Modeling and Simulation-Based Data Engineering: Introducing Pragmatics Into Ontologies for Net-Centric Information Exchange

It is often the case that a new area of inquiry, comprised of elements from seemingly disparate disciplines, yields surprising insights. Zeigler's and Hammonds's effort is no exception. While linguistics has from time to time informed mathematics and computer science, to my knowledge little has been done to date towards the application of semantics and pragmatics to this type of data exchange. The prose is concise, clear, and conversational. Given the complexity of the two topics and the more or less mutual exclusivity of their lexicons, readers whose experience has been acquired only in the one or the other of these two disciplines will nevertheless quickly become comfortable in this discussion. The authors provide many examples to illustrate their line of reasoning, all drawn from a wide variety of sources. As an IT professional with 15 years' experience and an advanced degree in a foreign language, I found this book satisfying, illuminating, and provocative. While it is intended to address a specific engineering problem, its implications extend well beyond its stated purview. Heartily recommended for those who would like to think about the synergies of data engineering and pragmatics, and also for those who want to think about what might be beyond the horizon.

This book offers to the reader a thick and consistent theoretical background for dealing with ontologies (i.e., languages for describing "a state of the world".) Both authors merged very carefully their knowledge and disciplines [social and computer (engineering) sciences] in a complete and homogeneous framework. In the new research area of computer-based problems, dealing with complex systems induces increasing efforts for building unifying modifiable ontologies describing the systems, data and communications. Large digital data are described and abstracted through more and more complex software. Computer-based problems need to have strong theories to map very quickly evolving technical evolutions. Developing such theories allows to build a common field for discussions and specifications to participate all together bringing tools and incremental concepts (concepts of concepts of concepts...) Always thinking of knowledge of knowledge (or metaknowledge) models can be constructed. Using such a philosophy, ideas become program-independent and right issues and perspectives are more easily identified. Knowledge can be organized to cognitively map real systems to computer-based models. This is what offers us this new book. But that's not all! More than neutral/specifiable mathematical structures, this book provides precise mappings and discusses usual notations and current orientations (XML, HTML, UML, MDA, etc.) Actual generic large applications (geospatial sensor data, natural languages, hierarchical constructions, WWW, etc.) and a plethora of didactical examples are presented. Lastly, a web-based interface allows the reader to experiment his understandings. Even researchers from the modelling and simulation field will find here a way to deal with digital input data. According to me, this book is the starting point (and foundation) for those who intend to build soundly ontologies through computers in a modular, generic and hierarchical way: government agencies, developers, standards organizations, researchers, etc. They will find here the precise technical solutions they are searching for, as well as a common evolutive language to model data for dynamic systems. If all problems could not be grasped in one book, the latter will pinpoint major issues in such an abstract way that people are able to identify easily them and to find further solutions. This book is definitely for those who intend to increase their knowledge on ontology, develop mental models and want to talk and search together in a controlled and original perspective!

Addressing the compatibility issues raised in sharing data between collaborative organizations that employ different approaches to representing data becomes a major concern in today's net-centric computing environment. Effective information exchange requires not only an agreement on the syntax and semantics to be established between data producers and consumers, but also a common understanding of the pragmatics, namely the intended use of the data in specific situational contexts. It is the development of a generic ontology framework called System Entity Structure (SES) to describe both static and dynamic world states and a set of openly available tools to support automated creating and testing of the data model, then, that is at the center of Bernard Zeigler and Phillip Hammonds's new book Modeling & Simulation-based Data Engineering. By delineating the critical relationships that best structure a data engineer's domain of interest with the extra expressive power, the proposed pragmatic framework captures the exact intent of the data producers and consumers, which, in turn, allows for effective conversation and appropriate downstream processing. The SES framework is formulated as a labeled tree comprising basic elements and relations that satisfy a set of formation rules or axioms. With the supporting tools, it can be defined in a restricted form of natural language and subsequently be mapped into various computational forms, including eXtensible Markup Language (XML), Document Object Models (DOM), XML Document Type Definition (DTD), and XML Schema. A standard way of restructuring and pruning different SES representations is provided to improve representation utility and harmonization. The Pruned Entity Structure (PES) provides the basis for static and dynamic world state descriptions, efficient extraction of data, and more advanced form of information exchange. As the authors put it, "the SES together with the Discrete Event Systems Specification (DEVS) formalism offers a powerful system-theoretic framework for specifying families of dynamic services that can execute in simulated or real-time and interact with other services in a net-centric environment." Throughout the book, a broad range of easy-to-follow examples, case studies, and exercises is provided to consolidate the concepts and methodologies presented in the text and to give readers significant hands-on experience. This book is addressed to all those who are concerned either with data engineering in general or with interoperability in multi-institutional collaboration. Any reader with a general knowledge of ontology and discrete-event modeling and simulation will be able to benefit from the authors' insights.

For those who are working on complex problems of data interoperability and reuse of data sources in distributed environment, especially GIG/SOA, this book provides rigorous and novel methods and framework approach to solve data harmonization and ontology integration problems effectively. The authors present the pragmatic frame concepts, ontologies, System Entity Structure (SES) framework, and modeling and simulation based data engineering, all of which are useful methodologies to achieve automated interoperability testing at syntactic, semantic, and pragmatic levels in a net-centric environment. The book identifies complex problems encountered in harmonization and testing and illustrates framework and approach to implement such solutions in software tools and services. The concept of SES is being implemented in a commercial software with some online support. It is a truly fine resource for data and system engineers who look for solid approach to solve complex real-world problems!