CN109033204A - A kind of level integration histogram Visual Inquiry method based on WWW - Google Patents
A kind of level integration histogram Visual Inquiry method based on WWW Download PDFInfo
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Abstract
The invention discloses a kind of Visual Inquiry methods of level integration histogram, comprising the following steps: step 1: configuring to raw data set, including discretization interval number, crosses the condition of filter data and need to carry out the dimension of aggregate statistics；Step 2: with the building of offline pretreatment mode and storage hierarchy partition tree, wherein data are divided into multiple data subsets by level partition tree, and the statistical nature of each data subset is expressed by integration histogram；Step 3: visualization space uniform is discretized into specific zonule, distinguishing hierarchy tree in the coordinate input step 2 of zonule is subjected to range query, the range query is the process found the data subset for having intersection with target area and remove estimation target area statistical nature with the integration histogram of the intersection, and all zonules obtain a matrix about statistical nature after being all performed range query；Step 4: visual element binding being carried out to the matrix of statistical nature, carries out visualization request.
Description
Technical field
The present invention relates to rapid visual inquiry field, in particular to a kind of method for quickly querying of level integration histogram.
Background technique
In the visual analysis scene of large-scale structure data, people need to understand and grind from the statistical nature of data
The distribution for studying carefully data by characteristic distributions Rule Summary, carries out decision.The most common aminated polyepichlorohydrin (refers to and calculates from a class value
A value out) visual representation is generally carried out by histogram or discretization scatter plot etc..When data volume is sufficiently large, directly traverse
The method of data item counting statistics feature will be unable to the real-time demand for meeting Interactive Visualization exploration.How to tie on a large scale
Quick search obtains the data of specified range in structure data, for example, traffic resource real-time management scheduling, financial transaction reality
When monitoring etc., become internet, traffic, space flight, the heat subject in the fields such as business.
For the large-scale structure data in reality, dimension is high, and data item is more, and data modality and format are a variety of more
Sample, data distribution are unique.Visual Inquiry operation is executed on the data set of such bulky complex, can exist can not timely respond to very
To time-consuming too long problem.Many existing methods are all to carry out query optimization in database level, they are accurate in order to obtain
Result, need to be arranged on data set to consider to be configured with simultaneously and external be expressed conducive to what user understood；In addition, a few thing base
In the target of approximation use a series of approximate query strategy (approximate query refers to reduce the response time of inquiry,
Data are inquired with approximate strategy), it is being reached based on histogram table and be based on wavelet transformation such as based on sampling algorithm
Technology.
Above-mentioned approximation technique some has used fixed precomputation mode, is confined to certain statistical feature, may not apply to
A plurality of types of data, such as dynamic data and flow data；Some is only limitted to low-dimensional situation, and High Dimensional Data Set calculates required memory
It is excessive.
Summary of the invention
The present invention provides a kind of Visual Inquiry methods of level integration histogram, and search time is reduced to 500 milliseconds
Within, reach interaction level while substantially reducing the demand to storage.
A kind of Visual Inquiry method of level integration histogram, comprising the following steps:
Step 1: raw data set being configured, including discretization interval number, the condition of filter data is crossed and needs to carry out
The dimension of aggregate statistics；
Step 2: the data handled based on the configuration in step 1, with the building of offline pretreatment mode and storage hierarchy
Partition tree, wherein data are divided into multiple data subsets by level partition tree, and the statistical nature of each data subset is straight by integrating
Square figure is expressed；
Step 3: specific zonule being discretized into for space uniform is visualized by the configuration in step 1, for each
Distinguishing hierarchy tree in the coordinate input step 2 of zonule is carried out range query by block zonule, and the range query is to find
There is the data subset of intersection with target area and go the process of estimation target area statistical nature with the integration histogram of the intersection,
All zonules obtain a matrix about statistical nature after being all performed range query；
Step 4: visual element binding being carried out to the matrix of the statistical nature of step 3, carries out visualization request.
Time loss is transferred to pretreatment stage by this method, and the approximate meter in allowable range of error is carried out to query result
It calculates, compared with the conventional method, carrying cost, and the quantity of time complexity and data point can be significantly reduced in this querying method
It is unrelated, efficient online Visual Inquiry can be carried out.
The present invention is based on the configuration parameters of user to pre-process raw data set and target visible space, and passes through layer
Secondary partitioning algorithm carries out distinguishing hierarchy to data set, to realize the table for using different accuracy and scale to the region of different distributions
It reaches.For each sub-regions, the statistical nature in the approximate region is removed with integration histogram, in Visual Inquiry, system is utilized
Distinguishing hierarchy tree, which fast and effeciently traverses, searches target area collection merging return approximation, to obtain the approximate system of target area
Count feature.
Compared with the existing methods, time loss is transferred to data preprocessing phase by this method, visual to that may need
The offline pretreatment in advance of the data set of inquiry, obtains a kind of efficient approximate expression to data set, so can be used for it is subsequent
Line Visual Inquiry.This method is based on the conception that gradually refines again of approximation, it is only necessary to storing data counted after integration histogram,
Other many Visual Inquiry methods need to store initial data, need biggish time and space loss, while cannot be preferable
Ground captures the distribution of data, therefore the application of this method is wider.
In order to improve the scope of application and intelligence of the invention, it is preferred that further include that interactively adjusting can in step 4
Depending on the parameter changed, and instant visible feedback result is obtained in visualization process.
In order to further increase computational efficiency, it is preferred that in step 2, raw data set is with n dimension D=
{D1,…,DnHigh Dimensional Data Set V, the domain of each dimension is expressed as { [a1,b1],…,[an,bn]}。
In order to further increase computational efficiency, it is preferred that in step 2, data are divided into multiple data by level partition tree
Subset detailed process are as follows: entire data space is subjected to recurrence division, generates the tree construction of a layering, data space is reconstructed
For V '={ v '1,…v′i…v′p, wherein each v 'i∈ V ' corresponds to a leaf node for tree.
In order to further increase computational efficiency, it is preferred that in step 2, the integration histogram is a kind of expansion of summation table
It opens up, the value of each grid is equal to the summation of all values in its upper left corner in table, and then the value in each grid can be by four
The plus-minus of a value obtains.The English name of summation table is a two-dimensional table summed area table.
In order to further increase computational efficiency, it is preferred that in step 2, calculating integration histogram, detailed process is as follows: right
In by N1×…NdGrid carries out the d dimension data collection of branch mailbox, and is summarized by the histogram with b branch mailbox number, leaf node
Integration histogram is defined as:
Wherein, x1,…,xdIt is the index of the branch mailbox in d dimension, b is the index of branch mailbox in histogram, h (x1,…,
xd) indicate the histogram of each grid intermediate value；
The integration histogram of any rectangular area can be calculated by following manner in data space:
Wherein xpIt is the angle point of rectangular area, p ∈ { 0,1 }d。
Beneficial effects of the present invention:
The Visual Inquiry method of level integration histogram of the invention is realized and uses different accuracy to the region of different distributions
And scale expression mode, the approximate statistical feature of target area is obtained, time loss is transferred to data preprocessing phase, to possible
The data set offline pretreatment in advance for needing Visual Inquiry, obtains a kind of efficient approximate expression to data set, again based on approximation
The conception gradually refined, it is only necessary to storing data counted after integration histogram, reduce time and space loss, while preferably
Ground captures the distribution of data, and application is wider.
Detailed description of the invention
Fig. 1 is the flow diagram of the Visual Inquiry method of level integration histogram of the invention.
Fig. 2 is that the POI data collection on map is divided into the result schematic diagram after multiple subsets by level partition tree.
Fig. 3 is the amplified result schematic diagram of close quarters of Fig. 2.
Specific embodiment
As shown in Figure 1, the Visual Inquiry method of the level integration histogram of the present embodiment the following steps are included:
Step 1: there is n dimension D={ D for one1,…,DnHigh Dimensional Data Set V, each of which dimension domain difference
It is expressed as { [a1,b1],…,[an,bn], term branch mailbox is a kind of user-defined scale bar for aggregated data space, is used
The dimension for carrying out branch mailbox, filtering and polymerization is specified at family from High Dimensional Data Set, as shown in figure 1 shown in wire frame a.
Step 2: the data handled based on the configuration in step 1, system use the space partitioning algorithm of R tree first,
Entire data space is carried out recurrence division by the variant R* tree that R tree is used in the present embodiment detailed process, to generate one
The tree construction of layering, as shown in figure 1 shown in wire frame b, as shown in Figures 2 and 3, Fig. 3 can see close quarters and be divided into more
Subspace, and division result is preferable.Data space is reconfigured as V '={ v '1,…v′p, wherein each v 'i∈ V ' is corresponding
In a leaf node for R tree.Then integration histogram is calculated on all leaf nodes, as shown in figure 1 shown in wire frame c, it is to ask
With a kind of extension of table, the English name of table of summing is a two-dimensional table summed area table, each in table
The value of grid is equal to the summation of all values in its upper left corner, and then the value in each grid can be obtained by the plus-minus of four values
?.
Store that single scalar value is different, and integration histogram summarizes falls in each net in each grid from original summation table
The distribution of data point in lattice calculates the histogram of all data points on leaf node within the scope of each grid, and is similar to logical
The mode for crossing summation meter calculation rectangle region thresholding returns to the result of inquiry.
For by N1×…NdGrid carries out the d dimension data collection of branch mailbox, and is carried out by the histogram with b branch mailbox number
Summarize, the integration histogram of leaf node is defined as:
Wherein, x1,…,xdIt is the index of the branch mailbox in d dimension, b is the index of branch mailbox in histogram, h (x1,…,
xd) indicate the histogram of each grid intermediate value, so in data space any rectangular area integration histogram can by with
Under type calculates:
Wherein xpIt is the angle point of rectangular area, p ∈ { 0,1 }d。
Step 3: user defines a query contextWith an aggregate function A, two
Person forms an aggregate query, is expressed as Q (R, A), which is located at range for polymerization respectivelyInterior data point.
Obtained the range of each query region, can by the integration histogram in step 2 in constant time to every
The value in a branch mailbox region is inquired, and as shown in figure 1 shown in wire frame e, and a result histogram is returned to, so as to estimate approximation
Polymerization result.
Step 4: after obtaining approximate polymerization result, user can carry out some visualized operation requests to it, as shown in figure 1 line
Shown in frame d, and since it is expected that the level integration histogram calculated is stored in memory, so visualization and aggregate query
Building all executes online, and user can interactively adjust visual parameter, and obtains immediately in visualization process
Visible feedback result.
Claims (6)
1. a kind of Visual Inquiry method of level integration histogram, which comprises the following steps:
Step 1: raw data set being configured, including discretization interval number, the condition of filter data is crossed and is polymerize
The dimension of statistics；
Step 2: the data handled based on the configuration in step 1, with the building of offline pretreatment mode and storage hierarchy divides
Tree, wherein data are divided into multiple data subsets by level partition tree, and the statistical nature of each data subset is by integration histogram
It is expressed；
Step 3: specific zonule is discretized into for space uniform is visualized by the configuration in step 1, it is small for each piece
Distinguishing hierarchy tree in the coordinate input step 2 of zonule is carried out range query by region, and the range query is searching and mesh
There is the data subset of intersection in mark region and goes the process of estimation target area statistical nature with the integration histogram of the intersection, owns
Zonule obtains a matrix about statistical nature after being all performed range query；
Step 4: visual element binding being carried out to the matrix of the statistical nature of step 3, carries out visualization request.
2. the Visual Inquiry method of level integration histogram as described in claim 1, which is characterized in that in step 4, further include
Visual parameter is interactively adjusted, and obtains instant visible feedback result in visualization process.
3. the Visual Inquiry method of level integration histogram as described in claim 1, which is characterized in that in step 2, original number
According to collection for n dimension D={ D1..., DnHigh Dimensional Data Set V, the domain of each dimension is expressed as { [a1,
b1] ..., [an, bn]}。
4. the Visual Inquiry method of level integration histogram as described in claim 1, which is characterized in that in step 2, data quilt
Distinguishing hierarchy tree is divided into multiple data subset detailed processes are as follows: entire data space is carried out recurrence division, generates one point
The tree construction of layer, data space are reconfigured as V '={ v '1... v 'i...v′p, wherein each v 'i∈ V ' corresponds to tree
One leaf node.
5. the Visual Inquiry method of level integration histogram as claimed in claim 4, which is characterized in that in step 2, the product
Dividing histogram is a kind of extension of summation table, and the value of each grid is equal to the summation of all values in its upper left corner in table, in
It is that value in each grid can be obtained by the plus-minus of four values.
6. the Visual Inquiry method of level integration histogram as claimed in claim 5, which is characterized in that in step 2, calculate product
Dividing histogram, detailed process is as follows: for by N1×...NdGrid carries out the d dimension data collection of branch mailbox, and by with b points
The histogram of case number is summarized, the integration histogram of leaf node is defined as:
Wherein, x1..., xdIt is the index of the branch mailbox in d dimension, b is the index of branch mailbox in histogram, h (x1..., xd)
Indicate the histogram of each grid intermediate value；
The integration histogram of any rectangular area can be calculated by following manner in data space:
Wherein xpIt is the angle point of rectangular area, p ∈ { 0,1 }d。
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