Expert Systems with Applications 36 (2009) 7576–7580

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Expert Systems with Applications

journal homepage: www.elsevier .com/locate /eswa

An OWL-based semantic business process monitoring framework

Dongwoo Kang a, Sunjae Lee b,1, Kwangsoo Kim a,*, Jae Yeol Lee c,2

a Department of Industrial and Management Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Namgu, Pohang 790-784, South Koreab Engineering Information Group, Samsung Electronics Semiconductor Business, South Koreac Department of Industrial Engineering, Chonnam National University, Gwangju, South Korea

a r t i c l e i n f o

Keywords:Semantic business process monitoringframeworkBusiness process monitoringService-oriented business processOntology-based process monitoring

0957-4174/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.eswa.2008.09.027

* Corresponding author. Tel.: +82 54 279 2195; faxE-mail addresses: hyunil@postech.ac.kr (D. Kang)

(S. Lee), kskim@postech.ac.kr (K. Kim), jaeyeol@chonn1 Tel.: +82 31 208 7206.2 Tel.: +82 62 530 1782.

a b s t r a c t

Process monitoring phase is one of the service-oriented business process (SOBP) lifecycle phases. Tradi-tional process monitoring approaches have been only achieved at the syntactic level of the process mon-itoring contexts, which causes the communication problems such as ambiguous understandings anddivergent interpretations. To solve the problems, the process monitoring should be achieved at thesemantic level as well as at the syntax level of the process monitoring context. In order to support seman-tic monitoring operations, an ontology-based monitoring framework for the SOBP execution is suggestedin this paper. The suggested framework combines a BPEL4WS process model with the semantic monitor-ing context which is expressed with OWL.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Web services provide a suitable technical framework for mak-ing business processes accessible within enterprises and acrossenterprises, so that they have promoted a new paradigm of a busi-ness process which is called the service-oriented business process(SOBP) (Leymann, Roller, & Schmidt, 2002). The SOBP uses Webservices as an implementation platform for activities that belongto a business process (Leymann et al., 2002), and it is modeled withthe BPDLs which are used to describe the sequence of businessactivities in a SOBP.

The lifecycle of the SOBP is generally composed of the followingfour phases: (1) discovery of relevant activities, (2) design of over-all processes, (3) deployment and execution of the processes, and(4) monitoring and improvement of the processes (Muehlen &Rosemann, 2000). In the SOBP lifecycle, this paper focuses on theprocess monitoring whose purposes are informing the user ofundesired situations and run-time misbehaviors (Grigori et al.,2004; McGregor & Kumaran, 2002), providing necessary data basisfor continuous process improvement (Muehlen & Rosemann,2000), enabling the exact and timely analysis of the processes(McGregor et al., 2002; Wang, Wang, & Xu, 2005), and evaluatingthe performance of processes (McGregor et al., 2002; Muehlen &Rosemann, 2000). The contexts of the process monitoring includesthe execution status of each SOBP instance, the execution progress

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: +82 54 279 5998., sunjae76.lee@samsung.comam.ac.kr (J.Y. Lee).

of the preceding SOBP instances, the maximum number of SOBP in-stances available for concurrent use, and so on (Maamar, Narendra,& Sattanathan, 2006).

In order to monitor the process, the traditional process monitor-ing approaches have been only achieved at the syntactic level ofthe process monitoring contexts. Because only the syntactic levelcould not guarantee for the meanings related to the monitoringcontexts to be formally defined, the heterogeneous meanings atthe syntactic level are defined, published and discovered. Conse-quently, the monitoring only at the syntactic level causes the com-munication problems such as ambiguous understandings anddivergent interpretations. For example, supposing that a monitor-ing context includes a variable named executionStatus, the syntaxof the monitoring context may prescribe that the value of the exe-cutionStatus should be in the form of strings. In spite of this pre-scription, the meanings of the executionStatus as well as itsstring values such as active, completing, prepared, etc. are not for-mally defined in the syntax. Even if the meanings are described inthe extra documents which are related to the monitoring context,most of the document contents are expressed in a natural lan-guage. Because expressing in a natural language could not formallydefine the meaning, it also has the high possibility of causing thecommunication problems such as ambiguous understandings anddivergent interpretations.

In order to solve the communication problems, the monitoringcontext should be formalized using ontology at the level of seman-tics, and the SOBP monitoring is required to be conducted at thelevel of semantics as well as at the level of syntax (Medjahed,Bouguettaya, & Elmagarmid, 2003). By monitoring the executionstatus at the level of semantics, a SOBP gets abilities to solve theproblem of a divergent understanding and interpretation of the

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status, to assess the ongoing commitments, and to cope with theunpredictable change of surrounding environment. In spite of theimportance of the monitoring semantics, most BPDLs includingthe BPEL4WS ( Thatte, 2003) which is a de facto standard of theBPDLs do not support any monitoring operations at the level ofsemantics.

In order to support semantic monitoring operations, an ontol-ogy-based monitoring framework for the SOBP execution is sug-gested in this paper. The suggested framework combines aBPEL4WS process model with the semantic monitoring contexts.Before the semantics of the SOBP monitoring contexts are formal-ized using ontology, it is needed to define the concept of the ontol-ogy obviously. An ontology is a shared conceptualization based onthe semantic proximity of terms in a specific domain of interest(Gruber, 1993; McIlraith et al., 2001), and it is typically modeledusing the OWL. The ontology is increasingly seen as key to enablingsemantics-driven data access and processing (Bussler et al., 2002),and it is expected to play a central role in the SOBP, extending syn-tactic service interoperability to semantic interoperability (Hor-rocks, 2002). The suggested monitoring framework inserts theOWL semantic tags into a BPEL4WS process model, which are usedto describe the semantic monitoring contexts. The semantically de-scribed monitoring contexts are expected not only to reduce thecommunication problems, but also to provide higher value-addedbusiness contexts through the reasoning mechanism supportedby the OWL formalism (Smith et al., 2004).

This paper is organized as follows. A suggested semantic moni-toring framework is overviewed in Section 2. Section 3 presents adescription of the semantic monitoring contexts in the BPEL4WSprocess model. Section 4 shows a reasoning based on the semanticmonitoring contexts and relevant knowledge and Section 5 con-cludes this paper.

2. The semantic monitoring framework

Fig. 1 shows the overview of the suggested semantic monitor-ing framework for the SOBP execution which consists of thesemantic monitoring agent, the BPEL4WS execution engine, theBPEL4WS context manager and the business knowledge base.

The semantic monitoring agent communicates with the processowner at the level of semantics using the OWL monitoring requestand response, and the agent communicate with the BPEL4WS exe-cution engine at the level of syntax. The semantic monitoring agentrearranges the monitoring request and context at both the seman-tic and syntactic levels by reasoning relevant knowledge from thefollowing business knowledge base. The business knowledge basemanages the knowledge related to the business processes, and

Fig. 1. Overview of the semantic monitorin

the knowledge base sends appropriate knowledge to the semanticmonitoring agent whenever the semantic monitoring agent re-quests the knowledge. The BPEL4WS execution engine managesthe execution of the BPEL4WS business process, and keeps the con-tinuously-changing execution data generated during the executionof the BPEL4WS process. The BPEL4WS context manager containsthe model of the BPEL4WS business processes in the model repos-itory and parses the model in order to get the OWL-based semanticmonitoring contexts.

Under the suggested framework, a semantic monitoring opera-tion is performed through the following six steps.

Step 1: The process owner sends the monitoring request whichis in the form of the OWL to the semantic monitoring agent. Fig. 2shows an exemplary monitoring request at the semantic level, andthe meaning of the request is that the process owner wants toknow a list of unexecuted activities. The details about the OWLsemantic tags will be explained in Section 3.

Step 2: In order to interpret the monitoring request from theprocess owner, the semantic monitoring agent gets relevantknowledge from the business knowledge base and performs rea-soning based on the knowledge as occasion demands. After theinterpretation is completed, the semantic monitoring agent rear-ranges the monitoring request in the level of syntax and sends itto the BPEL4WS execution engine. Fig. 3 shows an example moni-toring request at the syntactic level.

Step 3: After the BPEL4WS execution engine receives the moni-toring request at the syntactic level, the engine requests theBPEL4WS context manager to parse monitoring contexts whichwere inserted into the BPEL4WS process in the form of the OWL.

Step 4: After the BPEL4WS context manager parses the monitor-ing contexts, the context manager sends the OWL monitoring con-texts to the BPEL4WS execution engine.

Step 5: The BPEL4WS execution engine receives the OWL mon-itoring contexts, and then it sends syntactic monitoring responseincluding extra execution data generated during execution to thesemantic monitoring agent.

Step 6: The semantic monitoring agent performs reasoningbased on the syntactic monitoring response and the relevantknowledge from the business knowledge base, and then it trans-forms the monitoring contexts at the semantic level. The trans-formed monitoring contexts are sent to the process owner.

3. Description of the semantic monitoring contexts in theBPEL4WS process model

As discussed in Section 2, the semantic monitoring contexts arelocated in the BPEL4WS process model. This section explains how

g framework for the SOBP execution.

Fig. 2. Exemplary monitoring request at the semantic level.

Fig. 3. Exemplary monitoring request at the syntactic level.

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the semantic monitoring contexts are described in the BPEL4WSprocess model and how the BPEL4WS process model containingthe semantic monitoring contexts still conforms to the BPEL4WSspecification.

In order to demonstrate practicality of the suggested approach,a semiconductor foundry business process is given as an exampleand the BPEL4WS process model containing the semantic monitor-ing contexts for the foundry business process is designed. In anexemplary foundry business, Fabless Semiconductor Companies(FSCs) use Manufacturing Fabs (MFs) in order to get data for inlinetest, wafer test and module test of the semiconductors which aredesigned by the FSCs (RosettaNet.org). After a MF receives thesemiconductors designed by a FSC, the MF performs test-relatedprocesses such as prefuse test, fuse blow repair, and postfuse test.The MF captures data for logistics, inline metrology, and test at theseveral points of test-related processes. The captured data are pro-cessed in order to make a report, and the report is securely sent tothe FSC according to a request of the FSC. Fig. 4 shows theBPEL4WS process model of the MF which consists of the activitiessuch as receiving semiconductors, prefuse testing, fuse blowrepairing, postfuse testing, creating a report, and sending thereport.

In this paper, it is assumed that all these activities are imple-mented as individual Web services and the executions of the activ-ities are triggered by the invocations for the Web services. Forexample, Fig. 5 shows the invocation for the Web service whichimplements the activity of the prefuse testing.

Fig. 4. BPEL4WS process model for the

The BPEL4WS process model in Fig. 4 starts its execution byreceiving semiconductors then performing the prefuse test. The<sequence> tag in Fig. 4 indicates that receiving semiconductors,prefuse testing, the composite activity enclosed by the <switch>,creating a report, and sending the report are performed sequen-tially. Depending on the results of the prefuse test, whether thefuse blow repair and the postfuse test for the semiconductors areto be performed is determined in the BPEL4WS process model.The combination of <switch> and <case> is used to express sucha conditional behavior so that the result of the prefuse testing isevaluated in the <case> clause. The <while> tag is used to describethe repetition of the enclosed activities, and it also can contain aconditional behavior. After zero or more a composite activity com-posed of a fuse blow repair and a postfuse test is repeated, a reportis created and sent to the process owner.

After the BPEL4WS process model is designed, semantic moni-toring contexts are inserted into the process model. As shown inFig. 6, the semantic monitoring contexts are located between thetwo comment tags, . For the purpose of automatic context parsing,it is required to indicate that semantic monitoring contexts are lo-cated between the comment tags. In order to indicate the existenceof semantic monitoring contexts, the tag <semanticMonitoring-Context> is recommended to be used in this approach.

In the <semanticMonitoringContext>, the semantic monitoringcontexts are descried using the OWL semantic tags such as <owl:class>, <rdfs:subClassOf>, <owl:ObjectProperty>, and so on. Forexample, the semantic monitoring contexts in Fig. 6 are used to ex-

semiconductor foundry business.

Fig. 5. Invocation of an activity in the form of Web service.

Fig. 6. Semantic monitoring context inserted into the BPEL4WS process model.

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plain what the Fab, the Fabrication, and the Prefuse Test are, whichproduct the Fab produces, and so on. The first line in the <seman-ticMonitoringContext> explains that there is a class named Factoryusing the tag <owl:class>, and the lines 3–5 describe that there is aclass named Fab and the Fab is a kind of Factory using the tag<rdfs:subclassOf>. The lines 6–10 specifies the relationship be-tween the Fab and the Fabrication using <owl:ObjectProperty>,and the relationship means that the Fab is a short form of the Fab-rication. From 11th line to 20th line, the semantic monitoring con-texts specify the Fab produces IntegratedCircuits and the Fabincludes the process of PrefuseTest. In this manner, various mean-ings related to the process can be described in the semantic mon-itoring context.

As mentioned above, it is notable that the semantic monitoringcontext is located in the comment tags. By being located in the com-ment tags, the semantic monitoring context does not violate theBPEL4WS specification so that the existing BPEL4WS execution en-gines that are not able to recognize the semantic monitoring con-texts can also manipulate the BPEL4WS process model withoutany modification. The semantic monitoring contexts included inthe BPEL4WS process model are parsed using the context parser,then they are sent to the semantic monitoring agent in order to cre-ate higher valuable contexts for the process owner by reasoning.

4. Reasoning based on the semantic monitoring contexts andrelevant knowledge

Since the semantic monitoring contexts are supported by theOWL formal semantics (Patel-Schneider et al., 2004), reasoningcan be performed based on the monitoring contexts and the busi-ness knowledge which is retrieved from the business knowledgebase.

The OWL formal semantics provide five property characteristicsthat can be used to reason.

� First, if a property, P, is specified as transitive then for any x, y,and z:

Pðx; yÞ and Pðy; zÞ implies Pðx; zÞ

� Second, if a property, P, is tagged as symmetric then for any xand y:

Pðx; yÞ iff Pðy; xÞ

� Third, if a property, P, is tagged as functional then for all x, y, andz:

Pðx; yÞ and Pðx; zÞ implies y ¼ z

Fig. 7. OWL-based context reasoning.

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� Fourth, if a property, P1, is tagged as the owl:inverseOf P2, thenfor all x and y:

P1ðx; yÞ iff P2ðy; xÞ

� Fifth, if a property, P, is tagged as InverseFunctional then for all x,y, and z:

Pðy; xÞ and Pðz; xÞ implies y ¼ z

In Fig. 7, the object property named isPerformedBefore is spec-ified as transitive and two facts that the PrefuseTest is performedbefore the FuseBlowRepair and that the FuseBlowRepair is per-formed before the PostfuseTest are described in the semantic mon-itoring context. Based on this transitive characteristic of theisPerformedBefore, the new fact that the PrefuseTest is performedbefore PostfuseTest can be reasoned. This reasoning operation isformalized as follows:

isPerformedBefore(PrefuseTest,FuseBlowRepair) andisPerformedBefore(FuseBlowRepair, PostfuseTest)implies isPerformedBefore (PrefuseTest,PostfuseTest)

In this manner, various new facts can be reasoned based on thesemantic monitoring context and the business knowledge.

5. Conclusion

Existing BPDLs such as the BPEL4WS, WSCDL, etc. suffer difficul-ties in monitoring the execution status of business processes at thesemantic level. By monitoring the execution status semantically, aSOBP gets abilities to solve the problem of a divergent understand-ing and interpretation of the status, to assess the ongoing commit-ments, and to cope with the unpredictable change of surroundingenvironment. In order to monitor the execution status semanti-cally, a semantic monitoring framework using an ontology is sug-gested in this paper.

The suggested framework inserts the OWL semantic tags be-tween comment tags in a BPEL4WS process model, and the seman-tic tags are used to describe the semantic monitoring contexts. Dueto the location of the semantic tags, the traditional BPEL4WS exe-cution engines are also able to deal with the suggested processmodel. In addition, the reasoning based on the semantic monitor-ing contexts and the process relevant knowledge enables to pro-vide higher value-added business contexts. In conclusion, thesuggested framework can be used as a semantic monitoring meth-od complementing the BPEL4WS process model, not as a substituteof the BPEL4WS model.

Future research works still remain as follows. First, since theontology containing the semantic monitoring context evolves asthe environments changes, the systematic research on the ontol-ogy evolution is required to be conducted. Second, when the busi-ness process management system simultaneously manipulatesseveral process instances including semantic monitoring contexts,the ontology merging would be a solution to support interoperabil-ity among the semantic monitoring contexts.

Acknowledgement

This work is supported by Grant No. R01-2007-000-11040-0from the Basic Research Program of the Korea Science and Engi-neering Foundation.

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