SSOA Implementation
What I've discussed up to this point might lead you to the conclusion that to implement SSOA, you must create a domain-specific ontology, semantically enhanced services, and provide a process-aware mechanism, such as a Business Process Execution Language (BPEL) engine, that leverages the ontology and the semantic extensions to build business processes. RDF and OWL arise, therefore, as a means to enable SSOA.
Semantically enhancing services implies including metadata into the service definition. This can be handled within the service definition or within the service discovery infrastructure. In a web services environment, extensions to WSDL may be used, or the UDDI can be extended to provide additional semantic-level detail.
As suggested by the desire for BEM and CEP, the ultimate goal of SSOA is dynamic process creation and management. Deterministic, statistical, and hybrid approaches may be used to create processes upon deployment or at runtime. A number of approaches are being studied.
Deterministic process creation can be represented in an activity diagram and achieved by creating process templates using a business-process-specific language, such as a BPEL or Business Process Modeling Language (BPML). The process template specifies start and end states, and a sequence of activities that occur during the process. The service implementations are not defined in the template but are bound to services at deployment time or runtime.
The service-binding mechanism can use a ranking system to determine the best service for the activity in the given context. As an example, the relative importance of properties of the resource (see RDF) can be weighted. A weighted sum over the properties leads to the best candidate service for the activity. In this fashion, properties such as QoS can be defined to select the appropriate service.
Statistical process creation intends to discover processes through inferencing of business events. In this approach, the candidate processes are determined using input/output information and probabilities based on business event correlation. Given the input information, probabilities are calculated for activities that may satisfy the output requirements. This requires ontological reasoning services such as Bayesian networks or case-based reasoning. The topic remains an area of active debate and research.
Conclusion
While it is clear that SOA can be seen as a reinvention of older programming concepts at the enterprise level, the introduction of semantic processing is a more complex endeavor that broaches mathematics and artificial intelligence to allow machine-based analysis and interpretation. The foundations of the latter have been provided through efforts in the Semantic Web, and more generally, semantic analysis in other fields. In the business arena, the use of reasoning engines as part of real-time processing remains in a nascent state. Institutions in academia and industry are taking the lead in semantically enabling business technology.
For More Information
Rong Pan, Zhongli Ding, Yang Yu, Yun Peng. "A Bayesian Network Approach to Ontology Mappings" (ebi.seu.edu.cn/ISWC2005/papers/3729/37290563.pdf).
Rama Akkiraju, Richard Goodwin, Prashant Doshi, Sascha Roeder. "A Method for Semantically Enhancing the Service Discovery Capabilities of UDDI" (www.isi.edu/infoagents/workshops/ ijcai03/papers/Akkiraju-SemanticUDDI-IJCA%202003.pdf).
Alexandra Galatescu, Taisia Greceanu. "Ontology-Driven Improvement of Business Process Quality" (www.i3s.unice.fr/odbis2005/Ontology-driven%20Improvement.pdf).
Martin Hepp, Frank Leymann, Chris Bussler, John Domingue, Alexander Wahler, Dieter Fensel. "Semantic Business Process Management: Using Semantic Web Services for Business Process Management" (dip.semanticweb.org/documents/Hepp-et-al-Semantic-Business-Process-Management-Using-Semantic-Web-Services-for-Business-Pro.pdf).
Mathieu d'Aquin, Jean Lieber, Amedeo Napoli. "Decentralized Case-Based Reasoning for the Semantic Web" (www.loria.fr/equipes/orpailleur/Documents/daquin05b.pdf).
Juhnyoung Lee, Richard Goodwin, Rama Akkiraju, Anand Ranganathan, Kunal Verma, SweeFen Goh. "Towards Enterprise-Scale Ontology Management" (uk.builder.com/whitepapers/0,39026692,60132313p-39000926q,00.htm).
Pavel Hruby. "Ontology-Based Domain-Driven Design" (www.softmetaware.com/oopsla2005/hruby.pdf).
Brand Neimann. "Data Reference Model: Update on Status." (web.gov/scope03072005.ppt).
Paulo Cesar G. da Costa, Kathryn B. Laskey, Kenneth J. Laskey. "PR-OWL: A Bayesian Ontology Language for the Semantic Web" (ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-173/paper3.pdf).
Shashi Kant, Evangelos Mamas. "Statistical Reasoning: A Foundation for Semantic Web Reasoning" (ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-173/pos_paper6.pdf).
