Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration (Wiley Series in Systems Engineering and Management) [Hardcover]

Jeffrey T. Pollock (Author), Ralph Hodgson (Author)

Book Description

Publication Date: September 21, 2004 | ISBN-10: 0471488542 | ISBN-13: 978-0471488545 | Edition: 1

• New Paradigm for considering application integration and B2B problems
• Heightens the importance of conveying meaning between systems
• Addresses movement in the EAI space toward more data handling capabilities
• Offers a solution for the multitude of managers disconnected with the latest technologies
• Leverages the technical advances made in complex data integration over 15 years
• Shifts the focus from technology solutions to information solutions
• Relies heavily on the use of practical examples, tips, definitions, and soapbox excerpts throughout the main body of text

Editorial Reviews

Review

"Overall, I found the book to be enjoyable and informative, with lots of references and pointers to areas of interest. The variety of topics discussed within the book encourages the reader to read outside of their core specialisms." (BCS, 1 November 2010)"

...an essential read for all Semantic Web practitioners-from CIOs to CTOs and architects to managers...a must-read for upper management." (IEEE Distributed Systems Online, August 2005)

From the Back Cover

Creating dynamic, adaptive, self-configuring enterprise infrastructures

As many traditional integration technologies prove inadequate to the tasks for which they were designed, it has become clear that any effective EAI (Enterprise Application Integration) solution requires a new approach to thinking about enterprise infrastructures. Adaptive Information provides that new approach. This groundbreaking guide introduces a revolutionary new framework for the infrastructure of the future: semantically aware; driven by models of information, process, and service interfaces; and able to learn new behavior, find patterns, and rebuild itself for different platforms.

Offering copious pragmatic examples of how semantic interoperability already fosters new capabilities, speeds up processes, and reduces costs, the authors provide a solid foundation in semantics and describe how metadata, ontologies, and software tools are used to provide semantic interoperability. Readers learn how to:

• Organize and architect a semantic interoperability project
• Make decisions about tools
• Choose the most appropriate architectural patterns and technologies
• Achieve new capabilities, including dynamic information repurposing, adaptive service-oriented networks, and loosely coupled application connections

Complete with plenty of helpful tips, definitions, and practical examples, Adaptive Information is must reading for CIOs and CTOs, IT managers, IT architects, and application developers alike.

See all Editorial Reviews

Product Details

• Hardcover: 440 pages
• Publisher: Wiley-Interscience; 1 edition (September 21, 2004)
• Language: English
• ISBN-10: 0471488542
• ISBN-13: 978-0471488545
• Product Dimensions: 9.6 x 6.1 x 1 inches
• Shipping Weight: 1.6 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars  See all reviews (8 customer reviews)
• Amazon Best Sellers Rank: #1,045,022 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

8 of 8 people found the following review helpful:

3.0 out of 5 stars Unable to clear up the 'fuzziness', February 13, 2006

By 

M (CA United States) - See all my reviews

This review is from: Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration (Wiley Series in Systems Engineering and Management) (Hardcover)

You'd think that a book attempting to explain semantic technologies would make an extra effort to be as clear and practical as possible so as not to make the subject even more confusing.

I think this book would have been better if it was written by only one author. Part of the book was written clearly and was obviously directed toward a reader who was not familiar with semantic (and other, related) technologies. This author clearly laid out various ways that semantic technology is (and can be) used. Information provided about current products and companies using this technology was also very informative.

Other parts of the book, however, were written as if the reader is just as familiar with the technology as the author. It seems as though one of the authors was more concerned about how they sounded (in writing) to themselves (and possibly their colleagues) than they were in attempting to give practical knowledge about the subject to an uninformed reader. It's difficult enough to understand the subject of semantic technologies without having to fight through densely-written sentences and unnecessarily vague buzz words (like "follow-on query"??).

I was interested in learning exactly HOW this technology enables interoperability "with little or no human intervention" but found no difinitive, practical explanation of how this is achieved. Yes, I understand that ontologies and inference engines are involved, but how do they work together to remove the human part of the middleware equation? How is the "mapping" involved in semantic technologies different from the "mapping" that is involed in today's EAI solutions? I thought that this is the most exiting and unique selling proposition of this technology but I finished this book unsatisfied with how this was explained. Perhaps this selling point is more of a vision than a reality?

8 of 10 people found the following review helpful:

3.0 out of 5 stars An ambitious but yet unfulfilled vision, December 12, 2005

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
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This review is from: Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration (Wiley Series in Systems Engineering and Management) (Hardcover)

How is the meaning in natural language related to the meaning in computational machines? The answer to this question is of course of immense importance to the information age, and the lack of an answer has resulted in vast financial commitments from businesses that depend heavily on information technology. The sharing of information between machines has been hampered by the lack of common understanding between these machines, since each one of them has its own way of formatting or conceptualizing the data. Even though some of these machines should definitely be characterized as `intelligent', there is of yet no machine that can communicate or share information with any arbitrarily selected machine in a manner that is independent of the nature of this information. There are some machines that are capable of interacting with many other machines in this manner, but at some point when confronted with a particular machine, they are unable to converse meaningfully without some amount of human intervention. This intervention must take place because the machines do not understanding the meaning of each other's data. They cannot conceptualize it without the assistance of a human, who must then give the appropriate (semantic) translation between the data patterns of the respective machines.

The authors of this book approach these questions in terms of what they have called `semantic interoperability.' This notion is supposed to settle the difficulties of meaning and definition that occur not only in natural language but also in communications between software applications. The context of words in digital systems is dependent on both domain cues and local cues, just as is the case for natural languages, they authors say. Data context for example influences the interpretation of data, and therefore it's meaning may change if viewed from another context or perspective. `Data semantics' then is the meaning of data, and will change as the context changes. Therefore, a successful data processing system will need to make the data semantics explicit. The authors discuss various approaches to the understanding of semantics, such as pattern analysis, schema mappings, and abductive logic. Of all the approaches discussed, the authors seem to favor the one based on abductive logic, referring to it as the least developed approach but one that shows the greatest promise in going beyond rule-based digital systems. However, the authors are incorrect in stating that this approach is not very well developed, as there are a few highly resilient systems, used primarily in bioinformatics and telecommunications, that make heavy use of abductive reasoning.

The conflicts that can occur between data on different machines motivate the authors to consider various approaches in dealing with these conflicts that does not involve customized code. After reviewing the specific types of conflicts that can occur, such as those due to data type, labeling, naming, and domain, the authors review some of the different `semantic solution patterns', for dealing with them. These include the `machine learning pattern' that is based on statistical analysis and reasoning patterns from artificial intelligence and is used to discover semantics within instance data; the `third-party reference pattern' which uses a thesaurus or ontology having a shared meaning across sources and targets; the `model-based mapping pattern' which uses well-defined metadata about context instead of mappings between data structures; and the `inference pattern' that requires a formalism to describe the semantic relationships in the system.

The authors obviously believe that these approaches are not entirely satisfactory, or they would not have written this book. Most of the book therefore is devoted to their solutions for solving semantic conflicts. Central to their approach is the role of metadata, which is viewed by the authors as forming a hierarchy with six layers: instance data (essentially "raw data"), syntactic metadata (needed to process data), structural metadata (which gives form and structure to units of data), referent metadata (to provide linkages between different data models), domain metadata (forms a "conceptual domain ontology" to provide a reference point on which all metadata can be understood), and rules (which constrain the semantics of metadata specifications).

One might call the authors approach a version of ontological engineering, the latter term being used currently to describe efforts to make data understandable in different contexts. In this regard, the authors consider four different types of ontology, namely interface (models essentially the API), process (applying to time-dependent processes), information (specification of a collection of concepts for a given scope), and policy (specification of rules of usage). Of particular interest in their discussion is that of the transformation of ontologies, which allows the moving of data from one model to another.

The approach that is advocated in this book is that of semantic interoperability, which the authors view as a `multimodal' solution since it can apply to different modes of operation and is not tied to a particular technology. A successful semantic architecture however is not a "plug-in" to be incorporated easily into standard middleware. Instead, it is an engine that is dynamic and adaptive, and responds real-time to changes by generating its own instruction sets. At first glance this engine would seem be one that is enormously complex, but the authors break it down into its modes of operation, in order to clarify just how it would function. Crucial to their conception of semantic interoperability is that it allows the discovery and utilization of new information without the intervention of a human. If such a capability can indeed be realized, it would definitely be a major advance and would justify the expense of its operation. The authors discuss in fair detail just how semantic interoperability would work in a business enterprise, at least schematically. To alleviate any skepticism on part of the reader of the reality of their ideas, they devote a large amount of space in the book to (nonproprietary and proprietary) case studies of implementation of semantic interoperability. Although these studies to not encapsulate all of the author's ideas, they do serve to convince the reader that these ideas should be taken seriously.

4 of 5 people found the following review helpful:

5.0 out of 5 stars If you are serious about implementing the semantic web today, February 25, 2005

By 

Steven Wright "Enterprise Data Architect" (Bradford, MA USA) - See all my reviews
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This review is from: Adaptive Information: Improving Business Through Semantic Interoperability, Grid Computing, and Enterprise Integration (Wiley Series in Systems Engineering and Management) (Hardcover)

This is the best book on Semantic Interoperability and Ontologies to date. Full of real solutions, atlternatives and product recommendations.