The Research of Dynamic Workflow Technology Based on Knowledge Engineering

Transcription

1 The Research of Dynamic Workflow Technology Based on Knowledge Engineering Sun Jiaoyao 1, Huang Ning 1, Wang Feng 1, Liu Chao 2 1 Beihang University, Beijing, , China, sunjiaoyao@hotmail.com 2 Shanghai Jiaotong University, Shanghai, ,China, lucas@sjtu.edu.cn Abstract As more and more enterprises and organizations get involved in e-business acquisition, the process of acquisition becomes even more complicated. In this paper we propose workflow technique in process control of acquisition, which need high flexibility of workflow. The processing model, organizational model and resource model based on workflow need adjusting when operations alternate. The workflow system in this paper stores workflow model to ontology knowledge database in the form of knowledge. Users can log onto the knowledge database server to modify models dynamically and therefore realize its dynamic operation. At the same time, because the workflow model has been constructed in the knowledge database, the knowledge there becomes more enriched as this workflow is operated effectively. We can implement reasoning knowledge by applying logical reasoning block function, which carries out automatically or provides active information to people when transferring tasks and achieves an intelligent operation of workflow. Keywords Workflow, dynamic modelling, knowledge engineering, ontology 1 Introduction The course of military purchasing affairs involves various customers such as equipment ministry, the design-finalizing committee and production contractor and also many enterprises, units and organizations. Because these units have different kinds of organizational structure and dissimilar examining and approving courses, it is extremely difficult to centrally define the whole flow and for each task to define its definite executive. Therefore, firstly we give customers the right to define the definition of workflow and its organizational structure. Supervisors can define holistic course of workflow and organizational structure. However, it is each unit s duty that modifies and confirms the flow itself and inner organizational structure in detail. Secondly, there are always some changes in the defined flow or uncertain factors about how to execute every step during the flow course. Obviously, the organizational structure, role and staff of one unit are also in changing, which turns out that a well-adjusted workflow often can not adapt to the changes of flow and organization as time goes by. Thus, we need to give customers privilege to dynamically modify and define the course of workflow and its organizational structure. Thirdly, because the workflow system is not a simple office automation system, but a system which comprises manual and automatic flow. The executive is not only people, also local

2 applications. Every unit often has a set of operational software/system of its own and forms an independent information island, making it very hard to be utilized among units. Because of the advances of network technologies and distribution applications, this information can published out through Grid and Web Service. In order to make full use of software resources of every unit, this system counts grid resource, Web Service resource and data resource into the definition of resource model. We define the related resource for man to use directly, or it is called by some application to achieve the operation of flow automatically. Fourthly, with more and more complicated course of workflow, we need various knowledge to solve the problem that how some steps should be carried out. And it is often difficult to judge the direction of next step by intuition when the flow is operating. To solve this problem, this paper stores workflow information in the knowledge database as knowledge pieces and compiler provides assistant reasoning function for flow operation through logical reasoning function of knowledge, furthering actualize automatic reasoning function. This paper builds up models on base of ontology database of knowledge engineering to realize agilely dynamic structural definition and flow definition, then make full use of more and more network resources, finally implement flow s intellective operation and reasoning. As the workflow system is constantly applied and knowledge database is accumulated, the system we designed is also more and more perfect than before. 2 Dynamic Workflow System The basic concept of workflow originates from the area of production structure and office automation. Workflow is a kind of operating process that is able to be totally or partly carried out and can be transferred and carried out among different executives according to a series of course rules, documents, info or tasks. One workflow includes a group of activities and their consequence relation, course and condition of activities starts and ends, in addition attached by description of every activity. The prime advantage of workflow management is to separate application logic from course logic, change systematic function by modifying course model under the condition that not modifies detail function and efficiently combine user, info and applications together to accomplish the integral management of total course. It is convenient for carrying out fixed workflow if a flow is simple and has higher structural level, also repeated rules such as weekly finance report and yearly increase and decrease table etc. while if the tasks are changeable and flexible when we make the flow, common workflow software can not accomplish these tasks. Therefore, a lot of dynamic workflow systems with flexibility character are brought out. The notion of flexibility is specially brought forward to solve the rigidity or stiffness problem during defining and executing the model in the system. These characters of workflow system can make more route choices and redundancy nodes when we define the workflow, then calculate and agilely decide direction of the route according to route rules and parameters

3 made by customers. Or someone do it by a flexible activity which symbols one activity or a sub-flow whose definition is not given out completely. In all, we actualize the dynamicity of workflow through flexible preparation for concentrative modeling in order to keep its dynamicity. However, there is in defect of make dynamic modification when the workflow is being operated, so customers only have right to execute by steps without right to modify dynamically. While this workflow system enables customers to dynamically modify workflow sample data, point out its direction and the temporary executive or every property including setup the finishing time or else through definition of distribution according to user s limit when they are operating this workflow. Although workflow itself can be modified dynamically, the next step execution of workflow is probably hard to be confirmed by intuitive acknowledge because of the complexity of whole workflow, and the intelligent reasoning helps solve this trouble, thus we need the support of knowledge engineering. 3 A Workflow System supported by Knowledge Engineering Because corporate inner flow is complex and there are constant personnel changing affairs, it leads to low efficiency when work is being carried out. We can handle this flow as knowledge and make people finish their task by assistant skills according to former knowledge and their reasoning. Knowledge Engineering is a theory which using artificial intelligence technique. Its purpose is to solve problems, make intelligent creativity and treat with automatic. It researches on knowledge s formal expression and logic reasoning. The formal symbol is that knowledge must be structural stored and organized by some kinds of consistency in order to realize computer automatic knowledge handling and problem solution. Ontology is a kind of formal knowledge. It has collective shared knowledge expression, reuse knowledge and knowledge exchange purpose. Therefore, ontology can be a group of agreement set, a language, a standard and a structure. Next, ontology should be the minimum knowledge set of organizational system space; the minimum set ensures the consistency of knowledge semantic. Thirdly, ontology is one kind of steady knowledge, which is beyond content knowledge in application detail. Knowledge database and ontology: the former is a set of statement knowledge and course knowledge concerning some special area and the knowledge in it must be formal expressed, which has knowledge expressive methods based on logic, relation, objection and rule; the latter is also one kind of formal knowledge expressive methods. This paper implements a series of practices and explorations based on the ontology project s compiler Protégé, through which we realize the characters of knowledge engineering. At the same time we also explore workflow tools supported by knowledge engineering when applying some workflow technology so that we can describe enterprise s operating course ideally. As we know, modelling workflow is the basis of workflow technology and because of characters of knowledge engineering itself, data can be reused and shared. Therefore, we

4 provide effective managing and supporting tools for the usage of knowledge engineering towards whole E-business from the beginning of our modelling. Thus, the research about this project has better research value and realistic value to doing purchasing and systematic integration. 4 Workflow Modelling based on Ontology Protégé is produced by medicine institution of Stanford. This product is based on the structure of ontology engineering and we can set up a simple or giant ontology model to describe some stuffs even our objective world through Protégé. Figure 1: Knowledge database of constructing workflow model We abide by the rule of XPDL - XML Process Definition Language - and build up the knowledge database of workflow modelling, namely we define workflow model in knowledge database under the criterion of workflow uniformed interface and make it totally matched with XPDL modelling, and thus all information of workflow modelling can be stored into knowledge database. On the basis of workflow modelling course, system also builds up the organizational model into knowledge database. When modelling one workflow, each activity performer can directly pick out from organization, role or individual of the organizational model in knowledge database. What s more, in order to make use of network resources, we make the definition of grid and Web Service addresses, parameters and methods etc. to knowledge database, thus this leads some automatic flow finished by one service or these services provide performers with better usage to fulfil tasks. After building up the structure of knowledge database, we can start a new example, namely building a workflow model. There is a GraphWidget type of Form in Protégé which can begin a node of workflow and become a example of transition automatically when nodes are connected. Through this we can easily self-define the flow.

5 Figure 2: Modeling course of workflow After defining the flow, the engine of workflow directly parses all information about the flow by API interface. 5 Workflow system structure This workflow rebuilds core of open source code OBE as engines and takes dynamic modelling technology to solve the question above. As Figure 3 shows, the server of this system is composed by two parts, one of which realizes the function of ordinary workflow engine, supports and watches workflow s operation, while the other part supports workflow s dynamic modelling. Client terminal has also two parts, one of which is used to handle ordinary workflow, while the other part is used to dynamic model workflow. Both client and server terminals communicate with each other by RMI and HTTP. Among them parsing workflow modelling is the difficult part in this system. This workflow engine is modified on the base of OBE (open business engine) workflow engine. The parsed part of OBE course model is implemented through reading document of standard XML workflow definition. However, its organizational model is implemented by database. Now we put its model components to Protégé and read this model through API interface of knowledge database. First, we define the frame structure of course model according to uniform XPDL of workflow, at the same time construct post-organizational model database and resources model database s frame structure according to the features of its organization and resources. Through the connection of RMI, we can construct an instance at client terminal according to the features of our organization, resources and course, fill in corresponding slots and create automatically corresponding instance by the graphic interface of course modelling. Thus the whole model is built up. When reading the model, we only find the workflow s instance, and then find its corresponding slot, finally read its corresponding information. Because clients can connect to the server at any time, they can revise this model dynamically, which extremely overcomes the faults of concentrative modelling workflow at old times.

6 After the administrator builds up the huge sketch of organizational structure, every staff can construct corresponding organization according to its condition. If personnel changes, we can update the corresponding part of organizational model at any moment, also can alter the performer of workflow activities. Second, some service and resources of every unit can be put in to resource modelling, and other people can use these resources according to their provided information. Third, when workflow is being carried out, there are some changes of the flow because of some reasons. So at this time, clients can log onto their workflow modelling client terminal and dynamically modify flow s definition, the result is that the flow behind can judge from new flow definition to continue operating. Pr ocess cl i ent Model i ng cl i ent task manger Cl i ent cont r ol message task message WEB DATA Pr oj ect DATA WEB DATA BOM DATA WEB DATA CAD DATA Pr oj ect file file file r esour ce model par ser Open busi ness engi ne or gani ze model par ser pr ocess model par ser Ont ol ogy i nt er f ace Ser ver Pr ocess model ont ol ogy Resour ce model ont ol ogy Or gani ze model ont ol ogy Figure 3: structure of workflow system At last, the workflow may read data from knowledge database when branching off and jumping, realizing workflow s automatic operation. For example, the XOR display model. After one task is dealt over, we find many sub-flows available behind it to go, but there is only one sub-flow permitted to operate. Because the model is constructed on the knowledge database, beside, the inserter JESS of Protégé has reasoning function, we can provide our experience and gist for its decision according to the information to judge next flow direction or provision information for deciders. There also may be some course meaning automatic course, we transfer corresponding application to realize flow s operation. At this time of its operating, the knowledge database can be viewed as condition and gist for its automatic operation.

7 Figure 4: Intelligent jumping Otherwise, the course of workflow operation can be viewed as knowledge to store up, as more time is used to it, we can make use of information of flow operation in the database to actively provide information when we decide how to operate flow for the next time. For example, when there is a stuff streaming, a new secretary will take place this position, but she does not know how to handle it if she encounters any emergency, therefore she first can find similar experience according to the positive hint above or through searching function. So, such a new worker can adapt to the position as soon as possible, and make better choice through former experiences. She can store up every flow operation as knowledge, slowly realizing the intelligence of workflow. 6 Conclusion and Prospect Through this design of workflow system, we put workflow constructed in the knowledge database, enabling the dynamic modification of workflow model. However, the parts referred as power limitation and role control during this dynamic operation course is not perfect. Otherwise, although Web service and grid service information is provided, next objection is to make out the complex information service. Finally the construction of knowledge database and intelligent reasoning function are still not perfect, wishing we can find its faults as using it and make further improvement at later work. Acknowledgement This work has been funded by the Aviation Science Fund of China (No. 02J51068). The authors wish to acknowledge the Commission for their support. And some information of Protégé and Ontology are achieved from the web site: And thank the developer of the Protégé. References Fan Yun-Shun. Workflow management technology, Pku University publish CUI Li-zhen, A Method of Workflow Dynamic Modification 2003 JOURNAL OF SYSTEM SIMULATION. YU Zhong-Chen, Research on Dynamic Modification Technology in Workflow Management System, Journal of Yanshan Univ Tatiana Almeida S. C. Vieira, An Ontology-driven Architecture for Flexible Workflow Execution, Proceedings of the WebMedia & LA-Web 2004 Joint Conference 10th Brazilian Symposium on Multimedia and the Web 2nd Latin American Web Congress, 2004.

8 LI Shan-Ping, Overview of Researches on Ontology, JOURNAL OF COMPUTER RESEARCH AND DEVELOPMENT WfMC. Workflow Management Coalition Terminology & Glossary[ S]. WFMC2TC21011, Feb, Prashant Doshi, Dynamic Workflow Composition using Markov Decision Processes, Paulo Dias, Dynamic Evolution in Workflow Management Systems, Proceedings of the 14th International Workshop on Database and Expert Systems Applications, 2003.