1st Edition

Introduction to Contextual Processing Theory and Applications

    286 Pages 82 B/W Illustrations
    by Chapman & Hall

    286 Pages 82 B/W Illustrations
    by Chapman & Hall

    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.

    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.

    Biography

    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.