Net-centric Service-oriented Enterprises

1. Net-centric Service-oriented Enterprises Bina Ramamurthy Chapter 1 of The Semantic Web book

2. Introduction • Werservices Mashups Service-oriented architectures What next? • Web is still a set of static and dynamically generated web pages linked together. • Usually coded in html and meant for human consumption. • Web information need to be used not only display purposes but also for interoperability and integration between systems and applications: XML is a solution that partially addresses this need. • In order to enable machine-machine exchange and automated processing we need to provide information in such a way that machines can understand. • New standards and languages are being investigated and developed to give meaning to web information. • Examples: RDF (Resource Description framework), OWL (Web Ontology Language) • Improve expressiveness of the web, allow automatic and semiautomatic processing of web resources and web pages. • Answer to “What next?”: Semantic Web Services for a given business/industrial domain

3. Topics for Discussion • Creating and using semantic information • Ontologies: Cornerstone of Semantic Web Services and service-oriented enterprises • Characteristics of a new world • Challenges for service-based applications • Importance of semantics for organizations • Semantic Service Oriented Architecture • Ontologies + Ontology management system

4. Semantic information • Discover, acquire, and create metadata for unstructured, semi-structured, and structured information • Reason, interpret, infer and answer using semantics • Represent, organize, integrate resources, content and knowledge using semantics • provision, present, communicate and act using semantics • Provide machine-machine semantic interface, human-semantics semantic interface

5. WS and SOA and Semantics (Web) HTML W XML WS SOA SWS SW OWL

6. Ontologies in Business • Provide formal support for communication between agents and exchange of knowledge. • In the context of human communications, it aims at reducing and eliminating terminological and conceptual confusion. • Unifying framework enabling cooperation amongst people in reaching better inter-enterprise organization.

7. Examples of successful Ontologies • Healthcare hierarchical and controlled vocabulary for human disease representation • Food and agriculture organizations of the United Nations FAO • Data management and interchange between enterprises: Open EDI for business transactions • Scientific Computing • Knowledge representation ontologies.

8. Characteristics of a new world • Information and knowledge are key enablers of business and economic performance and sustainable development. • Globalization: creation and consumption of knowledge and information are made in the global context. • Exploitation of synergies and capacities beyond boundaries • Realization of new opportunities • Understanding of threats • Human and social networks • New levels of performance

9. Characteristics: Business networking • Business and economic activities as well as competition, require new models of business networking. • Advanced documentation of skills and competencies • Newer business models: Example: IF.com’s banking product • Context-based collaboration define new demands for advanced business networking at global level

10. Characteristics: Shared Models • A global consensus toward peace, development, health and prosperity needs to be based on shared conceptual models that addresses issues on global scale. Examples: • Global warming • Mars exploration • Financial and manufacturing sectors • Global AIDS initiatives • Global information landscape shared models are required for interoperability, exploitation of collective intelligence.

11. Characteristics: Collective intelligence • Apply collective intelligent filters or collaborative filtering in the context of global information world. • These may challenge the traditional models of business performance, marketing and profitability. • Example: Financial domain: once again revisit IF.com

12. Characteristics: Open Paradigm • Open paradigm relates with several complimentary movements: • Open source software • Open content • Open access • Open knowledge • Open research • Open culture • Result: amazing capacity to support new business models and several application models. • Example: amazon EC2, cloud computing, map reduce

13. Challenges for Semantic Web Services-based Systems: Summary • Definition of new modes of human, knowledge and business networking beyond local boundaries: well defined conceptual models that match information sources and human services. • With ontologies and social networks as anchors • Process and service-oriented infrastructure • Globalizing information and definition of new contexts for value exploitation: • Design of multiple reference levels to the same set of information and knowledge delivers a new level for dynamic, and personalized systems. • Internationalization

14. Challenges… • Delivering and integrating quality to information: • Enormous explosion of content while quality is very subjective concept • We need infrastructure that deliver assessment models of information quality. • Integration of isolated information assets: build more meaningful services. SOA can help in this aspect. Lets discuss how? • Support of business value and co-located distributed business models: crucial aspect is to translate web semantic ontologies to business models. SOA can help bridge this gap. How? • Promotion of a critical shift in human understanding and interacting with digital world: Web needs to respond to human demand for richer modes of meaningful, useful and productive interaction. Combination of semantics and SOA can help here.

15. Importance of semantics and services (SOA) to organizations • Integration is the top priority for many worldwide enterprises. • Inter, intra and human interface integration. • Cross-organization cooperation in small and medium enterprises (SME). • Semantics and service combination can facilitate discovery of heterogeneous components, data integration and communication. • Semantics SOA is most suitable for business-business interaction and in integration of e-business value chains. Ex: amazon.com market place, yahoo.com

16. Ontology Management System (OMS) • Supports entire lifecycle of inter-enterprise ontologies, including creation, storage, search, query, reuse, maintenance, and integration. • An OMS needs to address a wide range of problems: ontology models, ontology base design, query languages, programming interfaces, query processes and optimization, federation of knowledge sources, caching and indexing, transaction support, distributed system support, and security support.

17. Overall Challenge • High volume and wealth of data and information generated by the numerous web applications that needs to be analyzed and processed to provide useful and timely knowledge for decision makers. • Arcelor Mittal: 330000employees, 60 countries, flat steel products. How to extract knowledge from the information generated?

18. Contributions of the text chapters • Important industries (vertical domains) covered: semantic enterprises, finance, government, healthcare and life sciences, education, business and customer management, enterprise management and security. • Highlights in the context of actual industry, the full range of business and technological issues that must be addressed. • Provides a comprehensive discussion of the required integration of semantic web services (SOA) and business strategies. • Sets a context for critical thinking.

19. Project ideas • Read the text • Choose a vertical domain that appeals to you and that is familiar to you. • Form your project group of two. • More directions will be given in Assignment#1

20. Chapter 2: Semantic Enterprises • For a concept to be widely adopted it needs to reach a level of maturity. • Semantic web is a new concept that still has some distance to go before it reaches a point of this widespread adoption. • Lets examine how semantic web (tools and technologies) can help address some of the challenges that companies are facing today.

21. Topics for Discussion • The Business context • Tools and Technologies for representing semantics • Software for semantic services • Use cases for semantic representation of information: • Recruitment services • Agile manufacturing • Patterns and insights in data • Integration of scientific data • Enterprise search and navigation • Compliance and regulation • SOA metadata

22. Business and Technology Drivers: The Context • Read Section 2 of Ch.2 • Commercial organizations are always under pressure to perform financially. • Growing interest in being able to integrate all data related to the core components that drive their success. • Integrate not only structured data but also huge volume of unstructured data collected and generated. Ex: explosive email • Many industries are moving towards collaborative business models. Ex: drug discovery and clinical trials • Companies conduct businesses in many countries. • Integrating data across department also comes with its challenges. • Ability to respond rapidly to change. • Data is the most important asset and access to it should be controlled. Provide API for access and build revenue models around it. • Effective use of business data and change/adapt as needed.

23. Tools and Technologies: RDF • Resource Description Framework (RDF) is a core semantic web recommendation from W3C. • Represents data using triplet: subject-predicate-object

24. Tools and Technologies: OWL • OWL (Web Ontology Language) is a more expressive language once a a standard from W3C. • It provides ways to define classes and instances and relationship for modeling real-world objects. • <owl:Class rdf:ID="PotableLiquid"> <rdfs:subClassOf rdf:resource="#ConsumableThing" /> ... </owl:Class> • SPARQL is a query language for RDF and OWL.

25. Software • Databases, middleware and applications must be enhanced to work with RDF, OWL and SPARQL. • Remember most of today’s data is in relational databases and in XML formats. So we need converters or interfaces to bridge this gap. • Pages 21-22 has a excellent collection of software initiatives in this direction. • Bottom line is that we need to pay attention to data representation in order to build an efficient SOA. • On to use cases..

26. Recruitment Services hireme.com Geo:4930956 hireme:candidate101 hireme:candidate102Job1 hireme:candidate102Job2 hireme:candidate102Job3 RDF..

27. Recruitment Services • To enable querying across multiple recruiter databases, the hiring company would encourage all its recruitment agencies to make a subset of their data available in RDF. • Common vocabulary, SPARQL endpoint • OWL could also be used • Conversion to legacy relational info into OWL or RDF • Expose recruitment companies services too.

28. Agile Manufacturing • Business drivers: • Reuse machines • Use ingredients in multiple products • Follow trends and latest food craze • FDA labeling regulations • Enterprise resource planning: incorporate ontologies • Scheduling supported by food ontologies. • Services and semantics will make it easier to incorporate new data that is deemed relevant and help in decision making.

29. Identifying of Patterns and Insights in data • Business drivers: • Non-structured data : reports, email • Need to mine this data • Use natural language to extract triplets and store as RDF which can then be queried. • Association of semantics and services will make the querying this RDF or OWL database very efficient. • Oracle database supports RDF and OWL. • Java APIs are available for querying patterns.

30. Integration of Scientific Data • Business driver: • Drug discovery and development is very expensive and time consuming process • For a drug to get from bench to market takes 5000 screened compounds, 15 years and nearly $1 billion. • Desire to eliminate late stage attrition: identifying and eliminating drugs that do not have the desirable safety profiles. • Need to be aware of competitive offerings or patents to access market potential • Solution: semantic data integration of heterogeneous databases and services for semantic queries

31. Integration of scientific data (contd.) • Data types include: chemical structures, biological sequences, images, biological pathways, clinical observations and scientific papers. • Data warehouse is NOT a solution. • We need a unified view with no ambiguity in terms.. GSK protein needs to different than GSK the company name.. • This would allow biological mashups for discovery and decision making.

32. Enterprise Search and Navigation • OTN (The Oracle Technology Network) is the main source of technical information for oracle developer community. • Web site provides access to product documentation, product releases, software downloads, etc. Richness, complexity and dynamism of the information made it challenging for traditional search. • Oracle worked with Siderean and created a semantic web: http://otnsemanticweb.oracle.com

33. Compliance and Regulation • Increasing complex set of regulations by such congressional acts such as SARBOX and HIPPA. • Policies can be implemented using semantic web. • Semantics and services can keep trace and verify compliance.

34. SOA Metadata • Semantics can be used to assign metadata that will help in true dynamic discovery, invocation and composition. • Thus semantics can improve inherent flexibility of SOA infrastructure.

35. Summary • We understand that incorporating semantics into the services infrastructure can help advance SOA goals. • Future designs should consider both semantic web concepts and SOA concepts.