CN105956077B - Based on the matched digging flow system of semantic requirement - Google Patents
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Abstract
One kind being based on the matched digging flow system of semantic requirement, it include: platform base unit, algorithm groupware unit and model visualization unit with algorithm groupware, wherein: platform base unit receives demand text and user journal data, and the algorithm groupware in algorithm groupware unit is called to carry out digging flow, model visualization unit is transmitted to after the Result of platform base unit receiving algorithm bound cell to show Result, the present invention matches demand text and the algorithm groupware description of user, realize the automation of plug-in unit selection course, the dynamic integrity of algorithm groupware collection improves the flexibility of system in design, it can ensure the effective use of computing resource and the orderly execution of mining task.
Description
Technical field
It is specifically a kind of to be based on the matched process of semantic requirement the present invention relates to a kind of technology in digging flow field
Digging system.
Background technique
Information system is widely used enterprise's production, running, flow monitoring and optimization in fields such as manufacturing industry, to enterprise
The business activity of industry provides effective management and supports.Information system produces a large amount of journal file, energy in the process of running
Enough distribution for directly reflecting task practical operation situation and resource in operation flow.Digging flow is as in Business Process Management
Important supplementary means correct system for checking and improving existing procedural model from the journal file that information system generates
Unite building process in subjectivity, analysis enterprise production and operation resource utilization, promote corporate process optimization and
Scheduling of resource.
The existing process model restorative procedure based on Petri network basic structure, mainly by legacy data
Reason, becomes the event log for meeting specification；It is used for conclusion mining algorithm later and excavates corresponding process model；It is logical
The process model that the event log that crossing will be enlarged by is obtained with excavation is calibrated, deviation present in discovery process simulation model；Finally
Propose the recovery scenario of process model under different structure, it is intended to which repair process model enhances the consistency of process model.But this
A little technologies only use a kind of algorithm, and the discovery of Petri model and the model calibration of event log are completed from control flow angle,
Itself do not have analysis and matches the ability of user demand.
The written DTMiner:A Tool for Decision MakingBased on of Josue Obregon
Proposed in Historical Process Data it is a kind of according to history flow data from tissue and time Perspective Analysis process simultaneously
The method of aid decision judgement.But this method is to organize using being deployed in stand-alone environment, to need based on visual angle and time visual angle
User builds in client completion；Log analysis process only relates to single algorithm simultaneously, can not integrate according to the demand of user
Multiple visual angles are analyzed, and the update and maintenance of algorithm are unfavorable for, while reducing the ease for use of product.
Summary of the invention
The present invention In view of the above shortcomings of the prior art, proposes a kind of based on the matched digging flow system of semantic requirement
System.
The present invention is achieved by the following technical solutions:
The present invention includes: platform base unit, algorithm groupware unit and model visualization unit with algorithm groupware,
Wherein: platform base unit receives demand text and user journal data, and call algorithm groupware in algorithm groupware unit into
Row digging flow is transmitted to model visualization unit to show after the Result of platform base unit receiving algorithm bound cell
Result.
It include data format, field format, desired output model and input/output argument in the demand text.
The platform base unit includes: demand matching module, excavates module and log processing module, in which: demand
Matching module receives demand text generation and plug set merging is called to be transferred to excavation module, and log processing module receives user journal
Data product process log object is simultaneously transferred to excavation module.
The demand matching module according to digging flow ontologies and plug-in unit describe text set carry out it is semantic-based
It is generated after the matching of plug-in unit demand and calls plug-in unit set.
The log processing module parses product process log object through log integrity and log.
The excavation module includes transferring the algorithm groupware scheduler of each algorithm groupware.
The algorithm groupware includes Petri net model discovery algorithm groupware, social networks discovery algorithm groupware and process
Log statistic plug-in unit.
The model visualization unit shows Result using visualization component D3.js and Highcharts.
Technical effect
Compared with prior art, the present invention matches demand text and the algorithm groupware description of user, realizes slotting
The automation of part selection course, the dynamic integrity of algorithm groupware collection improve the flexibility of system in design, it can be ensured that meter
Calculate the effective use of resource and the orderly execution of mining task.
Detailed description of the invention
Fig. 1 is structure of the invention flow diagram.
Specific embodiment
It elaborates below to the embodiment of the present invention, the present embodiment carries out under the premise of the technical scheme of the present invention
Implement, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to following implementation
Example.
Embodiment 1
As shown in Figure 1, the present embodiment include: platform base unit, the algorithm groupware unit including several algorithm groupwares with
And model visualization unit, in which: platform base unit receives demand text and user journal data, and calls algorithm groupware list
Algorithm groupware in member carries out digging flow, is transmitted to model after the Result of platform base unit receiving algorithm bound cell
Visualization is to show Result.
The platform base unit is the input interface and optimized integration of whole system framework, including three modules,
It is demand matching module, log processing module and excavation module respectively.
The demand matching module receives user demand, and user demand is included in demand text, wraps in demand text
Data format, field format, desired output model and input/output argument are included, further includes input journal description, desired output
As a result it describes.Later, it is labeled according to existing digging flow ontologies (OWL), is retouched in combination with predefined plug-in unit
Text set is stated, it includes input parameter and output result description which, which describes text set, completes semantic-based matching to solve
Certainly demand text and plug-in unit describe the inconsistent of text set statement, ultimately generate and call plug-in unit set.The digging flow knowledge sheet
Body is that the mode modeled in conjunction with DBPedia and domain expert describes the relevant some terms of digging flow and art in the form of OWL
Relationship between language.
The log processing module is able to carry out log upload, transmits user journal data by HTTP or FTP, and will
The user journal data received carry out the operations such as log integrity, including removal noise, format transformation and field correspondence, most
It is parsed afterwards by log by the accessible process log object of processing result generating algorithm plug-in unit and is transferred to excavation module.
The stream for calling plug-in unit set and log processing module to generate that the excavation module is generated with demand matching module
Journey log object is as input.It excavates and is equipped with algorithm groupware scheduler in module, can be closed according to the dependence between algorithm groupware
System generates scheduling process, and completes the orderly scheduling to algorithm groupware, and the dynamic call process of algorithm groupware is by unification
RESTful interface, which calls, to be realized, the Result of scheduled algorithm groupware is received by excavation module and completes Result remittance
Always, model visualization unit is provided.
The algorithm groupware unit is the core processing unit of whole system framework, including all kinds of digging flow algorithms are patrolled
The specific implementation collected mainly includes three algorithm groupwares, is Petri net model discovery algorithm groupware, social networks discovery respectively
Algorithm groupware and process log count plug-in unit.
The Petri net model discovery algorithm groupware realizes the Petri net model discovery algorithm in digging flow field
That is α+algorithm completes the foundation of task nexus in process log object by the building of track matrix, and simultaneously based on multithreading
The Petri net model that the method for hair completes task based access control relationship generates.
The social networks discovery algorithm that the social networks discovery algorithm groupware realizes digging flow field works
Handover relationship discovery algorithm is closed using the parameter preset of user as design conditions by task participant in process log object
It is the foundation of matrix, generates the social network diagram based on work handover relationship.
The process log statistics plug-in unit realizes the statistical function to input process log object.By to input day
Different dimensions in will object, such as task participant, the statistics for executing the time, executing task dispatching, generate the statistics report of process log
It accuses, including in task execution frequency statistics, process log time series analysis, task participant execution mission frequency statistics etc.
Hold.
The model visualization unit is the output interface of whole system framework, receives from platform base unit and excavates knot
Fruit simultaneously carries out visual presentation and result export, which realizes digraph using visualization component D3.js and Highcharts
Model, Petri network and social network diagram and statistical result, such as the display and export of line chart Result.
It is parsed after the user demand of demand textual form of the demand matching module to getting, with reference to existing
Digging flow ontologies demand text is labeled, and in conjunction with plug-in unit describe text and carry out semantic-based matching to count
It calculates, calls plug-in unit set to generate.Log processing module handles log and uploads, and obtains user journal data, and complete log
Pretreatment and log resolving, ultimately generate process log object.Excavate the calling plug-in unit that module is generated according to preamble module
Set and process log object complete the calling of actual algorithm plug-in unit by algorithm groupware scheduler based on dependence between plug-in unit
And execution.Each algorithm groupware in algorithm groupware unit is described according to the plug-in unit of itself, for the transmission of algorithm groupware scheduler
Process log object and the computing resource of distribution complete mining process, and result is fed back to excavation module.Module is excavated to obtain
The Result of all called plug-in units simultaneously carries out result and summarizes, and is finally sent to model visualization unit in unified form.
Model visualization unit selects different visualization components to realize Result according to the data characteristics of the Result received
Output i.e. show and export.Platform base unit realizes the solution between algorithm groupware unit and user in the entire system
Coupling enables algorithm groupware unit to realize in the case of not influencing user's use and increases the dynamic of algorithm groupware, modifies
And deletion.
Compared with prior art, the present invention understands user demand accurately and comprehensive, can adjust system to dynamically transparent
Integrated algorithm groupware improves scalability, maintainability, ease for use and the fault-tolerant ability of system, reduces making for user
Use threshold.
Claims (1)
1. one kind is based on the matched digging flow system of semantic requirement characterized by comprising platform base unit has and calculates
The algorithm groupware unit and model visualization unit of method plug-in unit, in which: platform base unit receives demand text and user day
Will data, and the algorithm groupware in algorithm groupware unit is called to carry out digging flow, platform base unit receiving algorithm plug-in unit list
Model visualization unit is transmitted to after the Result of member to show Result；
It include data format, field format, desired output model and input/output argument in the demand text；
The platform base unit realizes the decoupling between algorithm groupware unit and user in the entire system, so that algorithm
Bound cell can be realized in the case of not influencing user's use to be increased, modify and deletes to the dynamic of algorithm groupware, this is flat
Platform base unit includes: demand matching module, excavates module and log processing module, in which: demand matching module receives demand
Text generation calls plug set merging to be transferred to excavation module, and log processing module receives the product process log of user journal data
Object is simultaneously transferred to excavation module；
The demand matching module describes text set according to digging flow ontologies and plug-in unit and carries out semantic-based plug-in unit
It is generated after demand matching and calls plug-in unit set, specifically: the user of demand textual form of the demand matching module to getting needs
It is parsed after asking, demand text is labeled with reference to existing digging flow ontologies, and describe text in conjunction with plug-in unit
Semantic-based matching primitives are carried out, call plug-in unit set to generate；
The log processing module parses product process log object through log integrity and log, specially acquisition user day
Will data, and log integrity and log resolving are completed, ultimately generate process log object；
The excavation module includes transferring the algorithm groupware scheduler of each algorithm groupware, is specially generated according to preamble module
Calling plug-in unit set and process log object, by algorithm groupware scheduler be based on plug-in unit between dependence complete actual algorithm
The calling and execution of plug-in unit, each algorithm groupware in algorithm groupware unit is described according to the plug-in unit of itself, for algorithm groupware tune
The computing resource of the process log object and distribution of spending device transmission completes mining process, and result feedback is dug to module is excavated
Pick module, which obtains the Result of all called plug-in units and carries out result, to summarize, and being finally sent to model in unified form can
Depending on changing unit；
The algorithm groupware unit includes Petri net model discovery algorithm groupware, social networks discovery algorithm groupware and process
Log statistic plug-in unit；
The model visualization unit shows Result using visualization component D3.js and Highcharts, specifically: root
According to the data characteristics of the Result received, different visualization components is selected to realize that the output of Result shows and leads
Out.
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| CN108255802B (en) * | 2016-12-29 | 2021-08-24 | 北京国双科技有限公司 | Universal text parsing architecture and method and device for parsing text based on architecture |
| CN106599325A (en) * | 2017-01-18 | 2017-04-26 | 河海大学 | Method for constructing data mining visualization platform based on R and HighCharts |
| CN109192317B (en) * | 2018-07-17 | 2022-08-30 | 山东协力合智通信科技有限公司 | Process model correction method of cyclic concurrency structure based on logic Petri net |
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Effective date of registration: 20210128 Address after: Room 309, building 1, 563 Songtao Road, China (Shanghai) pilot Free Trade Zone, Pudong New Area, Shanghai, 201203 Patentee after: SHANGHAI LANDFUN INFORMATION TECHNOLOGY Co.,Ltd. Address before: 200240 No. 800, Dongchuan Road, Shanghai, Minhang District Patentee before: SHANGHAI JIAO TONG University |