CN105956077A - Process mining system based on semantic requirement matching - Google Patents
Process mining system based on semantic requirement matching Download PDFInfo
- Publication number
- CN105956077A CN105956077A CN201610280378.0A CN201610280378A CN105956077A CN 105956077 A CN105956077 A CN 105956077A CN 201610280378 A CN201610280378 A CN 201610280378A CN 105956077 A CN105956077 A CN 105956077A
- Authority
- CN
- China
- Prior art keywords
- unit
- algorithm
- system based
- module
- digging flow
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000008569 process Effects 0.000 title claims abstract description 40
- 238000005065 mining Methods 0.000 title abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 63
- 238000012800 visualization Methods 0.000 claims abstract description 17
- 230000008878 coupling Effects 0.000 claims description 15
- 238000010168 coupling process Methods 0.000 claims description 15
- 238000005859 coupling reaction Methods 0.000 claims description 15
- 238000012545 processing Methods 0.000 claims description 11
- 238000009412 basement excavation Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000013461 design Methods 0.000 abstract description 3
- 230000010354 integration Effects 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 239000004614 Process Aid Substances 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000004899 motility Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012731 temporal analysis Methods 0.000 description 1
- 238000000700 time series analysis Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Business, Economics & Management (AREA)
- Computing Systems (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a process mining system based on semantic requirement matching. The process mining system comprises a platform foundation unit, an algorithm plugin unit with an algorithm plugin, and a model visualization unit, wherein the platform foundation unit receives a requirement text and user log data, and calls the algorithm plugin in the algorithm plugin unit to carry out process mining; and after the platform foundation unit receives the mining result of the algorithm plugin unit, the platform foundation unit transmits the mining result of the algorithm plugin unit to the model visualization unit to display the mining result. The requirement text of the user and the algorithm plugin are matched to realize the automation of a plugin selection process, the dynamic integration of the algorithm plugin set improves system flexibility on an aspect of design, and the effective utilization of computation resources and the ordered execution of mining tasks can be guaranteed.
Description
Technical field
The present invention relates to the technology in a kind of digging flow field, a kind of digging flow system based on semantic requirement coupling
System.
Background technology
Information system is widely used the enterprise's production in fields such as manufacturing industry, running, flow monitoring and optimization, to enterprise
Operational action provides effective management and supports.Information system creates substantial amounts of journal file in running, it is possible to the most anti-
Mirror task practical operation situation and the distribution of resource in operation flow.Digging flow is as the important nondominant hand in BPM
Section, for checking and improve existing procedural model, the master in correcting system building process from the journal file that information system generates
The property seen, analyzes enterprise and produces and the resource utilization of operation, promote corporate process optimization and scheduling of resource.
Existing process model restorative procedure based on Petri network basic structure, mainly by processing legacy data, makes
It becomes the event log of compliant；It is used for concluding mining algorithm afterwards and excavates the process model of correspondence；By will be enlarged by
Event log calibrate with excavating the process model that obtains, deviation present in discovery process simulation model；Finally propose different knot
The recovery scenario of process model under structure, it is intended to repair process model, strengthens the concordance of process model.But these technology simply use
A kind of algorithm, finds and model calibration from the Petri model controlling stream angle and completing event log, itself does not possess analysis
Ability with coupling user's request.
Josue Obregon written DTMiner:A Tool for Decision MakingBased on Historical Process
Data proposes a kind of method judged with time Perspective Analysis flow process aid decision from tissue according to history flow data.But should
Method is based on tissue visual angle and time visual angle, and application is deployed in stand-alone environment, needs user to complete to build in client；Simultaneously
Log analysis process only relates to single algorithm, can not be analyzed according to the multiple visual angle of the need integrate of user, be unfavorable for algorithm
Update and safeguard, reducing the ease for use of product simultaneously.
Summary of the invention
The present invention is directed to deficiencies of the prior art, propose a kind of digging flow system based on semantic requirement coupling.
The present invention is achieved by the following technical solutions:
The present invention includes: platform base unit, with the algorithm groupware unit of algorithm groupware and model visualization unit, wherein:
Platform base unit receives demand text and user journal data, and calls the algorithm groupware in algorithm groupware unit and carry out digging flow,
Model visualization unit it is sent to show Result after the Result of platform base unit receiving algorithm bound cell.
Described demand text includes data form, field format, desired output model and input/output argument.
Described platform base unit includes: demand matching module, excavation module and log processing module, wherein: demand is mated
Module reception demand text generation is called plug set merging and is transferred to excavate module, and log processing module receives user journal data genaration
Process log object also is transferred to excavate module.
Described demand matching module describes text set according to digging flow ontologies and plug-in unit to be carried out based on semantic plug-in unit need
Generate after seeking coupling and call plug-in unit set.
Described log processing module resolves product process log object through log integrity and daily record.
Described excavation module includes the algorithm groupware scheduler transferring each algorithm groupware.
Described algorithm groupware includes that Petri network model finds that algorithm groupware, social networks find algorithm groupware and process log system
Meter plug-in unit.
Described model visualization unit uses visualization component D3.js and Highcharts to show Result.
Technique effect
Compared with prior art, demand text and the algorithm groupware of user are described and mate by the present invention, it is achieved that plug-in unit selects
The automatization of process, the dynamic integrity of algorithm groupware collection improves the motility of system in design, it can be ensured that calculate having of resource
Effect utilizes and the orderly execution of mining task.
Accompanying drawing explanation
Fig. 1 is present configuration schematic flow sheet.
Detailed description of the invention
Elaborating embodiments of the invention below, the present embodiment is implemented under premised on technical solution of the present invention,
Give detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment 1
As it is shown in figure 1, the present embodiment includes: platform base unit, the algorithm groupware unit including some algorithm groupwares and mould
Type visualization, wherein: platform base unit receives demand text and user journal data, and calls in algorithm groupware unit
Algorithm groupware carries out digging flow, is sent to model visualization unit after the Result of platform base unit receiving algorithm bound cell
To show Result.
Described platform base unit is input interface and the optimized integration of whole system framework, including three modules, is respectively
Demand matching module, log processing module and excavation module.
Described demand matching module receives user's request, and user's request is included in demand text, and demand text includes data
Form, field format, desired output model and input/output argument, also include that input journal description, desired output result describe.
Afterwards, it is labeled according to existing digging flow ontologies (OWL), describes text set in combination with predefined plug-in unit, should
Plug-in unit describes text set and includes inputting parameter and output result description, completes based on semantic coupling to solve demand text and plug-in unit
Describe the inconsistent of text set statement, ultimately generate and call plug-in unit set.This digging flow ontologies is to combine DBPedia and neck
The mode of territory expert modeling describes relation between some term and terms that digging flow is relevant with the form of OWL.
Described log processing module can carry out daily record and upload, and transmits user journal data by HTTP or FTP, and will connect
The user journal data received carry out log integrity, including removing the operations such as noise, format transformation and field correspondence, finally lead to
Cross daily record resolve result generating algorithm plug-in unit accessible process log object and be transferred to excavate module.
Described excavation module calls plug-in unit set and the process log of log processing module generation with what demand matching module generated
Object is as input.Excavating in module and be provided with algorithm groupware scheduler, it can generate scheduling according to the dependence between algorithm groupware
Process, and complete the orderly scheduling to algorithm groupware, the dynamic call process of algorithm groupware is real by unified RESTful interface interchange
Existing, the Result of the algorithm groupware being scheduled receives and completes Result and collect by excavating module, it is provided that to model visualization list
Unit.
Described algorithm groupware unit is the core processing unit of whole system framework, including the tool of all kinds of digging flow algorithm logics
Body realizes, and mainly includes three algorithm groupwares, is that Petri network model finds that algorithm groupware, social networks find algorithm groupware respectively
Plug-in unit is added up with process log.
Described Petri network model finds that algorithm groupware achieves the Petri network model discovery algorithm i.e. α+calculation in digging flow field
Method, completes the foundation of task nexus in process log object by the structure of track matrix, and method based on multi-thread concurrent is complete
Become the Petri network model generation of task based access control relation.
Described social networks finds that algorithm groupware achieves the social networks discovery algorithm i.e. work handover pass in digging flow field
System finds algorithm, using the parameter preset of user as design conditions, by the building of task participant relational matrix in process log object
Vertical, generate social network diagram based on work handover relation.
Described process log statistics plug-in unit achieves the statistical function to input flow process log object.By to input journal object
Middle different dimensions, such as task participant, performs time, the statistics of execution task dispatching, produces the statistical report of process log, including
Tasks carrying frequency statistics, process log time series analysis, task participant perform the contents such as mission frequency statistics.
Described model visualization unit is the output interface of whole system framework, receives Result from platform base unit and goes forward side by side
Row visual presentation and result derive, and this unit uses visualization component D3.js and Highcharts to realize Directed Graph Model, Petri
Net and social network diagram, and statistical result, such as display and the derivation of the Results such as broken line graph.
Described demand matching module resolves after the user's request of the demand textual form got, with reference to existing flow process
Demand text is labeled by Extracting Knowledge body, and combines plug-in unit and describe text and carry out based on semantic matching primitives, thus generates
Call plug-in unit set.Log processing module processes daily record and uploads, and obtains user journal data, and completes log integrity and daily record solution
Analysis process, ultimately generates process log object.Excavate module and call plug-in unit set and process log object according to what preamble module produced,
Calling and performing of actual algorithm plug-in unit is completed based on dependence between plug-in unit by algorithm groupware scheduler.In algorithm groupware unit
Each algorithm groupware describes according to the plug-in unit of self, for process log object and the calculating resource of distribution of the transmission of algorithm groupware scheduler
Complete mining process, and feed back to result excavate module.Excavate module obtain the Result of all called plug-in units and tie
Fruit collects, and is finally sent to model visualization unit with unified form.Model visualization unit is according to the Result received
Data characteristics, the output selecting different visualization components to realize Result i.e. shows and derives.Platform base unit is in whole system
System achieves the decoupling between algorithm groupware unit and user so that algorithm groupware unit can not affect the situation that user uses
Lower realization dynamically increasing, revise and deleting algorithm groupware.
Compared with prior art, user's request is understood accurately and comprehensive by the present invention, it is possible to dynamically transparent ground adjusts the system integration
Algorithm groupware, improves the extensibility of system, maintainability, ease for use and fault-tolerant ability, reduces the use threshold of user.
Claims (8)
1. a digging flow system based on semantic requirement coupling, it is characterised in that including: platform base unit, with calculation
The algorithm groupware unit of method plug-in unit and model visualization unit, wherein: platform base unit receives demand text and user journal number
According to, and call the algorithm groupware in algorithm groupware unit and carry out digging flow, the excavation of platform base unit receiving algorithm bound cell
Model visualization unit it is sent to show Result after result.
Digging flow system based on semantic requirement coupling the most according to claim 1, is characterized in that, described demand literary composition
This includes data form, field format, desired output model and input/output argument.
Digging flow system based on semantic requirement coupling the most according to claim 1 and 2, is characterized in that, described is flat
Platform base unit includes: demand matching module, excavation module and log processing module, wherein: demand matching module receives demand literary composition
This generation is called plug set merging and is transferred to excavate module, and log processing module receives user journal data genaration process log object also
It is transferred to excavate module.
Digging flow system based on semantic requirement coupling the most according to claim 3, is characterized in that, described demand
Join module according to digging flow ontologies and plug-in unit describe text set carry out based on generate after semantic plug-in unit demand coupling call slotting
Part set.
Digging flow system based on semantic requirement coupling the most according to claim 4, is characterized in that, at described daily record
Reason module resolves product process log object through log integrity and daily record.
Digging flow system based on semantic requirement coupling the most according to claim 5, is characterized in that, described excavation mould
Block includes the algorithm groupware scheduler transferring each algorithm groupware.
Digging flow system based on semantic requirement coupling the most according to claim 1, is characterized in that, described algorithm is inserted
Part unit includes that Petri network model finds that algorithm groupware, social networks find algorithm groupware and process log statistics plug-in unit.
Digging flow system based on semantic requirement coupling the most according to claim 1, is characterized in that, described model can
Visualization component D3.js and Highcharts is used to show Result depending on changing unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610280378.0A CN105956077B (en) | 2016-04-29 | 2016-04-29 | Based on the matched digging flow system of semantic requirement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610280378.0A CN105956077B (en) | 2016-04-29 | 2016-04-29 | Based on the matched digging flow system of semantic requirement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105956077A true CN105956077A (en) | 2016-09-21 |
| CN105956077B CN105956077B (en) | 2019-10-15 |
Family
ID=56916137
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610280378.0A Active CN105956077B (en) | 2016-04-29 | 2016-04-29 | Based on the matched digging flow system of semantic requirement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105956077B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106599325A (en) * | 2017-01-18 | 2017-04-26 | 河海大学 | Method for constructing data mining visualization platform based on R and HighCharts |
| CN108255802A (en) * | 2016-12-29 | 2018-07-06 | 北京国双科技有限公司 | Generic text Analytical framework and the method and apparatus based on framework parsing text |
| CN109192317A (en) * | 2018-07-17 | 2019-01-11 | 山东科技大学 | The process model modification method of the concurrent structure of circulation of logic-based Petri network |
| CN109710239A (en) * | 2018-12-29 | 2019-05-03 | 北京航天数据股份有限公司 | Industry pattern generation method and device, digital asset processing method and electronic equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102624865A (en) * | 2012-01-09 | 2012-08-01 | 浙江大学 | Cluster load prediction method and distributed cluster management system |
| CN102929606A (en) * | 2012-10-09 | 2013-02-13 | 曙光信息产业(北京)有限公司 | Cloud-computing-based plug-in model of data mining system |
| CN102929607A (en) * | 2012-10-09 | 2013-02-13 | 曙光信息产业(北京)有限公司 | Cloud-computing-based function chromatography architecture of data mining system |
| WO2015023568A3 (en) * | 2013-08-13 | 2015-11-19 | Siemens Industry, Inc. | Systems, methods and apparatus for optimizing fuel mix, fuel allocation and scheduling of generator resources |
-
2016
- 2016-04-29 CN CN201610280378.0A patent/CN105956077B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102624865A (en) * | 2012-01-09 | 2012-08-01 | 浙江大学 | Cluster load prediction method and distributed cluster management system |
| CN102929606A (en) * | 2012-10-09 | 2013-02-13 | 曙光信息产业(北京)有限公司 | Cloud-computing-based plug-in model of data mining system |
| CN102929607A (en) * | 2012-10-09 | 2013-02-13 | 曙光信息产业(北京)有限公司 | Cloud-computing-based function chromatography architecture of data mining system |
| WO2015023568A3 (en) * | 2013-08-13 | 2015-11-19 | Siemens Industry, Inc. | Systems, methods and apparatus for optimizing fuel mix, fuel allocation and scheduling of generator resources |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108255802A (en) * | 2016-12-29 | 2018-07-06 | 北京国双科技有限公司 | Generic text Analytical framework and the method and apparatus based on framework parsing text |
| 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 |
| CN109192317A (en) * | 2018-07-17 | 2019-01-11 | 山东科技大学 | The process model modification method of the concurrent structure of circulation of logic-based Petri network |
| 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 |
| CN109710239A (en) * | 2018-12-29 | 2019-05-03 | 北京航天数据股份有限公司 | Industry pattern generation method and device, digital asset processing method and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105956077B (en) | 2019-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu | The current status and developing trends of industry 4.0: A review | |
| CN112104751B (en) | Method, device and system for processing regulation and control cloud data | |
| CN105956077A (en) | Process mining system based on semantic requirement matching | |
| CN103853821B (en) | Method for constructing multiuser collaboration oriented data mining platform | |
| CN111131480A (en) | Cloud edge cooperative service system for smart power plant | |
| CN102646220A (en) | Multi-network-based manufacturing data real-time process management system and management method in ship manufacturing industry | |
| CN111079997A (en) | Modeling and collaborative optimization method | |
| Leitao et al. | A survey on factors that impact industrial agent acceptance | |
| CN115908046A (en) | Visual power distribution system based on airport terminal building BIM | |
| CN117787892A (en) | Enterprise collaborative office system and method thereof | |
| Ćwikła et al. | Analysis of the possibility of SysML and BPMN application in formal data acquisition system description | |
| CN102646119B (en) | Method for realizing integration of standardized system and information system | |
| CN112486666A (en) | Model-driven reference architecture method and platform | |
| CN104123584A (en) | Organization optimization method based on information system | |
| CN104123585A (en) | Service organization optimization analysis method based on service simulation | |
| Eynard et al. | Aeronautics product development and certification workflow based on process modelling | |
| Barták et al. | Flowopt: Bridging the gap between optimization technology and manufacturing planners | |
| Malek | Online dependability assessment through runtime monitoring and prediction | |
| Liu et al. | Automotive industry APP development strategy in the prospect of digitalization | |
| Peng | The Research and Implementation in Digital Transformation of Manufacturing Enterprises | |
| CN112346727B (en) | Method for uniformly modeling discrete random problem and continuous problem in production system | |
| CN103105838A (en) | Intelligent logistics electronic selecting system and implementation method | |
| CN113408843B (en) | Energy enterprise digital transformation management method | |
| Liu et al. | Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry | |
| Zhang et al. | The Banking System Reference Implementation Based on SOA |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
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 |
|
| TR01 | Transfer of patent right |