The goal of enterprise integration is the development of computer-based tools that facilitate coordination of work and information flow across organizational boundaries. These proceedings, the first on EI modeling technologies, provide a synthesis of the technical issues involved; describe the various approaches and where they overlap, complement, or conflict with each other; and identify problems and gaps in the current technologies that point to new research.The leading edge of a movement that began with computer-aided design/computer-aided manufacturing (CAD/CAM), EI now seeks to engage the development of computer-based tools to control not only manufacturing but the allied areas of materials supply, accounting, and inventory control. EI technology is pushing forward research in areas such as distributed AI, concurrent engineering, task coordination, human-computer interaction, and distributed planning and scheduling. These proceedings provide the first common technical ground for comparing, evaluating, or coordinating these efforts.Charles J. Petrie, Jr., is Senior Member of Technical Staff at MCC in Austin, Texas.Topics include: Computer Integrated Manufacturing. Open System Architecture Standards. The results of five workshops on EI modeling topics: Model Integration, Model/Application Namespace, Heterogeneous Execution Environments, Metrics and Methodologies, and Coordination Process Models.

Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool. This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling. - Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality - Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates - Develops ideas for creating consistent approaches to high quality data models

Making Data Integration Work: How to Systematically Reduce Cost, Improve Quality, and Enhance Effectiveness Today’s enterprises are investing massive resources in data integration. Many possess thousands of point-to-point data integration applications that are costly, undocumented, and difficult to maintain. Data integration now accounts for a major part of the expense and risk of typical data warehousing and business intelligence projects--and, as businesses increasingly rely on analytics, the need for a blueprint for data integration is increasing now more than ever. This book presents the solution: a clear, consistent approach to defining, designing, and building data integration components to reduce cost, simplify management, enhance quality, and improve effectiveness. Leading IBM data management expert Tony Giordano brings together best practices for architecture, design, and methodology, and shows how to do the disciplined work of getting data integration right. Mr. Giordano begins with an overview of the “patterns” of data integration, showing how to build blueprints that smoothly handle both operational and analytic data integration. Next, he walks through the entire project lifecycle, explaining each phase, activity, task, and deliverable through a complete case study. Finally, he shows how to integrate data integration with other information management disciplines, from data governance to metadata. The book’s appendices bring together key principles, detailed models, and a complete data integration glossary. Coverage includes Implementing repeatable, efficient, and well-documented processes for integrating data Lowering costs and improving quality by eliminating unnecessary or duplicative data integrations Managing the high levels of complexity associated with integrating business and technical data Using intuitive graphical design techniques for more effective process and data integration modeling Building end-to-end data integration applications that bring together many complex data sources

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

While many facets of our lives are rapidly becoming more digital, educational institutions are now faced with the task of finding new and innovative ways to incorporate technology into the classroom. Examining the latest trends in digital tools provides a more effective learning environment for future generations. The Handbook of Research on Digital Content, Mobile Learning, and Technology Integration Models in Teacher Education is a pivotal scholarly reference source that outlines the most efficient ways for educators to employ technology-enhanced lesson plans in their classroom. Featuring pertinent topics that include blended learning environments, student engagement, artificial intelligence, and learner-centered pedagogy, this is an ideal resource for educators, aspiring teachers, and researchers that are interested in discovering recent trends and techniques related to digital learning environments and technology-enhanced classrooms.

The integration of classic field-gathered data with new computer models has allowed many new advances in geomorphology, which the 31st Binghamton Millennium Symposium 2000 presents in this latest of the well-known Binghamton book series, the Integration of Computer Modeling and Field Observations in Geomorphology. Conceptual models have been most commonly inferred from analyses of topography and investigator perspectives derived from fieldwork. The main stumbling blocks to understanding surface processes, their interactions, temporal changes, and resulting landforms are the difficulty of observation, geological timescales involved, spatial-scale dependencies, and the inability to attribute differences to either process or age. Physically based computer models have thus become essential tools, primarily because of their ability to explore spatial and temporal trends and to determine the sensitivity of physical inputs to change without the difficulties of identification and generalization associated with the complexity of field studies. Thus, the combination of both methods, or the integration of field methods with computer modeling become a very powerful mechanism for robust understanding. This new book presents topics on fluvial processes of overland and channelized flow in arid, humid, and periglacial areas of high and low relief, as well as work on interlinked biogeographic and geomorphic fluctuations in alpine terrain, and ground penetrating radar of coastal geomorphology. Issues of long-term evolution of drainage networks are addressed in natural systems, as well as stream-table environments, and terrain analyses characterize surficial and subsurface geomorphic features by using GIS and remote sensing. Botanical and biogeomorphologic controls of landforms are assessed, along with issues of scientific visualization, cartographic representation, DEMs, spatial analyses, and scale dependencies.