CN107229815A - Data analysing method and device - Google Patents
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- CN107229815A CN107229815A CN201610180084.0A CN201610180084A CN107229815A CN 107229815 A CN107229815 A CN 107229815A CN 201610180084 A CN201610180084 A CN 201610180084A CN 107229815 A CN107229815 A CN 107229815A
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Abstract
The invention provides a kind of data analysing method and device, the corresponding processing model of node is respectively handled in data analysis flow by building, each processing model is sequentially connected according to dependence, pending initial data handled using the processing model after connection to obtain target data.Each processing model is directly connected to according to dependence in the present invention, the output of upper processing model can be directly inputted in next processing model, and intermediate data is no longer landed, and saves resource, and the loading procedure of intermediate data is omitted, data analysis efficiency is improved.
Description
Technical field
The invention belongs to data processing field, more particularly to a kind of data analysing method and device.
Background technology
Usually contained in data analysis process substantial amounts of data cleansing, feature smooth, characteristic criterion,
Feature refinement, feature selecting etc. handle node.In conventional data analysis, for each processing
Node, can all produce corresponding intermediate data, and the intermediate data of generation needs to carry out landing processing,
Because intermediate data is used to rely on as the upstream data of next processing node, next data section
Point needs the intermediate data loaded after upper back end landing to be inputted as it.
In conventional data analysis, two processing nodes carry out chain by the intermediate data of landing up and down
Connect, data are often accomplished by doing a data loading and landed by a processing node, for magnanimity
For the data analysis process of data or complexity, the data volume of intermediate data is huge, does not only result in
Computing resource, the wasting of resources of input and output (IO), and data loading and landing also can be serious
Influence the efficiency of data analysis.
The content of the invention
The present invention provides a kind of data analysing method and device, for solving number in available data analysis
It is accomplished by doing a data loading and lands by a processing node according to every, resource expense is not only resulted in,
And the problem of the efficiency of influence data analysis.
To achieve these goals, the invention provides a kind of data analysing method, including：
Build and the corresponding processing model of node is respectively handled in data analysis flow；
Each processing model is sequentially connected generation processing module according to dependence；
Pending initial data is handled using the processing model after connection to obtain number of targets
According to.
To achieve these goals, the invention provides a kind of data analysis set-up, including：
Module is built, the corresponding processing model of node is respectively handled in data analysis flow for building；
Link block, for each processing model to be attached according to dependence；
Processing module, for using the processing model after connection pending initial data is carried out with
Handle to obtain target data.
Data analysing method and device that the present invention is provided, by building in data analysis flow everywhere
The corresponding processing model of node is managed, each processing model is sequentially connected according to dependence, using even
Processing model after connecing is handled pending initial data to obtain target data.The present invention
In each processing model be directly connected to according to dependence, the output of upper processing model can be direct
It is input in next processing model, intermediate data is no longer landed, and saves resource, and save
Slightly the loading procedure of intermediate data, improves data analysis efficiency.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the data analysing method of the embodiment of the present invention one；
Fig. 2 is the schematic flow sheet of the data analysing method of the embodiment of the present invention two；
Fig. 3 schemes for the DAG of the embodiment of the present invention two；
Fig. 4 is one of application example schematic diagram of data analysing method of the embodiment of the present invention two；
Fig. 5 is the schematic flow sheet of the data analysing method of the embodiment of the present invention three；
Fig. 6 is the structural representation of the data analysis set-up of the embodiment of the present invention four；
Fig. 7 is the structural representation of the data analysis set-up of the embodiment of the present invention five.
Embodiment
Below in conjunction with the accompanying drawings to evaluation index acquisition methods provided in an embodiment of the present invention and device
It is described in detail.
Embodiment one
As shown in figure 1, its schematic flow sheet for the data analysing method of the embodiment of the present invention one.
The data analysing method comprises the following steps：
It is each in S101, structure data analysis flow to handle the corresponding processing model of node.
First, the data analysis flow to setting is analyzed, and is obtained in each data analysis flow
Each processing node, can be according to each function of handling node, structure after each processing node is got
Build each processing node and handle model accordingly.
For example, include in the data analysis flow feature smooth, feature normalization, feature extraction,
Feature selecting etc. handles node, and these processing nodes have specific processing function, these processing sections
Point can be handled the data of input to obtain a corresponding result, and this result is in data
It is exactly intermediate data in analysis process.Such as feature normalization is used to initial data being based on each spy
The average and standard deviation levied are normalized, and the data after normalized are exactly that this feature is returned
One intermediate data changed.In the present embodiment, in order to avoid producing intermediate data, by feature normalization
This processing node is modeled, and feature normalization processing model has data to the data of input
Translation function, the processing model is able to record that each characteristic mean and standard deviation, can be to original number
According to being changed.
S102, by it is each processing model be attached according to dependence.
When analyzing data analysis process, it is necessary to get the dependence of each processing model,
When construct it is each processing node it is corresponding processing model after, according to processing node between dependence,
Each processing model is connected in an orderly manner successively.In order that respectively processing model can be directly connected to, need
It is provided with data-interface, the present embodiment, the data-interface of each processing model is unified, warp
Cross data-interface according to dependence by it is each processing model be sequentially connected with after, data analysis flow
An orderly execution logic can be just converted into.
S103, pending initial data is handled to obtain using the processing model after connection
Target data.
After the connection of each processing model, execution logic in an orderly manner is possible to, can will be pending
Initial data is input in each processing model after connection, and initial data is initially entered patrols in execution
In the processing model for collecting top, then the data after processing model sequentially enter next processing
Model, is in the processing model of execution logic afterbody, the processing model is finally defeated until entering
The data gone out are exactly target data.
The data analysing method that the present embodiment is provided, processing model is connected according to dependence, on
The output of one processing model directly can be input in next processing model by data-interface,
Intermediate data is no longer landed, and saves resource, and due in upper one processing model generation
Between data be directly entered next processing model, it is to avoid the loading procedure of intermediate data, improve
Data analysis efficiency.
Embodiment two
As shown in Fig. 2 its schematic flow sheet for the data analysing method of the embodiment of the present invention two.
The data analysing method comprises the following steps：
S201, obtain data analysis flow without the oriented DAG in loop figures.
Data analysis flow is made up of a series of processing node, and data analysis process is carried out
Signature analysis, can get the data analysis flow without loop digraph (Directed Acyclic
Graph, abbreviation DAG scheme), DAG figures can connect out a series of orderly processing nodes.
S202, parsing DAG figures obtain the relevant information of data analysis flow.
Wherein, the relevant information includes：Handle node logic function, processing node between
Dependence and each processing model storage address.
The relevant information for carrying out being analyzed and acquired by the data analysis flow is schemed to DAG, wherein, the phase
Close information include the logic function for the processing node that data analysis flow includes, handle node it
Between dependence and each processing model storage address.Input data can also be included in relevant information
Information, output data information and user configuring parameter etc..These relevant informations can generate one
Extensible markup language (Extensible Markup Language, abbreviation with node dependence
XML) file, the XML file is saved in DB Backup and backstage is submitted to.
S203, the corresponding place of logic function structure according to each processing node in the relevant information
Manage model.
After the logic function of each processing node is got, according to the logic function structure of processing node
Build corresponding processing model.For example, a data zooming processing node is used to will be greater than setting range
Data reduced, the data less than preset range are amplified, according to this processing node
Logic function can just build corresponding data zooming model.
S204, according to the dependence handled in the relevant information between node by each processing model
Connected by data-interface.
After the corresponding processing model of each processing node is generated, saved according to being handled in the relevant information
Dependence between point, each processing model is directly connected to by data-interface.Specifically, after
Platform, which is received, to be illustrated to DAG after the XML file that analysis is obtained, and can get analysis DAG figures
Dependence between middle processing node.Backstage is according to the dependence between processing node to everywhere
Reason model program in machine code assemble automatically, i.e., backstage according to processing node between dependence to each
The program in machine code for handling model carries out DAGization, and the program in machine code after assembling is preserved and is compiled into can
Operating file.Designed data-interface is then based on, by the code of each processing module successively
After the completion of assembling, composition, each processing model is initialized.In order to realize processing model
It is directly connected to, unitized processing has been carried out to data-interface, so as to easily a series of
Code combination gets up, after being connected according to dependence, and data analysis flow is converted into program
One orderly execution logic of aspect.
Generally, reconfiguring for program in machine code can introduce new bugs (bug),
Risk is larger when causing to dispose, and needs to be tested again, causes the repetition of resource.
In the present embodiment, data analysis flow carry out DAGization in code aspect, journey can be reduced
The quantity of sequence defect, can integration by each processing model can operation code program pack direct portion
Environment on line is affixed one's name to, this will be significantly reduced the risk disposed on line.
S205, according to each processing model storage address in the relevant information each processing model is carried out
Landing storage.
, can be according to correlation in order to avoid computing repeatedly for processing model after generation processing model
The storage address of each processing model in information, landing storage, raising processing are carried out by each processing model
The reusability of model.In practical application, the big small-scale far smaller than intermediate data of processing model
Size, can not only save resource, and be conducive to the efficiency of data analysis.
S206, by pending initial data be input to connection after processing model in handled with
Obtain target data.
After processing model is attached by generation, pending initial data is inputted, passed through
The processing of processing model after connection, obtains final target data.In the present embodiment, to original
The process that data are handled is completed in internal memory, and then without the centre of each processing model of landing
Data.
In order to more fully understand the data analysing method of above-mentioned the present embodiment offer, citing below is carried out
Explanation：
Data analysis flow to initial data includes following processing node：Data standard, data contracting
Put and data smoothing.The data analysis flow is analyzed, the DAG of the data analysis flow is obtained
Figure, as shown in figure 3, the data of each processing node output are intermediate data in the DAG figures,
The intermediate data of such as data zooming processing node output is data after scaling, data smoothing processing section
The intermediate data of point output is smooth rear data.
DAG figures are parsed, can be got in the data analysis flow between each processing node
Relevant information, wherein relevant information includes：Handle node logic function, processing node it
Between dependence and each processing model storage address.In this example, between processing node
Dependence is：Data standard relies on data smoothing, and data smoothing relies on data zooming.
In order to no longer produce intermediate data in data analysis process, it is to avoid the landing of intermediate data and
Loading, is each processing node structure according to the logic function of each processing node in data analysis flow
Corresponding processing model is built, these processing models have corresponding data converting function.Specifically include：
Data zooming model, data smoothing model and data normative model.Further, saved according to processing
Dependence between point, each processing model is connected by data-interface, as shown in Figure 4.
After it will handle model connection, an orderly execution logic is formd, initial data is input to
In processing model after connection, the execution logic that the processing model after so above-mentioned connection is constituted just is opened
Beginning is handled initial data, obtains final target data.Each processing model is to initial data
Processing procedure can be completed in internal memory, it is to avoid produce intermediate data.It is possible to further incite somebody to action
These processing models built are landed, and can be stored according to the storage address of user configuring,
In order to the multiplexing of these processing models.
The data analysing method that the present embodiment is provided, is schemed by the DAG for obtaining data analysis flow,
DAG figures are parsed, each processing model is built according to analysis result, and by each processing model
Connected according to dependence, using the processing model after connection to pending initial data at
Manage to obtain target data.It is directly connected in the present embodiment between processing model, upper processing mould
The output of type can be directly inputted in next processing model, and intermediate data is no longer landed,
Resource is saved, and omits the loading procedure of intermediate data, data analysis efficiency is improved.
Embodiment three
As shown in figure 5, its schematic flow sheet for the data analysing method of the embodiment of the present invention three.
On the basis of above-described embodiment, pending initial data is entered using the processing model after connection
Row processing is comprised the following steps with obtaining target data：
S301, to initial data carry out data check.
, it is necessary to carry out data check to initial data after initial data is got, detection first is used
Whether family has corresponding processing model, if being stored with corresponding processing model, judges that this is original
Whether data did not did change, specifically, input data information and output in relevant information
Data message, if initial data did not did change, illustrating need not be to the processing model that has stored
It is updated, it is only necessary to be directly obtained each processing model of storage, then execution step S302, no
Then perform step S303.
S302, when initial data is by data check, according to each processing mould in the relevant information
The storage address of type obtains each processing model.
When initial data is by data check, illustrating the processing model of storage need not update, then
Each processing model can be got according to the storage address of each processing model in relevant information
S303, when initial data is not by data check, rebuild each processing model.
When carrying out data check to initial data, when judging that initial data did change, need
Each processing model of storage is updated, if being not detected by the processing model stored,
Data analysis flow to initial data is analyzed, and builds corresponding processing model.
S304, by it is each processing model be attached according to dependence.
Each processing model is attached according to the dependence between processing node, forming one has
Sequence ground execution logic.
S305, initial data is entered into row format it is converted to input data.
It is raw in order to ensure the uniformity of data-interface, it is necessary to be changed to initial data mechanical energy form
Into the input data of uniform format, in the present embodiment, initial data is uniformly converted into vectorial (Vector)
Or the input data of matrix (Matrix) form.
S306, by input data be input to connection after processing model in obtain target data.
Processing model is attached according to dependence, an orderly execution logic is formd,
Initial data is input in the processing model after connection, the so above-mentioned processing model structure by connecting
Into execution logic begin to handle initial data, obtain final target data.
S307, according to default compliance test result condition to target data carry out compliance test result.
User can preset certain compliance test result condition according to the demand of itself, according to setting
Fixed compliance test result condition is verified to the treatment effect of target data.For example there is provided number
According to Contrast on effect before and after the processing, user can be intuitive to see very much data before treatment and
Contrast on effect after processing.
Example IV
As shown in fig. 6, its structural representation for the data analysis set-up of the embodiment of the present invention four.
The data analysis set-up includes：Build module 11, link block 12 and processing module 13.
Wherein, module 11 is built, the corresponding place of node is respectively handled in data analysis flow for building
Manage model.
Link block 12, for each processing model to be attached according to dependence.
Processing module 13, for being carried out using the processing model after connection to pending initial data
Handle to obtain target data.
The data analysis set-up that the present embodiment is provided, processing model is connected according to dependence, on
The output of one processing model directly can be input in next processing model by data-interface,
Intermediate data is no longer landed, and saves resource, and due in upper one processing model generation
Between data be directly entered next processing model, it is to avoid the loading procedure of intermediate data, improve
Data analysis efficiency.
Embodiment five
As shown in fig. 7, its structural representation for the data analysis set-up of the embodiment of the present invention five.
The data analysis set-up is except including the structure module 11 in above-described embodiment four, the and of link block 12
Outside processing module 13, in addition to：Acquisition module 14, parsing module 15, compliance test result module
16 and landing module 17.
Acquisition module 14, for obtaining scheming without the oriented DAG in loop for the data analysis flow；
Parsing module 15, the relevant information of data analysis flow is obtained for parsing the DAG figures.
Wherein, the relevant information includes：Handle node logic function, processing node between
Dependence and each processing model storage address.
A kind of optional frame mode of processing module includes in the present embodiment：Data check unit 131,
Acquiring unit 132, format conversion unit 133 and processing unit 134.
Wherein, data check unit 131, for carrying out data check to the initial data.
Acquiring unit 132, for when initial data is by verifying according in the relevant information everywhere
The storage address for managing model obtains each processing model.
Format conversion unit 133, input data is converted to for the initial data to be entered into row format.
Processing unit 134, for being sequentially inputted to input data to carry out in the processing model after connection
Handle to obtain the target data.
Further, data analysis set-up also includes：Compliance test result module 16.
Compliance test result module 16, for being imitated according to default compliance test result condition to target data
Fruit is verified.
Module 11 is built, specifically for being patrolled according to each processing node in the relevant information
Collect the corresponding processing model of formation function.
Link block 12, specifically for being closed according to the dependence handled in the relevant information between node
System connects each processing model by data-interface.
Further, data analysis set-up also includes：Land module 17.
Module 17 is landed, for according to respectively processing model storage address will everywhere in the relevant information
Manage model and carry out landing storage.
In the present embodiment, schemed by the DAG for obtaining data analysis flow, DAG figures are solved
Analysis, builds each processing model, and each processing model is connected according to dependence according to analysis result
Connect, pending initial data is handled using the processing model after connection to obtain number of targets
According to.It is directly connected in the present embodiment between processing model, the output of upper processing model can be straight
Connect and be input in next processing model, intermediate data is no longer landed, save resource, and
The loading procedure of intermediate data is omitted, data analysis efficiency is improved.
It is possible to further which these processing models of structure are landed, it can be matched somebody with somebody according to user
The storage address put is stored, in order to the multiplexing of these processing models.
One of ordinary skill in the art will appreciate that：Realize the whole of above-mentioned each method embodiment
Or part steps can be completed by the related hardware of programmed instruction.Foregoing program can be with
It is stored in a computer read/write memory medium.Upon execution, execution includes the program
The step of stating each method embodiment；And foregoing storage medium includes：ROM, RAM, magnetic
Dish or CD etc. are various can be with the medium of store program codes.
Finally it should be noted that：Various embodiments above is merely illustrative of the technical solution of the present invention,
Rather than its limitations；Although the present invention is described in detail with reference to foregoing embodiments,
It will be understood by those within the art that：It can still be remembered to foregoing embodiments
The technical scheme of load is modified, or which part or all technical characteristic are carried out etc.
With replacement；And these modifications or replacement, the essence of appropriate technical solution is departed from this
Invent the scope of each embodiment technical scheme.
Claims (14)
1. a kind of data analysing method, it is characterised in that including：
Build and the corresponding processing model of node is respectively handled in data analysis flow；
Each processing model is attached according to the dependence between processing node；
Pending initial data is handled using the processing model after connection to obtain number of targets
According to.
2. according to the method described in claim 1, it is characterised in that the structure data analysis stream
In journey before the processing model of each processing node, including：
Obtain scheming without the oriented DAG in loop for the data analysis flow；
Parse the relevant information that the DAG figures obtain the data analysis flow；
Wherein, the relevant information includes：Handle node logic function, processing node between
Dependence and each processing model storage address.
3. method according to claim 2, it is characterised in that the place using after connection
Reason model is handled pending initial data to obtain target data, including：
Data check is carried out to the initial data；
When initial data is by data check, depositing for model is handled according to each in the relevant information
Storage address acquisition respectively handles model；
The initial data is entered into row format and is converted to input data；
The input data is input in the processing model after connection and handled to obtain the mesh
Mark data.
4. method according to claim 3, it is characterised in that described by the input data
It is input to after obtaining the target data in the processing model after connection, in addition to：
Compliance test result is carried out to target data according to default compliance test result condition.
5. the method according to claim any one of 1-4, it is characterised in that the structure number
According to the corresponding processing model of each processing node in analysis process, including：
According to the logic function structure of each processing node in the relevant information is corresponding
Handle model.
6. the method according to claim any one of 1-4, it is characterised in that it is described will everywhere
Reason model is attached according to the dependence between processing node, including：
Each processing model is passed through by number according to the dependence handled in the relevant information between node
Connected according to interface.
7. the method according to claim any one of 1-4, it is characterised in that the structure number
After the corresponding processing model of each processing node in analysis process, including：
Each processing model is carried out into landing according to each processing model storage address in the relevant information to deposit
Storage.
8. a kind of data analysis set-up, it is characterised in that including：
Module is built, the corresponding processing model of node is respectively handled in data analysis flow for building；
Link block, for each processing model to be attached according to dependence；
Processing module, at using the processing model after connection to pending initial data
Manage to obtain target data.
9. device according to claim 8, it is characterised in that also include：
Acquisition module, for obtaining scheming without the oriented DAG in loop for the data analysis flow；
Parsing module, the relevant information of the data analysis flow is obtained for parsing the DAG figures；
Wherein, the relevant information includes：Handle node logic function, processing node between
Dependence and each processing model storage address.
10. device according to claim 9, it is characterised in that the processing module includes：
Data check unit, for carrying out data check to the initial data；
Acquiring unit, for when initial data is by verifying according to respectively being handled in the relevant information
The storage address of model obtains each processing model；
Format conversion unit, input data is converted to for the initial data to be entered into row format；
Processing unit, for the input data to be sequentially inputted to enter in the processing model after connection
Row handles to obtain the target data.
11. device according to claim 10, it is characterised in that also include：
Compliance test result module, for carrying out effect to target data according to default compliance test result condition
Checking.
12. the device according to claim any one of 8-11, it is characterised in that the structure
Module, specifically for being built according to the logic function of each processing node in the relevant information
The corresponding processing model.
13. the device according to claim any one of 8-11, it is characterised in that described by respectively
Processing model is attached according to the dependence between processing model, including：
Each processing model is passed through by number according to the dependence handled in the relevant information between node
Connected according to interface.
14. the device according to claim any one of 8-11, it is characterised in that also include：
Land module, for according to each processing model storage address in the relevant information by each processing
Model carries out landing storage.
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| CN201610180084.0A CN107229815A (en) | 2016-03-25 | 2016-03-25 | Data analysing method and device |
| TW106105358A TWI798170B (en) | 2016-03-25 | 2017-02-17 | Data analysis method and device |
| PCT/CN2017/076876 WO2017162085A1 (en) | 2016-03-25 | 2017-03-16 | Data analysis method and apparatus |
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| CN201610180084.0A CN107229815A (en) | 2016-03-25 | 2016-03-25 | Data analysing method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111290948A (en) * | 2020-01-19 | 2020-06-16 | 腾讯科技（深圳）有限公司 | Test data acquisition method and device, computer equipment and readable storage medium |
| CN111290948B (en) * | 2020-01-19 | 2022-02-22 | 腾讯科技（深圳）有限公司 | Test data acquisition method and device, computer equipment and readable storage medium |
| CN112231378A (en) * | 2020-10-13 | 2021-01-15 | 中移（杭州）信息技术有限公司 | Data processing method, system, server and storage medium |
Also Published As
| Publication number | Publication date |
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| TW201734843A (en) | 2017-10-01 |
| WO2017162085A1 (en) | 2017-09-28 |
| TWI798170B (en) | 2023-04-11 |
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