Introduction to Contextual Processing : Theory and Applications book cover
1st Edition

Introduction to Contextual Processing
Theory and Applications

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ISBN 9781439834688
Published December 8, 2010 by Chapman and Hall/CRC
286 Pages 82 B/W Illustrations

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Book Description

Develops a Comprehensive, Global Model for Contextually Based Processing Systems
A new perspective on global information systems operation

Helping to advance a valuable paradigm shift in the next generation and processing of knowledge, Introduction to Contextual Processing: Theory and Applications provides a comprehensive model for constructing a contextually based processing system. It explores the components of this system, the interactions of the components, key mathematical foundations behind the model, and new concepts necessary for operating the system.

After defining the key dimensions of a model for contextual processing, the book discusses how data is used to develop a semantic model for contexts as well as language-driven context-specific processing actions. It then applies rigorous mathematical methods to contexts, examines basic sensor data fusion theory and applies it to the contextual fusion of information, and describes the means to distribute contextual information. The authors also illustrate a new type of data repository model to manage contextual data, before concluding with the requirements of contextual security in a global environment.

This seminal work presents an integrated framework for the design and operation of the next generation of IT processing. It guides the way for developing advanced IT systems and offers new models and concepts that can support advanced semantic web and cloud computing capabilities at a global scale.

Table of Contents

The Case for Contextually Driven Computation
Three Mile Island Nuclear Disaster
Indian Ocean Tsunami Disaster
Contextual Information Processing (CIP) of Disaster Data
Contextual Information Processing and Information Assurance (CIPIA) of Disaster Data
Components of Traditional IT Architectures
Example of Traditional IT Architectures and Their Limitations
Contextual Processing and the Semantic Web
Contextual Processing and Cloud Computing
Contextual Processing and Universal Core
The Case for Contextual Processing and Summary

Defining the Transformation of Data to Contextual Knowledge
Introduction and Knowledge Derivation from the Snow of Data
The Importance of Knowledge in Manmade Disasters
Context Models and Their Application
Defining Contextual Processing
The Properties of Contextual Data
Characteristics of Data
Semantics and Syntactical Processing Models for Contextual Processing
Storage Models That Preserve Spatial/Temporal Relationships among Contexts
Deriving Knowledge from Collected and Stored Contextual Information
Similarities among Data Objects
Reasoning Methods for Similarity Analysis of Contexts
Other Types of Reasoning in Contexts
Context Quality
Research Directions for Global Contextual Processing

A Calculus for Reasoning about Contextual Information
Context Representation
Modus Ponens
Fuzzy Set and Operations
Contextual Information and Non-Monotonic Logic
Situation Calculus
Recommended Framework

Information Mining for Contextual Data Sensing and Fusion
Data Mining Overview
Distributed Data Mining (DDM)
Context-Based Sensing, Data Mining, and Its Applications
Example—The Coastal Restoration Data Grid and Katrina
Power of Information Mining in Contextual Computing
Enabling Large Scale Data Analysis
Example—Accessing Real-Time Information: Sensor Grids
Research Directions for Fusion and Data Mining in Contextual Processing

Hyper Distribution of Contextual Information—The Consumer Producer Problem
Introduction to Data Dissemination and Discovery
Defining Hyper Distribution
Issues in Hyper Distribution
Methods Infrastructure, Algorithms, and Agents
Modeling Tools
Advanced Topics
Example—Contextual Hyper Distribution
Research Directions in Hyper Distribution of Contexts

Set-Based Data Management Models for Contextual Data and Ambiguity in Selection
Introduction to Data Management
Background on Contextual Data Management
Context Oriented Data Set Management
Contextual Set Spatial Ambiguity in Retrieval
A Set Model-Based Entity-Relationship Diagram (ERD)
A Fuzzy ERD Model for Contextual Data Management
Contextual Subsets
Fuzzy Relation Similar FnS()
Fuzzy Directionality
Discretenizing Function Ctemporal ()
Fuzzy Relation CSpatial ()
Extended Data Model for the Storage of Context Data Sets
Example—Set-Based Modeling and Contextual Data Management
Research Directions in Contextual-Based Set Model Data Management

Security Modeling for Contextual Data Cosmology and Brane Surfaces
General Security
Challenges and Issues in Development of Contextual Security
An N Dimensional Surface Model That Can Be Applied to Contextual Security
Textual Example—Pretty Good Security and Branes
Practical Example—Pretty Good Security and Branes
Research Directions in Pretty Good Security

References appear at the end of each chapter.

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Gregory L. Vert is an assistant professor of computer science at Texas A&M University–Central Texas in Killeen. Dr. Vert has worked in industry for companies that include IBM, American Express, and Boeing. While at American Express, he co-designed a portion of their worldwide database system. His current research deals with advanced methods for intrusion detection and autonomous system response, advanced data management models, biometrics and bioinformatics, and contextual processing.

Sundaraja Sitharama Iyengar is the Roy Paul Daniels Distinguished Professor and chairman of the Department of Computer Science as well as founder and director of the Robotics Research Laboratory at Louisiana State University in Baton Rouge. Dr. Iyengar is the founding editor-in-chief of the International Journal of Distributed Sensor Networks, has been an associate editor of IEEE Transaction on Computers and IEEE Transactions on Data and Knowledge Engineering, and has been a guest editor of IEEE Computer Magazine. He is a member of the European Academy of Sciences and a fellow of the IEEE, ACM, AAAS, and SDPS. He has received the Distinguished Alumnus Award of the Indian Institute of Science and the IEEE Computer Society’s Technical Achievement Award.

Vir V. Phoha is a professor of computer science, W.W. Chew Endowed Professor, and director of the Center for Secure Cyberspace at Louisiana Tech University in Ruston. An ACM Distinguished Scientist, Dr. Phoha has received funding from the NSF, Army Research Office, Office of Naval Research, Air Force Office of Scientific Research, Air Force Research Lab, and the State of Louisiana to support his research.

Drs. Vert, Iyengar, and Phoha are all members of the Center for Secure Cyberspace located at Louisiana Tech University.