CN103853821A - Method for constructing multiuser collaboration oriented data mining platform - Google Patents
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Abstract
The invention discloses a method for constructing a multiuser collaboration oriented data mining platform. According to the method, flexible workflow and a multiuser collaboration mechanism are integrated, a working space oriented to the collaborated data mining of three kinds of user roles, namely data acquisition staff, data analysis staff and result auditing staff, is provided, and the whole work flow is realized by components comprising a data acquisition component, a data preprocessing component, a data modeling component, a result visualization display component and a model evaluation component. The flexible workflow formed by the components and arrows can be created and manipulated by different use roles in different user views in a drag-and-drop manner. Aiming at the complexity, namely continuous repeating, continuous revising and continuous iterating, of data mining, the method has the advantages that data mining work can be greatly simplified, and data can be prevented from leaking so as to guarantee the safety of the data.
Description
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Technical field
The construction method that the present invention relates to the data mining platform of a kind of integrated resiliency workflow, facing multiple users cooperation, belongs to data mining technology field.
Background technology
Data mining (data mining) is a kind of from the historical business datum of magnanimity, sees through mathematical analysis pattern and extracts the process of containing in potential information wherein.Data mining is the process of a continuous repetition, constantly amendment, continuous iteration, mainly comprises: the flow processs such as data acquisition, data pre-service, data analysis, result visualization demonstration and model evaluation.At present, data mining is widely used at commercial fields such as bank, telecommunications, insurance, traffic, retails.
, there is following problem in existing data mining platform: lack revocable, can reform, preservable elasticity user workspace, user must be settled at one go in the time carrying out data mining, bring inconvenience; Lack can revise, can iteration, procedure member that can intermediate result output, make user can not understand well and handle its data analysis process; Towards the excavation mechanism at alone family, make user set data collector, data analyst, three roles of result audit crew, cannot in whole analysis process, cooperate, also be easy to cause leaking of data and analysis result, cause problem of data safety.
Summary of the invention
Goal of the invention: for problems of the prior art, the invention provides a kind of construction method of the data mining platform that relates to elastic working stream, multi-user Cooperation.
The data mining platform building by the inventive method provide a kind of based on Web revocable, can reform, preservable elasticity user workspace.In user workspace, data acquisition personnel can upload, upgrade, delete data set; The data analysis flow process of oneself can be set up and handle to data analyst; Result audit crew can carry out examination and the reply of Result.
Technical scheme: a kind of construction method of data mining platform of facing multiple users cooperation, a kind of data-oriented collector, data analyst and three kinds of user roles of result audit crew work space that carries out data mining that cooperates is provided, whole workflow realizes with member, comprising: data acquisition member, data pre-service member, data modeling member, result visualization display member and model evaluation member.Different user roles uses different Users, can use the mode pulling to set up and operate the data analysis flow process of oneself, described data acquisition personnel carry out uploading of data by described data acquisition member, upgrade and deletion action, described data analyst is utilized data pre-service member successively by flow sequence, data modeling member, result visualization member and model evaluation member carry out data acquisition, data pre-service, modeling, the data analysis operations such as model evaluation, described result audit crew is examined and is given an written reply Result by described result visualization member in described user workspace.
Described user workspace is the pattern manipulation interface of a towed, comprise: prioritizing member district and flow process create two, district part, described prioritizing member district is the region of a series of type of extension data digging flow members of display, and it is that user sets up and the region of manipulation data analysis process that described flow process creates district.
Described data analysis flow process is that a kind of elastic working being made up of member and arrow flows.In any data analysis flow process, execution parameter, change flow that user can adjust on component node are at any time carried out direction and are derived the operations such as intermediate operations result.
Data mining platform construction method comprises the following steps;
Step 1: design and Implement data acquisition member.In following two kinds of situations, carry out data acquisition: image data and web upload mode image data in database.
Image data in database, is connected and is realized by Java database, and the data access of data mining platform is converted into corresponding data query in database in real time.
Web uploads mode image data, and by monitoring the data upload request of web client, the socket that sets up client and data storage server is connected, and re-uses Java I/O stream data set is written in the file system of data storage server.
In the time of two kinds of data acquisition member specific implementations, all need the metadata information of the corresponding data of data set to be saved in the database of system, and unified access interface is externally provided.
Step 2: design and Implement data pre-service member.By R language, data set is carried out to statistical study, the basic descriptor in the mode of figure to user's demonstrating data collection; The mathematical method of removal and data correction is filled up, recorded to encapsulation interpolation, and the user interface of the data pre-service links such as deal with data missing values, processing repeating data, processing noise data and processing abnormal data is provided.
Step 3: design and Implement data modeling member.By data mining models such as R language encapsulation classification, cluster, association and time serieses; Provide graphical interfaces interface, to user, corresponding model analysis parameter is set.
Step 4: design and Implement result visualization display member.By R language, data mining results and model evaluation result are represented to user in the mode such as figure, list; Be pushed to result audit crew during by fructufy by Ajax polling technique.
Step 5: design and Implement model evaluation member.By using R language that the multiple model evaluation methods such as accurate rate, error rate and confusion matrix are provided; The user interface that model analysis parameter and model element data message is saved in to system database is provided.
Step 6: design and Implement user workspace.Realize the pattern manipulation interface of a towed by JQuery, comprise that member prioritizing member district and flow process create two, district part; Store User operation log by stack data structures, the user interface of cancelling, reforming and save workspace is provided.
Step 7: define and realize data digging flow.The data mining member designing taking step 1 to step 5 is node, the workflow that definition is made up of several nodes and arrow; Provide knot modification execution parameter, change flow to carry out direction and derive the user interfaces such as intermediate operations result.
Step 8: integrated and deployment Mining Platform.The data mining member that step 1 to step 5 is designed provides the configuration interface of JSON form, and the mode that provides to edit configuration file customizes the user interface of the function of Mining Platform.
The present invention adopts technique scheme, has following beneficial effect: for the complicacy of the continuous repetition of data mining, constantly amendment, continuous iteration, provide a kind of elastic data excacation space of facing multiple users cooperation.Not only reduced data excacation greatly, can also prevent leaking of data, ensures the security of data.
Brief description of the drawings
Fig. 1 is the structural principle block diagram of the facing multiple users data mining platform of the embodiment of the present invention.
Embodiment
Below in conjunction with specific embodiment, further illustrate the present invention, should understand these embodiment is only not used in and limits the scope of the invention for the present invention is described, after having read the present invention, those skilled in the art all fall within the application's claims limited range to the amendment of the various equivalent form of values of the present invention.
In the embodiment of the present invention, data mining platform construction method comprises the following steps;
Step 1: design and Implement data acquisition member.For the large quantification (volume) of data set, the complex characteristics such as variation (variety) and rapid (velocity), are divided into following two kinds of situation specific implementations: image data and web upload mode image data in database.
Image data in database, connects (JDBC) by Java database and realizes, and the data access of data mining platform is converted into corresponding data query SQL in database in real time.
Web uploads mode image data, and by monitoring the data upload request of web client, the socket that sets up client and data storage server is connected, and re-uses Java I/O stream data set is written in the file system of data storage server.
In the time of two kinds of data acquisition member specific implementations, all need the metadata information of the corresponding data of data set to be saved in the database of system, and unified access interface is externally provided.
Step 2: design and Implement data pre-service member.By R language, data set is carried out to statistical study, the basic descriptor in the mode of figure to user's demonstrating data collection; The mathematical method of removal and data correction is filled up, recorded to encapsulation interpolation, and the user interface of the data pre-service links such as deal with data missing values, processing repeating data, processing noise data and processing abnormal data is provided.
Step 3: design and Implement data modeling member.By data mining models such as R language encapsulation classification, cluster, association and time serieses; Provide graphical interfaces interface, to user, corresponding model analysis parameter is set.
Step 4: design and Implement result visualization display member.By R language, data mining results and model evaluation result are represented to user in the mode such as figure, list; Be pushed to result audit crew during by fructufy by Ajax polling technique.
Step 5: design and Implement model evaluation member.By R language, the model establishing is before assessed; The user interface that model analysis parameter and model element data message is saved in to system database is provided.
Step 6: design and Implement user workspace.Realize the pattern manipulation interface of a towed by JQuery, comprise that member prioritizing member district and flow process create two, district part; Store User operation log by stack data structures, the user interface of cancelling, reforming and save workspace is provided.
Step 7: define and realize data digging flow.The data mining member designing taking step 1 to step 5 is node, the workflow that definition is made up of several nodes and arrow; Provide knot modification execution parameter, change flow to carry out direction and derive the user interfaces such as intermediate operations result.
Step 8: integrated and deployment Mining Platform.The data mining member that step 1 to step 5 is designed provides the configuration interface of JSON form, and the mode that provides to edit configuration file customizes the user interface of the function of Mining Platform.
As shown in Figure 1, data mining platform data-oriented collector, data analyst and the three kinds of user roles of result audit crew that the present invention relates to carry out collaboration data excavation, and a kind of user workspace of componentization is provided, comprises data acquisition member, data pre-service member, data modeling member, result visualization display member and model evaluation member.
Different user roles uses different Users, can use the mode pulling to set up and operate the data analysis flow process of oneself, data acquisition personnel carry out uploading of data by data acquisition member, upgrade and deletion action, described data analyst is utilized data pre-service member successively by flow sequence, data modeling member, result visualization member and model evaluation member carry out data acquisition, data pre-service, modeling, the data analysis operations such as model evaluation, result audit crew is examined and is given an written reply Result by result visualization member in user workspace.
Data analysis flow process is that a kind of elastic working being made up of member and arrow flows.In any data analysis flow process, execution parameter, change flow that user can adjust on component node are at any time carried out direction and are derived the operations such as intermediate operations result.
Claims (1)
1. the construction method of the data mining platform of a facing multiple users cooperation, it is characterized in that: a kind of data-oriented collector, data analyst and three kinds of user roles of result audit crew work space that carries out data mining that cooperates is provided, specifically comprises the following steps:
Step 1: design and Implement data acquisition member;
In following two kinds of situations, carry out data acquisition: image data and web upload mode image data in database;
Image data in database, is connected and is realized by Java database, and the data access of data mining platform is converted into corresponding data query in database in real time;
Web uploads mode image data, and by monitoring the data upload request of web client, the socket that sets up client and data storage server is connected, and re-uses Java I/O stream data set is written in the file system of data storage server;
In the time of two kinds of data acquisition member specific implementations, all need the metadata information of the corresponding data of data set to be saved in the database of system, and unified access interface is externally provided;
Step 2: design and Implement data pre-service member; By R language, data set is carried out to statistical study, the basic descriptor in the mode of figure to user's demonstrating data collection; The mathematical method of removal and data correction is filled up, recorded to encapsulation interpolation, and the user interface of the data pre-service links such as deal with data missing values, processing repeating data, processing noise data and processing abnormal data is provided;
Step 3: design and Implement data modeling member; By R language encapsulation classification, cluster, association and seasonal effect in time series data mining model; Provide graphical interfaces interface, to user, corresponding model analysis parameter is set;
Step 4: design and Implement result visualization display member; By R language, data mining results and model evaluation result are represented to user in the mode such as figure, list; Be pushed to result audit crew during by fructufy by Ajax polling technique;
Step 5: design and Implement model evaluation member; By using R language that the multiple model evaluation method of accurate rate, error rate and confusion matrix is provided; The user interface that model analysis parameter and model element data message is saved in to system database is provided;
Step 6: design and Implement user workspace; Realize the pattern manipulation interface of a towed by JQuery, comprise that member prioritizing member district and flow process create two, district part; Store User operation log by stack data structures, the user interface of cancelling, reforming and save workspace is provided;
Step 7: define and realize data digging flow; The data mining member designing taking step 1 to step 5 is node, the workflow that definition is made up of several nodes and arrow; Provide knot modification execution parameter, change flow to carry out direction and derive the user interfaces such as intermediate operations result;
Step 8: integrated and deployment Mining Platform; The data mining member that step 1 to step 5 is designed provides the configuration interface of JSON form, and the mode that provides to edit configuration file customizes the user interface of the function of Mining Platform.
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| CN104572929A (en) * | 2014-12-26 | 2015-04-29 | 深圳市科漫达智能管理科技有限公司 | Data mining method and device |
| CN104731953A (en) * | 2015-03-31 | 2015-06-24 | 河海大学 | R-based building method of data preprocessing system |
| CN105159688A (en) * | 2015-10-14 | 2015-12-16 | 浙江大学 | Programmable information visualization interaction type design method |
| CN105468736A (en) * | 2015-11-23 | 2016-04-06 | 国云科技股份有限公司 | Plug-in and component based data preprocessing system and realization method therefor |
| CN105550365A (en) * | 2016-01-15 | 2016-05-04 | 中国科学院自动化研究所 | Visualization analysis system based on text topic model |
| CN106446238A (en) * | 2016-10-10 | 2017-02-22 | 合肥红珊瑚软件服务有限公司 | Web data mining system based on XML |
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