Introduction to Contextual Processing: Theory and Applications

Introduction to Contextual Processing: Theory and Applications

By: Gregory Vert (author), S. Sitharama Iyengar (author), Vir V. Phoha (author)Hardback

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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.

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About Author

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.


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.

Product Details

  • publication date: 06/12/2010
  • ISBN13: 9781439834688
  • Format: Hardback
  • Number Of Pages: 286
  • ID: 9781439834688
  • weight: 544
  • ISBN10: 1439834687

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