By Jeffrey T. Pollock
- New Paradigm for contemplating software integration and B2B difficulties
- Heightens the significance of conveying that means among structures
- Addresses circulate within the EAI house towards extra information dealing with functions
- Offers an answer for the multitude of managers disconnected with the newest applied sciences
- Leverages the technical advances made in advanced info integration over 15 years
- Shifts the focal point from expertise ideas to info recommendations
- Relies seriously at the use of useful examples, advice, definitions, and soapbox excerpts during the major physique of textual content
Chapter 1 Semantic Interoperability supplies IT which means (pages 3–22):
Chapter 2 info Infrastructure matters and difficulties (pages 23–38):
Chapter three Promise of Frictionless details (pages 39–65):
Chapter four Foundations in facts Semantics (pages 69–98):
Chapter five Semantic clash resolution styles (pages 99–125):
Chapter 6 Metadata Archetypes (pages 127–144):
Chapter 7 Ontology layout styles (pages 145–194):
Chapter eight Multimodal Interoperability structure (pages 195–221):
Chapter nine Infrastructure and E?Business styles (pages 223–255):
Chapter 10 strength Case reports (pages 259–304):
Chapter eleven Adoption techniques (pages 305–331):
Chapter 12 Tomorrow's Adaptive and Dynamic structures (pages 333–346):
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Additional info for Adaptive Information: Improving Business through Semantic Interoperability, Grid Computing, and Enterprise Integration
This need will drive development of newer kinds of dynamic information infrastructures. To fill this gap, a richer method for working with enterprise information must be created based on enabling the semantic interoperability of network services and components. Ultimately, technology by itself doesn’t matter to the business—but information does—and as long as information matters, the evolution of information technology will, too. A, Roth. IBM Systems Journal, Volume 41, No. 4, 2002. 18 Chapter 1 Semantic Interoperability Gives IT Meaning Data Rich, Information Poor Nicholas Carr’s article on the death of data processing should serve as a wake-up call to vendors and proponents of the same old middleware technologies.
However, the rise of new technologies and approaches has already resulted in new rules and a new curve—information processing. Emerging technology approaches discussed in this book, such as the application of semantic tools, technologies, and methodologies, will deliver us into the technology world beyond data—toward a real information technology industry. 2 This S-curve example is similar to Clayton Christensen’s (Innovator’s Dilemma) concept of sustained technical change within a value net—in this case we are referring to the constant innovation in the IT industry as a whole, and suggesting that strategic competitive advantage remains possible by out-maneuvering competition and adopting new technology at the right time.
Until recently, most vendors and organizations weren’t concerned with this mushrooming diversity. However, as IT has progressively crept into most dimensions of our lives, the software asset base has expanded rapidly creating a software complexity crisis due to the large number of disparate standards, applications, languages, and datasets that were created without an eye toward interoperability and integration. The burden created by the software complexity crisis now means that most organizations spend more time maintaining and integrating existing applications than building or implementing new applications.