dynamic soa framework to support ad hoc enterprise alliance formation

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New Generation Computing, 30(2012)123-139Ohmsha, Ltd. and Springer

Dynamic SOA Framework to Support Ad Hoc Enter-

prise Alliance Formation

Jason J. JUNG

Department of Computer Engineering, Yeungnam UniversityDae-Dong, Gyeongsan,

SOUTH KOREA, 712-749

[email protected]

Received 30 August 2011Revised manuscript received 16 December 2011

Abstract Collaboration among businesses is needed to successfullyfulfill a given task and goal. Service-Oriented Architecture (SOA) has beenregarded as an efficient platform to support flexible interoperability amongvarious enterprises by discovering, selecting and composing services. How-ever, since a large number of enterprises have been participating in this SOAplatform, relationships among these enterprises are getting too complicatedto obtain flexibility and scalability for efficient collaboration. Thereby, inthis paper, we propose a dynamic SOA platform to discover service chainsfor building ad hoc enterprise alliances where the only relevant enterprisesare sorted out and merged. As a result, given an event, decision makers canfind out which enterprises (and services from the enterprises) might be se-lected for their collaboration. The proposed SOA platform has been appliedto mobile advertisement application as a case study. With respect to twoindicators (i.e., precision and agility), we have shown that the proposed SOAoutperforms traditional enterprise collaboration schemes.

Keywords: Service Oriented Architecture, Event-driven SOA, Service Chain,Enterprise Alliance Formation, Enterprise Network.

1 IntroductionThere have been many studies on automating business collaborations. In

case of client-server platforms, message-based transactional processing has beenapplied many applications for sharing electronic resources. With emergence ofService-Oriented Architecture (SOA), service composition has been regarded as


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an essential and important process for the automated business collaborations.A number of studies have been proposed to conduct service composition. Sim-ply, various language models, e.g., WSMO,1 OWL-S,2 and SAWSDL3 haveprovided a standardized semantic metadata for describing the services to beshared.

Such collaborations among enterprises have been applied to many formsof cooperative business relations, like outsourcing, supply chains, or spontaneousconsortium.4,14) Figure 1 illustrates an example of enterprise collaborations be-tween multiple businesses in health care market. Two enterprises in pharmacybusiness and medical R&D business have to be integrated with others (e.g., med-ical equipment supplier and health insurance provider) for successfully deliveringtheir services to customers.

Fig. 1 Health care market model fragmented from Basole and Rouse(2008);2) The small circles indicate the services of the corre-sponding enterprises, and the curved arrows are relationshipsbetween services.

As shown in Fig. 1, due to various needs and requests from customers,enterprises have been trying to collaborate with others in many different do-mains. The services can be provided by not only traditional offline businesses

but also online businesses in many commerce areas (e.g., online travel agenciesand third-party logistics). They have a number of problems on heterogeneities.It means that it is difficult for the enterprises to automatically communicateand understand with each other. Even though some studies6,22) have proposed

1 Web Service Modeling Ontology, http://www.wsmo.org/2 OWL-S: Semantic Markup for Web Services, http://www.w3.org/Submission/OWL-S/3 Semantic Annotations for WSDL and XML Schema, http://www.w3.org/TR/sawsdl/


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Dynamic SOA Framework to Support Ad Hoc Enterprise Alliance Formation 125

semantic approaches to enable the heterogeneous enterprises to understand andcompare meaning of the services, they do not consider scalability and agilityin a dynamic environment. As the number of online and offline enterprises isincreasing, it is getting more difficult to support collaborations among the en-terprises by conducting SOA activities (e.g., service discovery, orchestration andcomposition). In other words, managing the services as well as service chains isa complicated task.

Thus, in this paper, we focus on enterprise alliance formation in thedynamic environment. Enterprise alliance in online business is to integrate aset of virtual organizations (VO) which are closely linked with each other forachieving a certain unified business goal.11,28) In order for the enterprise allianceto have better business strategies and tactics, it comes together to efficientlyshare not only services themselves but also various enterprise resources (e.g., ex-

periences, knowledge, and useful competencies) whose cooperation is supportedthrough computer networks. By taking into account the sequential links betweenservices, enterprise alliance is regarded as Service Chain Management (SvCM)which enables service organizations to meet customer requests and to minimizecosts through intelligent and optimized forecasting, planning and scheduling ofthe service chain, and its associated resources such as human, networks and otherassets. Practically, SvCM can be applied to broad areas, covering field force andworkforce automation, network and asset planning and also aspects of humanresources systems,21) enterprise resource planning24) and customer relationshipmanagement.

Moreover, in the dynamic environment, agility on discovering servicechains is a crucial factor for enterprise alliance formation. Many events can be

unpredictably occurred in many dynamic environment. Given a certain event,the SvCM system should be enough agile to build the most relevant servicechains.

Thereby, to deal with these two problems (i.e., scalability and agility) onsupporting collaboration among a large number of heterogeneous businesses, wefocus on enterprise alliance formation in a SOA platform. In this paper, weclaim that a service chain should be configured for better understandability onSvCM. Given a service network, we can apply network analysis methodologieswhich have been introduced in physics and sociology,30) and extract meaningfulpatterns (e.g., distance, centrality, betweenness, and so on) from a given servicenetwork. Especially, semantic enterprise alliance has been introduced in 1,5,11,14).The common goal of such semantic approaches for business alliances is to au-tomate interoperability processes between heterogeneous businesses which are

providing various information by referring to their own knowledge structures(e.g., database schema and ontologies).9) We refer to ontology-based SvCM as aprocess to manage sharable services annotated by either standard metadata (e.g.,


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126 J. J. Jung

BPML,4 and WPDI-XPDL5) or domain ontologies (e.g., BMO,6 BPEL4WS,7

and MWSAF8) of businesses.Thus, the main research questions of this study are i) how to discover

meaningful relationships between services and ii) how to apply them to buildthe optimized service chain for a given event. Especially, in the context of valuenetwork,2) we have to consider more general case where a number of differentbusinesses are participating in an enterprise alliance, as shown in Fig. 1. Sincesuch relationships between services will be exponentially increased, it is verydifficult for human experts and administrators to manage and understand theservices for a variety of service-oriented processes (e.g., building new services).It means that a service from a business has to be automatically compared withother services from different business for finding out how they are interrelatedwith each other (e.g., semantic relationships). Consequently, once we somehow

have a comparison result attached with a certain relationship, a new service canbe generated by composing two (or more) of the compared services.

The outline of this paper is as follows. In the following Sect. 2, we intro-duce a definition of service network. Sect. 3 presents network analysis methodsfor discovering useful structural patterns from service networks. In Sect. 4, wedescribe semantic interoperability dealing with the problem of semantic hetero-geneity between businesses for service composition, and show a simple example.Sect. 5 and Sect. 6 will give an experimental results, and discusses some signif-icant issues and compares our contributions with the previous studies, respec-tively. Finally, Sect. 7 draws our conclusions of this work.

2 From Enterprise Network to Service Network

Generally, enterprise networks for collaborations among online businessestend to be usually static and consistently fixed. Such networks are made of notonly standard middleware communication channels (e.g., EDI), integrated secu-rity packages (e.g., public key infrastructure), but also database integration tools(e.g., IBM WebSphere Message Broker, SAP Exchange Infrastructure, MicrosoftBizTalk Server and Oracle Enterprise Service Bus). These collaborations havebeen done with mutual agreements, market brokers and strategic partnerships.

Definition 2.1 (Enterprise agreement)Given a set of enterprises B, various Service-Level Agreements (SLA) can beestablished between two arbitrary enterprises. According to levels, there areCorporate-level SLA (CCor), Customer-level SLA (CCus), Service-level SLA(CSer), and Multilevel SLA. Thus, in this paper, enterprise agreements can be

represented asRM = {CCor , CCus, CSer}. (1)

4 Business Process Modeling Language. http://www.bpmi.org/5 Workflow Process Definition Interface Language. http://www.wfmc.org/6 Business Management Ontology. http://www.bpiresearch.com/Resources/7 Business Process Execution Language for Web Services. http://ifr.sap.com/bpel4ws/8 METEOR-S Web Service Annotation Framework19)


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In practice, enterprises can exploit Web Services Agreement Specification (WS-Agreement9) to represent them.

Definition 2.2 (Enterprise network)An enterprise network of a enterprise alliance NB is represented as

NB = B, M , RM (2)

where B is a set of enterprises which join to this enterprise alliance, and M |B| |B| means a set of direct partnerships between enterprises which are man-ually established in real world. Additionally, RM is a set of agreement typesmade between the corresponding enterprises.

In this paper, we assume that an enterprise alliance is based on SOA com-puting environment. It means that these enterprises in the enterprise alliance

have to describe the services that they open and provide to any other enter-prises. These service descriptions are advertised to the others. There shouldbe a standard language (e.g., WSDL) to make others understand, as shown inFig. 2.

Fig. 2 Describing Services with Multiple Ontologies; Concepts canbe derived from local ontologies for semantic annotation ofthe services. Also, the dotted arrow between c3 FT1 andc2 FT2 indicates the manual alignment declared by human

experts.

Furthermore, this services can contain semantic information (e.g., ontolog-ical elements) extracted from their local ontologies. This process is also referredto semantic annotation of business processes.13,26) Even though there are manydifferent definitions on ontologies, in this study, we choose a simplistic approach

9 http://www.ogf.org/


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128 J. J. Jung

on ontology engineering by merging a set of faceted taxonomy.11,25) The facetedtaxonomy can include various domain knowledge (e.g., product catalogue) whichis composed of a set of classes.

Definition 2.3 (Faceted taxonomy11))Given an enterprise Bk Bparticipating in an enterprise alliance NB, a facetedtaxonomy F Tk of Bk can be defined as a set of subclass assertions betweenclasses Ck. Hence, F Tk is given by

F Tk = {ci, subc, cj|ci, cj Ck, cj ci} (3)

where ci means a superclass of cj.

Once the faceted taxonomies of the parties in the enterprise alliance arecollected, they are merged with each other and regarded as an enterprise alliance

ontology.

Definition 2.4 (Ontology)An ontology OB of an enterprise alliance NB is built by aggregating a set offaceted taxonomies. Suppose that a set of enterprises B = {B1, . . . , BN} becomprised in an enterprise alliance. Thus, the ontology OB is simply formulatedby

OB =

BkB

F Tk. (4)

Surely, since we want to remove the duplications, there should be someprocess to discover alignments between the faceted taxonomies. More impor-

tantly, given two taxonomies (i.e., F Ta and F Tb) from two arbitrary enterprises(i.e., Ba and Bb), domain experts can manually assert alignments

AB = {cp,rel, cq|cp F Ta, cq F Tb} (5)

where rel indicates the semantic relationship (e.g., equivalence and subsump-tion) declared by the human expert. The mapping can be expressed with variousrelations between classes in different faceted taxonomies. It is illustrated as adotted arrow (between c3 F T1 and c2 F T2) in Fig. 2.

Thus, by using the ontology, the services provided from the enterprisealliance can be semantically annotated.

Definition 2.5 (Service)A service can be simply described by semantic annotation process. Thus, we

assume that a service s from Bi is represented ass = {ck|ck OB} (6)

where these concepts are derived from the enterprise ontology OB.

Practically, there have been many kinds of software APIs to annotateWeb services. In this work, we have employed SAWSDL4J API provided from


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METEOR-S framework.10 For example, in Fig. 2, some concepts relevant toService s3 are extracted from the ontology O (i.e., c1, c3, c5 FT1 and c5,c6 FT2), and they are attached to the corresponding service. Consequently,services with semantics are expected to show better interoperability on SOAenvironment. More detail on this issue will be explained in Sect. 4.

Given a certain event (or goal), decision makers have to figure out whichservices are necessary to execute, and how the services are sequentially con-nected. Thus, we want to build a service chain to represent all possible relation-ships between services provided by the enterprise alliance.

Definition 2.6 (Service chain)Given an enterprise alliance, a service chain NS is defined

NS = S ,E,R (7)

where S is a set of services supplied by the enterprise alliance, and E S Sis a chain matrix, meaning a set of relationships between services. Additionally,R = {,,,,} is a set of semantics for describing the semantics of servicerelationships.

By referring to the semantic annotations of the services, we can determinewhether two services are semantically related with each other or not. Practically,it is difficult for the software systems to automatically discover the relationshipsbetween services. As alignments between faceted taxonomies can be done byhuman experts, the service relationships can be attached by human expertsby referring to semantics from ontologies as well as their own experiences andknowledge.

S1

S2

S3

S4

S4

S5

Fig. 3 An example of service chain with five services (s1 to s5) andone auxiliary service (s4)

For example, Fig. 3 depicts a simple example of service chain composedof five services (i.e., from s1 to s5). When an enterprise join and provide one

auxiliary service (i.e., s4), the service chain should be expanded by taking intoaccount additional relationships with s4. Suppose the goal is to obtain theoutput from s5. By using the auxiliary service s

4, we may be able to obtain

better results.

10 http://lsdis.cs.uga.edu/projects/meteor-s/


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130 J. J. Jung

3 Service Chain AnalysisVarious measures have been proposed on social networks between people

designed from social network analysis30) and from semantic social network.14)

This is based on topological analysis on the graph-structured information spacesto discover hidden knowledge underlying the networks. Eventually, we can real-ize who is the most important person in the social network.

In this context, once we have built a service chain, a number of thosesocial network analysis methods can be exploited. Then, we can be aware ofimportance of individual services on the service chain. This paper claims thatthis information is useful for formatting enterprise alliances. Note that thesemeasures apply only if the service chain is connected with direction. Thesemeasures are often normalized (between 0 and 1) but we present their simplestform.

Shortest path distance (SPD) Given two arbitrary services s and s in aservice chain, we can find out a shortest path SP between them,and also measure the geodesic distance of the path. It is denoted asSP D(s, s). It can be computed by repeating multiplying the chainmatrix E. The larger SP D value between two services is indicatingthe poorer relatedness between them.

Closeness centrality The inverse of average length of the shortest path be-tween a service s and any other services in the service chain is givenby

Closeness(s) =1

s,e,rN SP D(s, s)

(8)

where N indicates a service chain of the given enterprise alliance.Betweenness centrality7) The proportion of shortest paths between two ser-

vices which contains a particular service (this measures the power ofthis service) is given by

Betweenness(s) =

s=s=sN

s,s(s)

SP(s, s)(9)

where s,s(s) (by Bellman criterion3)) indicate the number of short-

est paths p(s, s) SP(s, s) that service s N lies on.

Hub and authority There are different but interrelated patterns of power: i)authorities that are referred to by many good hubs, and ii) hubs thatrefers to many good authorities. The highest authorities are thosewhich are referred to by the highest hubs and the highest hubs thatthose which refers to the highest authorities. Kleinberg16) proposesan iterative algorithm to measure authority and hub degree of eachentity in interlinked environment. Hence, given initial authority andhub degrees of 1, the degrees are iteratively computed by

Hubt+1(s) =

s,e,rNi

Autht(s) and (10)


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Autht+1

(s) = s,e,rNi

Hubt(s) (11)

where SP D(s, s) = 1.

Similarly to betweenness, the hub weight indicates the structural positionof the corresponding service.15) It is a measure of the influence that serviceshave over the spread of information through the service chain. From a givenFig. 1, we can measure various measurements of each enterprise (or each serviceby an enterprise). As a simple example, with respect to the closeness (|B| =10) in Eq. (8), the closenesses of Goverment&Policy Makers, R&D Labora-

tories, and Health Providers are9

1 + 2 + 2 + 2 + 3 + 3 + 3 + 8 + 8= 0.28,

9

1 + 1 + 2 + 2 + 2 + 3 + 3 + 4 + 8

= 0.35, and9

1 + 1 + 1 + 2 + 3 + 3 + 2 + 1 + 2

=

0.56, respectively (Distance from unreachable services is assigned with N 1).Thus, we can guarantee that Health Providers has been located in more im-portant position rather than the others.

4 Interoperability by Enterprise AllianceSemantic heterogeneity problem between businesses is caused by several

reasons. Formation of the knowledge are semantically distinct with each other,because the knowledge are designed by experiences and heuristics of the lo-cal experts (or administrators). It means that semantic information extractedfrom the knowledge may be heterogeneous with the others. Such heterogeneitiesare caused by the difference of not only the terminologies (e.g., synonyms andantonym), but also, more importantly, the knowledge structures (e.g., database

schema8) and ontologies11)). Consequently, it is difficult for the enterprises to beintegrated, and more importantly, it is impossible for the enterprise alliances toautomatically achieve strategic cooperations (e.g., i) business rules, e.g., strate-gies and policies, and ii) hierarchical taxonomies for describing the resources)with heterogeneous enterprise alliances.

In order to overcome this drawback, we have focused on semantic inter-operability between virtual enterprises.11) A large number of enterprises havebeen inter-related with the others in a same enterprise alliance or different en-terprise alliances for performing ad-hoc (or real-time) collaboration. In orderto provide efficient interoperability between the enterprises, the heterogeneitiesbetween the corresponding ontological knowledge structures have to be dealtwith. Thereby, we have to consider efficient alignment method to resolve their

conflicts. While intra-alignment is a process merging all local ontologies intoan organizational ontology, inter-alignment is a process mapping all semanticcorrespondences between two organizational ontologies.

We have proposed an efficient method to build an integrated enterprisealliance by mapping heterogeneous ontologies of enterprises, i.e., maximizing thesummation of partial similarities between a set of possible pairs of classes. Thepartial similarity can be calculated by comparing both set of instances in theclasses. After both ontologies are aligned at conceptual level, and the source


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ontology instances are transformed into the target ontology entities according tothose semantic relations.

4.1 Discovering Semantic Relationships between ServicesFor unveiling the relationships between services, we have to figure out the

relationships between the corresponding descriptions (i.e., concepts). Thus, wehave to conduct ontology matching process. After ontology matching process,11

the alignments between heterogeneous ontologies can be represented as a set ofpairs of concepts from two different ontologies. We refer these concept pairs tocorrespondences (e.g., equivalence or subsumption).

Definition 4.1 (Alignment)Given two ontologies F Ti and F Tj , the alignments between two ontologies are

represented as a set of correspondences CRSPij = {c,rel,c

|c F Ti, c

F Tj}where rel means the relationship b etween c and c, by maximizing the summationof class similarities.

Finally, alignment process makes heterogeneous enterprise alliances inter-operable (even partially) among them. For example, local users in an enterprisealliance can easily and transparently access to the other enterprise alliances. Todo so, enterprise alliances have to conduct the ontology matching process in ad-vance. Suppose that a set of enterprise alliances B= {B1, . . . , BN} should be in-teroperable with each other. Alignment process can find out the correspondencesbetween all pairs of ontologies, i.e., Bi obtains N 1 sets of correspondences.

Table 1 Service Relationship Discovery by Semantic Matching ProcessScope Service description Semantic relationships

In a same ds = ds s s

enterprise ds ds s s

alliance ds ds = not decidableIn a different ds = ds , ds ds , ds ds = not decidable

enterprise {c,, c|c ds, c ds} CRSPij s s

alliance {c,, c|c ds, c ds} CRSPij s s

Most importantly, given two services s from Bi and s from Bj in a seman-

tic SvCM, the relationship between both of them should be discovered. Table 1shows a simple example of patterns for establishing relationships between ser-vices. Certainly this table can be expanded, according to the strategies on theSvCM.

4.2 ExampleIn this section, we want to show a simple example based on service net-

work analysis methods. While on a conventional marketplace with online andoffline enterprises, the enterprises are interlinked with each other by mutualagreements and contracts (e.g., supply chains), we have been considering in-tegrating and merging the link-based structures from several business sectors.

11 We skip ontology matching processes. Please refer to other literatures 23) for more details.


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When we need to find out the best service chain for achieving a certain goal(i.e., sequentially aggregating enterprises until customers), the best one shouldbe selected out of a set of all possible service chains by taking into account thesemantic interoperability between the enterprises.

As shown in Fig. 1, manufacturing industry sector (e.g., equipment sup-pliers) can be automatically integrated with medical producer sector (e.g., phar-macy wholesalers). Moreover, if they have semantic-based information systemson open networks, we can obtain semantic relationships between such enterpriseslocated in different sectors. For example, by matching pairs of ontologies,

S(O(Medical Equipment Supplier), O(Health Wholesalers)) = 0.64 S(O(Other Equipment Supplier), O(Pharmaceuticals Supplier)) = 0.33

we can realize that among all possible service chains from R&D Laboratories

to Customers, Medical Equipment Supplier and Health Wholesalers ismore closely related with each other, compared to Other Equipment Supplierand Pharmaceuticals Supplier.

5 ExperimentationWe have evaluated our contributions of this paper by considering two

main issues; i) human evaluation of building service chain networks, and ii)performance evaluation (i.e., scalability) of discovering the best service chainwith a certain event in a dynamic computing environment.

5.1 Mobile Advertisement: a Case StudyTo test the proposed SOA platform, we have selected a mobile advertise-

ment system as a case study. As mobile devices (e.g., cellphone) have been widelyused, many businesses have been trying to send advertisement to customers forincreasing their profits and revenues. Moreover, they are getting focusing onlocation-based advertisement. Once they are aware of the context (i.e., loca-tion) of the target customer, they can choose the most relevant advertisements.

However, it is difficult for the advertisement systems to send the informa-tion. They need to consider various conditions of their partners in the enterprisealliance. Thereby, the proposed SOA platform can discover which services (andservice providers) are most relevant to the customers by analyzing the servicechain network.

Table 2 Specification of the Testing-bed for the Proposed SOA PlatformEnterprise alliance MA1 MA2 MA3 Total

Number of enterprises 16 14 13 43Number of services 153 94 137 384Average number of services 9.6 6.7 10.5 8.9

Thus, as a testing-bed, we have collected 384 services by interviewingwith 3 mobile advertisement companies (i.e., MA1, MA2, and MA3) in Korea.We can consider each of the advertisement companies as an enterprise alliance.Table 2 is showing the specification of the test-bed for the service chain network.


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In average, average number of services of each enterprise is 8.9, while it is 10.5in MA3. It means a service chain network of MA3 is the densest one.

Regarding the ontologies, we have asked the enterprises to build theirown faceted taxonomies. The human experts from each alliance have manuallyintegrated them, and finally, we have collected three ontologies.

5.2 Experimental ResultsTo evaluate scalability of the proposed SOA platform, we have measured

the computation times of discovering the service chains. We have exploitedbetweenness to find the service chains and compare it to brute force approach.

Figure 4 shows computation time in three enterprise alliance, as the num-bers of enterprises get increased. We have found out that the proposed SOAplatform has outperformed in all the alliances (by 23%, 19%, and 22%). Es-

0

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Brute force

++++++

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+

+

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++

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Brute force

+ +

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Fig. 4 Scalability Testing on Three Enterprise Alliances


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pecially, mobile advertisement company MA1 has shown the best performance.Since the enterprises in MA1 are in the similar industrial domain (i.e., enter-tainment), their ontologies are strongly connected with each other. As a result,the service chains have been efficiently managed, even though the number ofenterprises is larger than those of the others.

6 Discussion and Related WorkHere, we want to put some discussion about the Web Services. Web

Services have been regarded as one possible way of realizing the technical as-pects of the so-called SOA (service-oriented architecture). These services can benew applications or just wrapped around existing legacy systems to make themSOA-enabled. Common technologies for developing web services are WSRF,12

SOAP,13 UDDI14 and WSDL.15 Furthermore, when using these technologies,

XML is a basic technology for developing web services this way. For reasoningaspects, a Web Service is interesting if several reasoning components are avail-able and accessible through the use of indexes possibly managed by other entity(a broker). A user is able to request a specific reasoning component by checkingthe indexes of the storage. For this, three instances can be identified, a serviceconsumer, a service provider and a Service Broker (storage of indexes).

There have been several important research issues on dealing with seman-tic matching between information systems. For doing this, many studies havebeen proposed to provide interoperability by discovering and integrating localknowledge structures between VOs.5) They can be briefly noted into three issues;

Incremental discovery of local knowledge,10)

Knowledge matching (including schema and ontology matching),23) and

Interoperability via third-party platforms, e.g., service-oriented architec-ture (SOA).27)

Moreover, human understandability is also important problem for takingcare of a large-scaled services and resources. In fact, in this work, we are focusingon supporting local users (e.g., decision makers) through aligning the ontologiesapplied to annotate (or classify) the services on enterprise alliances. It meansthe local users in a certain enterprise alliance can access to the other enterprisealliances which are not familiar with them. Unlike a centralized portal systems(e.g., meta search engines), the local users can be provided with a set of conceptmapping extracted from direct alignments, so that they can deploy meaningfultranslation services (e.g., query expansion20) and transformation).

We can think of some related work which should be compared with the

proposed work. Third party logistics31,32) is an important domain to consider theservice integration and composition for optimal solutions. Similarity, on-demande-supply chain integration12,29) has proposed a real-time approach to solve the

12 Web Service Resource Framework13 Simple Object Access Protocol14 Universal Description, Discovery and Integration15 Web Service Description Language


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136 J. J. Jung

service heterogeneity problems.These systems have been employed to a various domains like geographi-

cal location-based system,33) competitive partner selection,21) and collaborativeservice chain.24)

7 Conclusions and Future WorkServices have been regarded as a key factor on business success. In the

context of information engineering domain, a large amount of information from(and to) enterprises should be efficiently processed and manipulated to maximizethe values by integrating relevant businesses together. Particularly, service-oriented architecture (SOA) is regarded as an efficient platform to exchangeservices (e.g., publishing and subscribing services) between enterprises. WithinSOA platforms, XML-based standards have been employed by the enterprises.

Recently, service-dominant logic18) has been significantly emphasized onmany researchers in various domains of management, social, and engineeringscience.17) This paper is a theoretical paper for introducing a basic idea of ser-vice network analysis. We have presented a conceptual framework to integratemultiple service networks which had been isolated only in individual businesssectors into a global service network. Hopefully, the services can be annotatedwith business ontologies, so that the ontology alignment algorithms are effi-ciently applied to find out the relationships between services. In addition, wewant to note that the services mentioned in this paper is derived from online en-terprises as well as from offline enterprises. More importantly, traditional socialnetwork methods can be applied to understand the topological patterns fromthe integrated service networks.

As future work, we want to describe research limitations and problemsthat we have been realizing during this study as follows;

Legacy problem: It is difficult for offline legacy enterprises to put seman-tics into them. We are expecting some machine learning approached todeal with this issue.

Semantic description of services: There have been several service ontolo-gies and service metadata.

Human understandability: A study and system on service visualizationor service network visualization are needed to increase understandabilityof human users.

Acknowledgements This work was supported by the National ResearchFoundation of Korea (NRF) grant funded by the Korea government (MEST)(No. 2011-0017156).

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Jason J. Jung, Ph.D.: He is an assistant professor in Yeungnam

University, Korea, since September 2007. He was a postdoctoral

researcher in INRIA Rhone-Alpes, France in 2006, and a visit-

ing scientist in Fraunhofer Institute (FIRST) in Berlin, Germany

in 2004. He received the B.Eng. in Computer Science and Me-

chanical Engineering from Inha University in 1999. He received

M.S. and Ph.D. degrees in Computer and Information Engineer-

ing from Inha University in 2002 and 2005, respectively. His

research topics are knowledge engineering on social networks by

using machine learning, semantic Web mining, and ambient in-

telligence.

dynamic soa framework to support ad hoc enterprise alliance formation

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