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Structured search for big data : from keywords to key-objects /
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....
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Waltham, MA :
Morgan Kaufmann,
[2016]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 008 | 150902s2016 mau o 000 0 eng d | ||
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| 100 | 1 | |a Gilula, Mikhail, |e author. | |
| 245 | 1 | 0 | |a Structured search for big data : |b from keywords to key-objects / |c Mikhail Gilula. |
| 264 | 1 | |a Waltham, MA : |b Morgan Kaufmann, |c [2016] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Vendor-supplied metadata. | |
| 520 | |a 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. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data. As a Big Data Consultant, Mikhail Gilula combines. | ||
| 505 | 0 | |a Machine generated contents note: ch. 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? -- ch. 2 Key-Objects vs. Keywords -- 2.1. Introducing Key-Objects -- 2.2. Mary'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 -- ch. 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. | |
| 505 | 0 | |a Note continued: 3.5. Operations on Key-Object Instances -- 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 -- ch. 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. | |
| 505 | 0 | |a Note continued: 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 -- ch. 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 -- 5.2.2. Drop Catalog -- 5.2.3. Create Atomic Keyobject -- 5.2.4. Drop Atomic Keyobject -- 5.2.5. Create Nonatomic Keyobject -- 5.2.6. Drop Nonatomic Keyobject -- 5.2.7. Create Synonymy -- 5.2.8. Add To Synonymy -- 5.2.9. Remove From Synonymy -- 5.2.10. Drop Synonymy -- 5.3. Store Manipulation Language -- 5.3.1. Syntax of Key-Object Instances -- 5.3.2. Json Representation of Instances -- 5.3.3. Federative SELECT -- 5.3.4. Create Store -- 5.3.5. Drop Store -- 5.3.6. Native Select -- 5.3.7. Insert -- 5.3.8. Update -- 5.3.9. Delete. | |
| 505 | 0 | |a Note continued: 5.3.10. Create Store As Select -- 5.3.11. Insert Select -- 5.4. Show Statements -- 5.4.1. Show Atomic Keyobject -- 5.4.2. Show Nonatomic Keyobject -- 5.4.3. Show Catalog -- 5.4.4. Show Synonymy Relation -- 5.4.5. Show Keyobjects In Store -- ch. 6 Structured Search on Database Landscape -- 6.1. Questions and Topics -- 6.2. Key-Objects and Object-Oriented Programming Paradigm -- 6.3. Key-Objects and Object-Oriented Databases -- 6.4. KeySQL and NoSQL -- 6.5. Query Independence and Data Independence -- 6.6. KeySQL and MPP Architectures -- ch. 7 Structured Search Solutions -- 7.1.E-Commerce Applications -- 7.1.1. Saving Millions of Hours to Shoppers -- 7.1.2. Optimizing and Energizing Marketplace -- 7.1.3. Structured Search Advertising -- 7.1.4. Mobile E-Commerce -- 7.1.5. BayZon Marketplace -- 7.1.6. BinYahGoo Search Portal -- 7.2. Secure Federated System -- 7.3. Native KeySQL Systems -- 7.3.1. Healthcare Information Systems -- 7.3.2. Big Data Warehousing. | |
| 505 | 0 | |a Note continued: 7.3.3. KeySQL on MapReduce Clusters -- 7.4. Structured Search in Internet Evolution -- 7.4.1. Internet as Data Store. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Internet searching. | |
| 650 | 0 | |a Database searching. | |
| 650 | 0 | |a Keyword searching. | |
| 650 | 0 | |a Querying (Computer science) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Recherche sur Internet. | |
| 650 | 6 | |a Bases de données |x Interrogation. | |
| 650 | 6 | |a Recherche par mots-clés. | |
| 650 | 7 | |a online searching. |2 aat | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Database Management |x General. |2 bisacsh | |
| 650 | 7 | |a Querying (Computer science) |2 fast | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Database searching |2 fast | |
| 650 | 7 | |a Internet searching |2 fast | |
| 650 | 7 | |a Keyword searching |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Gilula, Mikhail. |t Structured search for big data. |d Waltham, MA : Morgan Kaufmann, [2016] |z 0128046317 |z 9780128046319 |w (OCoLC)919343215 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780128046524/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a EBSCOhost |b EBSC |n 1058933 | ||
| 938 | |a ProQuest MyiLibrary Digital eBook Collection |b IDEB |n cis32457118 | ||
| 938 | |a YBP Library Services |b YANK |n 12591789 | ||
| 994 | |a 92 |b IZTAP | ||