Structured Search for Big Data : From Keywords to Key-objects /

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The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search....

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Gilula, Mikhail
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : [publisher not identified], 2015.
Temas:
Big data.
Internet searching.
Database searching.
Keyword searching.
Données volumineuses.
Recherche sur Internet.
Bases de données > Interrogation.
Recherche par mots-clés.
online searching.
Big data
Database searching
Internet searching
Keyword searching
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Dedication
  • Contents
  • Quotation
  • Preface
  • Objective
  • Audience
  • Outline of the book
  • US patents
  • Acknowledgments
  • Chapter 1
  • Introduction to Structured Search
  • 1.1
  • Limitations of Keyword Search
  • 1.2
  • Keyword Search in E-Commerce
  • 1.3
  • Limitations of Database Search
  • 1.4
  • What is Structured Search?
  • Chapter 2
  • Key-Objects vs. Keywords
  • 2.1 -Introducing Key-Objects
  • 2.2
  • Maryâ€?s Printer
  • 2.3
  • Key-Objects and Instances
  • 2.3.1
  • Key-Objects
  • 2.3.2
  • Key-Object Instances 2.4
  • Catalogs and Query Expansion
  • 2.4.1
  • Querying via Key-Objects
  • 2.4.2
  • More Query Examples
  • 2.4.3
  • Catalogs With Relations
  • 2.4.4
  • Query Expansion
  • Chapter 3
  • Key-Object Data Model
  • 3.1
  • Key-Objects as Hereditarily-Finite Sets
  • 3.2
  • Operations on Key-Objects
  • 3.2.1
  • Key-Object Naming
  • 3.2.2
  • Union
  • 3.2.3
  • Intersection
  • 3.2.4
  • Difference
  • 3.2.5
  • Composition
  • 3.2.6
  • Composition Naming Convention
  • 3.3
  • Catalogs are Key-Objects
  • 3.4
  • Instances as hereditarily-finite sets 3.4.1
  • Multivalued Instances
  • 3.4.2
  • Multiassumption
  • 3.4.3
  • Flat Representation
  • 3.5
  • Instances as Hereditarily-Finite Sets
  • 3.5.1
  • Composition
  • 3.5.2
  • Projection
  • 3.5.3
  • Restriction
  • 3.6
  • Data Stores
  • 3.6.1
  • Heterogeneous, Homogeneous, and Flat Stores
  • 3.6.2
  • Comparison with Relational Model
  • 3.7
  • Operations on Stores
  • 3.7.1
  • Union
  • 3.7.2
  • Intersection
  • 3.7.3
  • Difference
  • 3.7.4
  • Filtering
  • 3.7.5
  • Restriction
  • 3.7.6
  • Projection
  • 3.7.7
  • Product 3.7.8
  • Join
  • Chapter 4
  • Structured Search Framework
  • 4.1
  • Introduction
  • 4.2
  • Principles
  • 4.2.1
  • Facts, not Documents
  • 4.2.2
  • Query Independence
  • 4.2.3
  • Search Scalability
  • 4.2.4
  • Precision Control
  • 4.2.5
  • Output Order Control
  • 4.2.6
  • Not Only for Humans
  • 4.2.7
  • Real-Time Access
  • 4.2.8
  • Security Control
  • 4.3
  • General Framework
  • 4.3.1
  • Basic Functions
  • 4.3.2
  • Queries and Responses: Q-Format and R-Format
  • 4.3.3
  • Catalogs as Federating Namespaces
  • 4.3.4
  • Data Providers
  • 4.3.5
  • Adding and Removing Data Providers 4.3.6
  • Bus and Subscription Modes
  • 4.3.7
  • Query Processing by Data Providers
  • 4.3.8
  • Query Origination
  • 4.3.9
  • Federative and Native Data Manipulation
  • 4.3.10
  • Query Independence, Scalability, and Security
  • 4.4
  • Data Store Functionality
  • 4.4.1
  • Catalog Management
  • 4.4.2
  • Store Manipulation
  • Chapter 5
  • Introduction to KeySQL
  • 5.1
  • Overview
  • 5.1.1
  • CML and SML
  • 5.1.2
  • Federative and Native Sublanguages
  • 5.2
  • Catalog Management Language
  • 5.2.1
  • CREATE CATALOG
  • ""5.2.1.1
  • Semantics ""

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