By Jeffrey T. Pollock
- New Paradigm for contemplating software integration and B2B difficulties
- Heightens the significance of conveying that means among structures
- Addresses flow within the EAI area towards extra information dealing with services
- Offers an answer for the multitude of managers disconnected with the most recent applied sciences
- Leverages the technical advances made in complicated information integration over 15 years
- Shifts the point of interest from know-how strategies to details suggestions
- Relies seriously at the use of sensible examples, suggestions, 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 power Case reviews (pages 259–304):
Chapter eleven Adoption thoughts (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
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Dynamic, adaptive middleware architectures will rely on semantic interoperability. D efining holistic middleware architecture may not be the first order of business for most CEOs, but cutting costs and improving business relationships surely are. So, although the question of semantic interoperability may be technical geek speak for most, software architects and CIOs should take heed of this emerging vision. It could make you a corporate hero. In this chapter we will examine the roots of application integration solutions and discover the reasons why they so frequently become money pits.
The modeler’s own background, combined with his assumed understanding of the organization, results in differences with the models. Although many patterns for good data modeling are understood, even the smallest of subtle differences in the models can result in absolute incompatibility among them. Even with packaged applications, culture and folklore of the company influence how they are used. Supply chain systems, financial systems, product data management systems, and all other ERP packages require significant amounts of customization to the business rules, data models, and application code.