 003 in step 3) corresponding diagram 3, the spatial variations attribute of typing entity elements is calculated, can be trained by data
To node location i(x, y, z), then i(x, y, z) is obtained by projecting to the one-dimensional space, i as shown in Figure 2=(1(4,3)) (i
Value it is different due to the difference of projection function)；
Shown in 004 in step 4) corresponding diagram 3, the structure change attribute of typing entity elements is calculated, by calculating each section
The degree of association size of point, and degree of association result s is added to obtain, the degree of association (s) calculation formula is as shown in Equation (1), wherein
deg+Represent the out-degree of node, deg-Represent the in-degree of node:
(1)
As shown in Fig. 2,=(sA+sB+sC+sD+sE+sF+sG)=9；
Shown in 005 in step 5) corresponding diagram 3, the usage frequency of typing entity elements is calculated, the calculation formula of usage frequency is such as
Shown in formula (2):
(2)
Shown in 006 in step 6) corresponding diagram 3, the association frequency of typing entity elements is calculated, is associated with the calculation formula of frequency such as
Shown in formula (3):
(3)
Shown in 007 in step 7) corresponding diagram 3, the variation frequency of typing entity elements is calculated, the calculation formula for the frequency that makes a variation is such as
Shown in formula (4):
(4)
Wherein, time, space are indicated, weight of the structure between different entities element indicates the variation function of time, indicates empty
Between change function, indicate that structure change function, specific formula for calculation are as shown in formula (5):
(5)
1) as a=1, b=0,；
2) as a=0, b=1,；
3) as a=0, b=0,；
WhereinExpression changes to obtain difference；
Shown in 008 in step 8) corresponding diagram 3, the integrated value Value of typing entity elements, the calculating of integrated value Value are calculated
Formula is as shown in formula (6):
(6)
Wherein, m, n, k indicate usage frequency, are associated with frequency, and make a variation weight of the frequency between different entities element；
Shown in 009 in step 9) corresponding diagram 3, by the integrated value Value of the obtained typing entity elements of step before, than
The difference of each state when difference and entity elements Self-variation between more each entity elements.
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| CN113254527A (en) * | 2021-04-22 | 2021-08-13 | 杭州欧若数网科技有限公司 | Optimization method of distributed storage map data, electronic device and storage medium |
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Application publication date: 20190108 |