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E-raamat: Introduction to Contextual Processing: Theory and Applications

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(Texas A&M University, Killeen, USA), (Louisiana Tech University, Ruston, USA), (Florida International University, Miami, USA)
  • Formaat: 286 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439834695
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Introduction to Contextual Processing: Theory and ApplicationsSuurem pilt
  • Formaat: 286 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781439834695
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"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"--

"Helping to advance a valuable paradigm shift in the next generation and processing of knowledge, this seminal work provides a comprehensive model for constructing a contextually based processing system that can support advanced semantic web and cloud computing capabilities at a global scale. 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. The book also describes numerous cutting-edge applications and research directions of contextual processing and offers novel methods for addressing the producer-consumer problem"--

Provided by publisher.
Preface xvii
About the Authors xxi
Contributors xxv
Chapter 1 The Case for Contextually Driven Computation
1(22)
Theme
1(3)
1.1 The Three Mile Island Nuclear Disaster
4(1)
1.2 Indian Ocean Tsunami Disaster
5(2)
1.3 Contextual Information Processing (CIP) of Disaster Data
7(2)
1.4 Contextual Information Processing and Information Assurance (CIPIA) of Disaster Data
9(2)
1.5 Components of Traditional Information Technology (IT) Architectures
11(1)
1.6 Example of Traditional It Architectures and Their Limitations
12(4)
1.7 Contextual Processing and the Semantic Web
16(1)
1.8 Contextual Processing and Cloud Computing
16(3)
1.9 Contextual Processing and Universal Core
19(2)
1.10 The Case for Contextual Processing and Summary
21(1)
References
22(1)
Chapter 2 Defining the Transformation of Data to Contextual Knowledge
23(52)
Theme
23(1)
2.1 Introduction and Knowledge Derivation from the Snow of Data
24(2)
2.2 The Importance of Knowledge in Manmade Disasters
26(2)
2.2.1 September 11: World Trade Center
26(2)
2.3 Context Models and Their Applications
28(3)
2.4 Defining Contextual Processing
31(4)
2.5 The Properties of Contextual Data
35(1)
2.6 Characteristics of Data
36(3)
2.7 Semantics and Syntactical Processing Models for Contextual Processing
39(8)
2.8 Storage Models that Preserve Spatial and Temporal Relationships Among Contexts
47(1)
2.9 Deriving Knowledge from Collected and Stored Contextual Information
48(3)
2.10 Similarities Among Data Objects
51(1)
2.11 Reasoning Methods for Similarity Analysis of Contexts
52(17)
2.11.1 Statistical Methods Means, Averages, Ceilings, and Floors
55(1)
2.11.2 Fuzzy Sets
55(1)
2.11.3 Standard Deviation
56(1)
2.11.4 Probabilistic Reasoning
57(3)
2.11.5 Support Vector Machines
60(2)
2.11.6 Clustering
62(3)
2.11.7 Bayesian Techniques
65(3)
2.11.8 Decision Trees
68(1)
2.12 Other Types of Reasoning in Contexts
69(1)
2.13 Context Quality
70(2)
2.14 Research Directions for Global Contextual Processing
72(1)
References
73(2)
Chapter 3 Calculus for Reasoning about Contextual Information
75(20)
Theme
75(1)
3.1 Context Representation
76(1)
3.2 Modus Ponens
77(2)
3.3 Fuzzy Set and Operations
79(1)
3.3.1 Union
79(1)
3.3.2 Intersection
79(1)
3.3.3 Complement
79(1)
3.3.3.1 De Morgan's Law
79(1)
3.3.3.2 Associativity
79(1)
3.3.3.3 Commutativity
80(1)
3.3.3.4 Distributivity
80(1)
3.4 Contextual Information and Nonmonotonic Logic
80(4)
3.4.1 Conflicts in Conclusions
80(2)
3.4.2 Default Theory
82(1)
3.4.2.1 Default
82(1)
3.4.2.2 Default Theory
82(2)
3.4.3 Entailment in a Contextual Case
84(1)
3.4.3.1 Prioritized Default Theory
84(1)
3.5 Situation Calculus
84(4)
3.5.1 Frame Problem
85(1)
3.5.2 Circumscription and the Yale Shooting Problem
85(2)
3.5.3 Formalism
87(1)
3.5.3.1 Action
87(1)
3.5.3.2 Situation
87(1)
3.5.3.3 Fluent
87(1)
3.5.4 The Successor State Axioms
87(1)
3.6 Recommended Framework
88(1)
3.6.1 Fuzzy Inference Scheme
88(1)
3.7 Example
89(3)
3.7.1 Prioritize Defaults
90(1)
3.7.2 Resolve the Frame Problem
90(1)
3.7.3 Fuzzy Inference
91(1)
3.8 Conclusion
92(1)
References
93(2)
Chapter 4 Information Mining for Contextual Data Sensing and Fusion
95(20)
Theme
95(1)
4.1 Data-Mining Overview
96(2)
4.2 Distributed Data Mining
98(10)
4.2.1 Motivation for Distributed Data Mining
98(1)
4.2.2 DDM Systems
99(1)
4.2.2.1 A Data-Driven Approach
99(1)
4.2.2.2 A Model-Driven Approach
99(1)
4.2.2.3 An Architecture-Driven Approach
100(1)
4.2.3 State of the Art
101(1)
4.2.3.1 Parallel and Distributed DM Algorithms
101(1)
4.2.4 Research Directions
102(1)
4.2.5 Scheduling DM Tasks on Distributed Platforms
102(1)
4.2.6 Data and the K-Grid
103(1)
4.2.7 The Knowledge Grid Scheduler (KGS)
104(1)
4.2.8 Requirements of the KGS
104(1)
4.2.9 Design of the KGS
104(2)
4.2.10 An Architectural Model for a K-Grid
106(2)
4.3 Context-Based Sensing, Data Mining, and its Applications
108(1)
4.3.1 Applications of Contextual Data Mining
108(1)
4.4 Example: The Coastal Restoration Data Grid and Hurricane Katrina
109(1)
4.5 The Power of Information Mining in Contextual Computing
110(1)
4.6 Enabling Large-Scale Data Analysis
110(1)
4.7 Example: Accessing Real-Time Information-Sensor Grids
111(1)
4.8 Research Directions for Fusion and Data Mining In Contextual Processing
112(1)
References
113(2)
Chapter 5 Hyperdistribution of Contextual Information
115(70)
Theme
115(1)
5.1 Introduction to Data Dissemination and Discovery
116(1)
5.2 Defining Hyperdistribution
117(8)
5.3 Issues in Hyperdistribution
125(3)
5.3.1 Context Generation
125(2)
5.3.2 Discovery of Consumers
127(1)
5.3.3 Routing of Data and Contextual Information
127(1)
5.4 Methods Infrastructure, Algorithms, and Agents
128(11)
5.4.1 Introduction
128(2)
5.4.2 Intelligent Agents
130(1)
5.4.3 Mobile Agents
131(1)
5.4.4 Web Services
132(1)
5.4.5 Security Issues with Web Services
133(3)
5.4.6 The Use of Web Services as Mobile Agent Hosts
136(1)
5.4.7 Security Issues with the Use of Web Services as Mobile Agent Hosts
137(1)
5.4.8 Web Services as Static Agents
137(1)
5.4.9 Hyperdistribution Methods
138(1)
5.5 Modeling Tools
139(13)
5.5.1 π-Calculus
139(1)
5.5.1.1 Overview
140(1)
5.5.1.2 Preliminary Definitions
140(3)
5.5.1.3 The Polyadic π-Calculus
143(1)
5.5.2 Ambient Calculus
143(1)
5.5.2.1 Ambients
144(1)
5.5.2.2 Mobility and Communication
144(3)
5.5.3 Petri Nets
147(1)
5.5.3.1 Overview
147(3)
5.5.3.2 Formal Definition
150(2)
5.5.3.3 Extensions to the Petri Net
152(1)
5.6 Advanced Topics
152(26)
5.6.1 Api-S Calculus
153(1)
5.6.1.1 Syntax
154(5)
5.6.1.2 Actions
159(3)
5.6.1.3 Binding
162(1)
5.6.1.4 Substitution and Convertibility
162(1)
5.6.1.5 Broadcasting
163(2)
5.6.1.6 Abbreviations
165(2)
5.6.1.7 Structural Congruence
167(1)
5.6.1.8 Reduction
168(4)
5.6.1.9 Simple Examples of API-S
172(6)
5.7 Example: Contextual Hyperdistribution
178(1)
5.8 Research Directions in Hyperdistribution of Contexts
179(2)
References
181(4)
Chapter 6 Set-Based Data Management Models for Contextual Data and Ambiguity in Selection
185(26)
Theme
185(1)
6.1 Introduction to Data Management
186(1)
6.2 Background on Contextual Data Management
187(2)
6.3 Context-Oriented Data Set Management
189(1)
6.4 Contextual Set Spatial Ambiguity in Retrieval
190(7)
6.5 A Set Model-Based Erd
197(2)
6.6 A Fuzzy Erd Model For Contextual Data Management
199(1)
6.7 Contextual Subsets
200(1)
6.8 Fuzzy Relation Similar Fns()
201(1)
6.9 Fuzzy Directionality
202(1)
6.10 Discretizing Function (Temporal ()
202(2)
6.11 Fuzzy Relation (Spatial()
204(1)
6.12 Extended Data Model for the Storage of Context Data Sets
204(4)
6.13 Example: Set-Based Modeling and Contextual Data Management
208(1)
6.14 Research Directions in Contextually Based Set Model Data Management
209(1)
References
210(1)
Chapter 7 Security Modeling Using Contextual Data Cosmology and Brane Surfaces
211(42)
Theme
211(1)
7.1 General Security
212(5)
7.1.1 Cybersecurity Overview and Issues
212(3)
7.1.2 Models of Security
215(2)
7.2 Challenges and Issues in the Development of Contextual Security
217(7)
7.2.1 Elements of Contexts
217(1)
7.2.2 Core Issues in Contextual Security
218(1)
7.2.2.1 Distribution
219(1)
7.2.2.2 Authentication
219(1)
7.2.2.3 Control and Geopolitics
220(1)
7.2.2.4 Spatial Data Security
220(1)
7.2.2.5 Time and Streaming
221(2)
7.2.2.6 Spatial Relationships
223(1)
7.2.2.7 Versioning Relationships
223(1)
7.2.2.8 Impact and Criticality
224(1)
7.3 An N-Dimensional Surface Model That Can Be Applied to Contextual Security
224(22)
7.3.1 Key Concepts of Relevance to Security
224(1)
7.3.2 Branes Defined
225(3)
7.3.3 Brane Geo-referencing
228(1)
7.3.4 Brane Classification Properties
228(1)
7.3.4.1 Inclusiveness
229(1)
7.3.4.2 Continuity
230(1)
7.3.4.3 Discreteness
231(1)
7.3.5 Selected Branes' Structures and Properties
232(1)
7.3.5.1 Hexahedron Brane
233(1)
7.3.5.2 Cylindrical Brane
234(1)
7.3.5.3 Frustum of a Cone Brane
235(2)
7.3.5.4 calcsecuritylevel()
237(2)
7.3.5.5 n-Sided Pyramid Brane
239(1)
7.3.5.6 pointinsideface (Eo, sides, apex)
240(1)
7.3.5.7 calcintersection (baseside, Eo)
240(4)
7.3.5.8 Frustum of a Pyramid Brane
244(2)
7.4 Textual Example: Pretty Good Security and Branes
246(2)
7.5 Practical Example: Pretty Good Security and Branes
248(1)
7.6 Research Directions in Pretty Good Security
249(2)
References
251(2)
Index 253
Gregory L. Vert is an assistant professor of computer science at Texas A&M UniversityCentral 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 Societys 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.
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