Introduction to Contextual Processing: Theory and Applications, 1st Edition (Paperback) book cover

Introduction to Contextual Processing

Theory and Applications, 1st Edition

By Gregory Vert, S. Sitharama Iyengar, Vir V. Phoha

Chapman and Hall/CRC

286 pages | 82 B/W Illus.

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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

Example

Conclusion

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.

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
COM043000
COMPUTERS / Networking / General
COM053000
COMPUTERS / Security / General
COM059000
COMPUTERS / Computer Engineering
COM079010
COMPUTERS / Social Aspects / Human-Computer Interaction
MAT000000
MATHEMATICS / General