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Introduction to contextual processing : theory and applications / Gregory L. Vert, S. Sitharama Iyengar, and Vir V. Phoha.

"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 Applicat...

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Bibliographic Details
Online Access: Full Text (via Taylor & Francis)
Main Author: Vert, Gregory
Other Authors: Iyengar, S. S. (Sundararaja S.), Phoha, Vir V.
Format: eBook
Language:English
Published: Boca Raton : CRC Press, ©2011.
Subjects:
Semantic computing.
Context-aware computing.
Table of Contents:
  • Ch. 1. The case for contextually driven computation
  • ch. 2. Defining the transformation of data to contextual knowledge
  • ch. 3. Calculus for reasoning about contextual information
  • ch. 4. Information mining for contextual data sensing and fusion
  • ch. 5. Hyperdistribution of contextual information
  • ch. 6. Set-based data management models for contextual data and ambiguity in selection
  • ch. 7. Security modeling using contextual data cosmology and brane surfaces.
  • Machine generated contents note: ch. 1 Case for Contextually Driven Computation
  • Theme
  • 1.1. Three Mile Island Nuclear Disaster
  • 1.2. Indian Ocean Tsunami Disaster
  • 1.3. Contextual Information Processing (CIP) of Disaster Data
  • 1.4. Contextual Information Processing and Information Assurance (CIPIA) of Disaster Data
  • 1.5. Components of Traditional Information Technology (IT) Architectures
  • 1.6. Example of Traditional It Architectures and Their Limitations
  • 1.7. Contextual Processing and the Semantic Web
  • 1.8. Contextual Processing and Cloud Computing
  • 1.9. Contextual Processing and Universal Core
  • 1.10. Case for Contextual Processing and Summary
  • References
  • ch. 2 Defining the Transformation of Data to Contextual Knowledge
  • Theme
  • 2.1. Introduction and Knowledge Derivation from the Snow of Data
  • 2.2. Importance of Knowledge in Manmade Disasters
  • 2.2.1. September 11: World Trade Center
  • 2.3. Context Models and Their Applications
  • 2.4. Defining Contextual Processing
  • 2.5. Properties of Contextual Data
  • 2.6. Characteristics of Data
  • 2.7. Semantics and Syntactical Processing Models for Contextual Processing
  • 2.8. Storage Models that Preserve Spatial and Temporal Relationships Among Contexts
  • 2.9. Deriving Knowledge from Collected and Stored Contextual Information
  • 2.10. Similarities Among Data Objects
  • 2.11. Reasoning Methods for Similarity Analysis of Contexts
  • 2.11.1. Statistical Methods Means, Averages, Ceilings, and Floors
  • 2.11.2. Fuzzy Sets
  • 2.11.3. Standard Deviation
  • 2.11.4. Probabilistic Reasoning
  • 2.11.5. Support Vector Machines
  • 2.11.6. Clustering
  • 2.11.7. Bayesian Techniques
  • 2.11.8. Decision Trees
  • 2.12. Other Types of Reasoning in Contexts
  • 2.13. Context Quality
  • 2.14. Research Directions for Global Contextual Processing
  • References
  • ch. 3 Calculus for Reasoning about Contextual Information
  • Theme
  • 3.1. Context Representation
  • 3.2. Modus Ponens
  • 3.3. Fuzzy Set and Operations
  • 3.3.1. Union
  • 3.3.2. Intersection
  • 3.3.3. Complement
  • 3.3.3.1. De Morgan's Law
  • 3.3.3.2. Associativity
  • 3.3.3.3. Commutativity
  • 3.3.3.4. Distributivity
  • 3.4. Contextual Information and Nonmonotonic Logic
  • 3.4.1. Conflicts in Conclusions
  • 3.4.2. Default Theory
  • 3.4.2.1. Default
  • 3.4.2.2. Default Theory
  • 3.4.3. Entailment in a Contextual Case
  • 3.4.3.1. Prioritized Default Theory
  • 3.5. Situation Calculus
  • 3.5.1. Frame Problem
  • 3.5.2. Circumscription and the Yale Shooting Problem
  • 3.5.3. Formalism
  • 3.5.3.1. Action
  • 3.5.3.2. Situation
  • 3.5.3.3. Fluent
  • 3.5.4. Successor State Axioms
  • 3.6. Recommended Framework
  • 3.6.1. Fuzzy Inference Scheme
  • 3.7. Example
  • 3.7.1. Prioritize Defaults
  • 3.7.2. Resolve the Frame Problem
  • 3.7.3. Fuzzy Inference
  • 3.8. Conclusion
  • References
  • ch. 4 Information Mining for Contextual Data Sensing and Fusion
  • Theme
  • 4.1. Data-Mining Overview
  • 4.2. Distributed Data Mining
  • 4.2.1. Motivation for Distributed Data Mining
  • 4.2.2. DDM Systems
  • 4.2.2.1. Data-Driven Approach
  • 4.2.2.2. Model-Driven Approach
  • 4.2.2.3. Architecture-Driven Approach
  • 4.2.3. State of the Art
  • 4.2.3.1. Parallel and Distributed DM Algorithms
  • 4.2.4. Research Directions
  • 4.2.5. Scheduling DM Tasks on Distributed Platforms
  • 4.2.6. Data and the K-Grid
  • 4.2.7. Knowledge Grid Scheduler (KGS)
  • 4.2.8. Requirements of the KGS
  • 4.2.9. Design of the KGS
  • 4.2.10. Architectural Model for a K-Grid
  • 4.3. Context-Based Sensing, Data Mining, and its Applications
  • 4.3.1. Applications of Contextual Data Mining
  • 4.4. Example: The Coastal Restoration Data Grid and Hurricane Katrina
  • 4.5. Power of Information Mining in Contextual Computing
  • 4.6. Enabling Large-Scale Data Analysis
  • 4.7. Example: Accessing Real-Time Information-Sensor Grids
  • 4.8. Research Directions for Fusion and Data Mining In Contextual Processing
  • References
  • ch. 5 Hyperdistribution of Contextual Information
  • Theme
  • 5.1. Introduction to Data Dissemination and Discovery
  • 5.2. Defining Hyperdistribution
  • 5.3. Issues in Hyperdistribution
  • 5.3.1. Context Generation
  • 5.3.2. Discovery of Consumers
  • 5.3.3. Routing of Data and Contextual Information
  • 5.4. Methods Infrastructure, Algorithms, and Agents
  • 5.4.1. Introduction
  • 5.4.2. Intelligent Agents
  • 5.4.3. Mobile Agents
  • 5.4.4. Web Services
  • 5.4.5. Security Issues with Web Services
  • 5.4.6. Use of Web Services as Mobile Agent Hosts
  • 5.4.7. Security Issues with the Use of Web Services as Mobile Agent Hosts
  • 5.4.8. Web Services as Static Agents
  • 5.4.9. Hyperdistribution Methods
  • 5.5. Modeling Tools
  • 5.5.1. π-Calculus
  • 5.5.1.1. Overview
  • 5.5.1.2. Preliminary Definitions
  • 5.5.1.3. Polyadic π-Calculus
  • 5.5.2. Ambient Calculus
  • 5.5.2.1. Ambients
  • 5.5.2.2. Mobility and Communication
  • 5.5.3. Petri Nets
  • 5.5.3.1. Overview
  • 5.5.3.2. Formal Definition
  • 5.5.3.3. Extensions to the Petri Net
  • 5.6. Advanced Topics
  • 5.6.1. Api-S Calculus
  • 5.6.1.1. Syntax
  • 5.6.1.2. Actions
  • 5.6.1.3. Binding
  • 5.6.1.4. Substitution and Convertibility
  • 5.6.1.5. Broadcasting
  • 5.6.1.6. Abbreviations
  • 5.6.1.7. Structural Congruence
  • 5.6.1.8. Reduction
  • 5.6.1.9. Simple Examples of API-S
  • 5.7. Example: Contextual Hyperdistribution
  • 5.8. Research Directions in Hyperdistribution of Contexts
  • References
  • ch. 6 Set-Based Data Management Models for Contextual Data and Ambiguity in Selection
  • Theme
  • 6.1. Introduction to Data Management
  • 6.2. Background on Contextual Data Management
  • 6.3. Context-Oriented Data Set Management
  • 6.4. Contextual Set Spatial Ambiguity in Retrieval
  • 6.5. Set Model-Based Erd
  • 6.6. Fuzzy Erd Model For Contextual Data Management
  • 6.7. Contextual Subsets
  • 6.8. Fuzzy Relation Similar Fns()
  • 6.9. Fuzzy Directionality
  • 6.10. Discretizing Function (Temporal ()
  • 6.11. Fuzzy Relation (Spatial()
  • 6.12. Extended Data Model for the Storage of Context Data Sets
  • 6.13. Example: Set-Based Modeling and Contextual Data Management
  • 6.14. Research Directions in Contextually Based Set Model Data Management
  • References
  • ch. 7 Security Modeling Using Contextual Data Cosmology and Brane Surfaces
  • Theme
  • 7.1. General Security
  • 7.1.1. Cybersecurity Overview and Issues
  • 7.1.2. Models of Security
  • 7.2. Challenges and Issues in the Development of Contextual Security
  • 7.2.1. Elements of Contexts
  • 7.2.2. Core Issues in Contextual Security
  • 7.2.2.1. Distribution
  • 7.2.2.2. Authentication
  • 7.2.2.3. Control and Geopolitics
  • 7.2.2.4. Spatial Data Security
  • 7.2.2.5. Time and Streaming
  • 7.2.2.6. Spatial Relationships
  • 7.2.2.7. Versioning Relationships
  • 7.2.2.8. Impact and Criticality
  • 7.3. N-Dimensional Surface Model That Can Be Applied to Contextual Security
  • 7.3.1. Key Concepts of Relevance to Security
  • 7.3.2. Branes Defined
  • 7.3.3. Brane Geo-referencing
  • 7.3.4. Brane Classification Properties
  • 7.3.4.1. Inclusiveness
  • 7.3.4.2. Continuity
  • 7.3.4.3. Discreteness
  • 7.3.5. Selected Branes' Structures and Properties
  • 7.3.5.1. Hexahedron Brane
  • 7.3.5.2. Cylindrical Brane
  • 7.3.5.3. Frustum of a Cone Brane
  • 7.3.5.4. calcsecuritylevel()
  • 7.3.5.5. n-Sided Pyramid Brane
  • 7.3.5.6. pointinsideface (Eo, sides, apex)
  • 7.3.5.7. calcintersection (baseside, Eo)
  • 7.3.5.8. Frustum of a Pyramid Brane
  • 7.4. Textual Example: Pretty Good Security and Branes
  • 7.5. Practical Example: Pretty Good Security and Branes
  • 7.6. Research Directions in Pretty Good Security
  • References.

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