Distributed Computing in Big Data Analytics : (Record no. 1013699)

001 - CONTROL NUMBER
control field EBC4996470
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
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007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191220s2017 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319598345
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9783319598338
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC4996470
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL4996470
035 ## - SYSTEM CONTROL NUMBER
System control number (CaPaEBR)ebr11430130
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1002908027
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA75.5-76.95
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 004
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) QA75.5-76.95
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Mazumder, Sourav.
245 10 - TITLE STATEMENT
Title Distributed Computing in Big Data Analytics :
Remainder of title Concepts, Technologies and Applications.
264 #1 -
-- Cham :
-- Springer,
-- 2017.
264 #4 -
-- ©2017.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (166 pages)
336 ## - Content
Term text
Code txt
Content rdacontent
337 ## - Media
Term computer
Code c
Media rdamedia
338 ## - Carrier
Term online resource
Code cr
Carrier rdacarrier
490 0# - SERIES STATEMENT
Series statement eBooks on Demand
490 1# - SERIES STATEMENT
Series statement Scalable Computing and Communications Ser.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Intro -- Editor's Notes -- Contents -- On the Role of Distributed Computing in Big Data Analytics -- 1 Introduction -- 2 History and Key Characteristics of Big Data -- 3 Key Aspects of Big Data Analytics -- 4 Popular Technologies for Big Data Analytics Utilizing Concepts of Distributed Computing -- 4.1 Hadoop -- 4.2 Yarn -- 4.3 Hadoop Map Reduce -- 4.4 Spark -- 5 Conclusion -- References -- Fundamental Concepts of Distributed Computing Used in Big Data Analytics -- 1 Introduction -- 2 Multithreading and Multiprocessing -- 2.1 Concept of Multiprocessing -- 2.2 Example of Multiprocessing -- 2.3 Concept of Multithreading -- 2.4 Example of Multithreading -- 2.5 Difference between Multiprocessing and Multithreading -- 3 Computing Architecture in Distributed Computing -- 3.1 SISD -- 3.2 Vector Processor -- 3.3 SIMD -- 3.4 MIMD -- 3.5 SM-MIMD -- 3.6 DM-MIMD -- 4 Scalability in Distributing Computing -- 4.1 Scalability Requirement and Category -- 4.2 Scaling Up -- 4.3 Scaling Out -- 4.4 Prospect of Scale Up and Scale Out -- 5 Queuing Network Model for Distributed Computing -- 5.1 Asynchronous Communication -- 5.2 Queue System -- 5.3 Queue Modeling -- 6 Application of CAP Theorem -- 6.1 Basic Concepts of Consistency, Availability, and Partition Tolerance -- 6.2 Combination of Consistency, Availability, and Partition Tolerance -- 7 Quality of Service (QoS) Requirements in Big Data Analytics -- 7.1 Performance -- 7.2 Interoperability -- 7.3 Fault-Tolerance -- 7.4 Security -- 7.5 Manageability -- 7.6 Load-Balance -- 7.7 High-Availability (HA) -- 7.8 SLA -- 8 Conclusion -- References -- Distributed Computing Patterns Useful in Big Data Analytics -- 1 Introduction -- 2 Primitives for Concurrent Programming -- 2.1 Concurrency Expression -- 2.2 Synchronization -- 3 Communication Protocols and Message Exchange -- 3.1 Synchronous Communication.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 3.2 Asynchronous Communication -- 3.3 Pseudo-Synchronous Communication -- 3.4 Client/Server Paradigm -- 3.5 Communication Deployment in Big Data -- 4 Data Distribution in Big Data on Distributed Environments -- 5 Implementation Problems -- 5.1 Race Condition Problems -- 5.2 Message Exchange -- 6 Conclusion -- References -- Distributed Computing Technologies in Big Data Analytics -- 1 Introduction -- 2 Distributed Database -- 2.1 NoSQL Database -- 3 Distributed Storage -- 3.1 Hadoop Distributed File System (HDFS) -- 4 Distributed Computation -- 4.1 Map-Reduce in Hadoop -- 4.2 Spark -- 5 Machine Learning Platforms -- 6 Search System -- 6.1 Search Software -- 7 Big Data Messaging Software -- 8 Cache -- 8.1 Distributed Caching Systems -- 9 Data Visualization -- 10 Conclusion -- References -- Security Issues and Challenges in Big Data Analytics in Distributed Environment -- 1 Introduction -- 1.1 Security Issues in Big Data in Distributed Environment -- 2 Infrastructure Based Security -- 2.1 Secure Computations -- 2.2 Secure Non-relational Data Stores -- 3 Data Privacy -- 3.1 Privacy Preservation in Data Mining -- 3.2 Cryptography Control Mechanism -- 3.3 Granular Access Control -- 4 Data Integrity and Data Management -- 4.1 Granular Audits -- 4.2 Secure Transactions and Transaction Logs -- 4.3 Data Provenance -- 5 Reactive Security -- 5.1 Input Validation at Distributed Nodes -- 5.2 Real Time Security -- 6 Countermeasures -- 7 Conclusion -- References -- Scientific Computing and Big Data Analytics: Application in Climate Science -- 1 Introduction -- 2 Computational Challenges in Solving Scientific Problems -- 3 Climate Change and Big Data Analytics -- 4 Use Case on Climate Analytics -- 4.1 The Scientific Challenge of the Climate System -- 4.2 Computational Challenge of the Climate Modeling -- 4.3 Post-processing Climate Model Output.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 4.4 BigData Climate Analytics Using Spark -- 5 Conclusions -- References -- Distributed Computing in Cognitive Analytics -- 1 Introduction -- 2 Building Blocks of Cognitive Analytic System -- 2.1 The Data Repositories -- 2.2 The Data Ingestion Tools -- 2.3 The Analytical Frameworks -- 2.4 The Hardware Components -- 2.5 Key Non-functional Requirements to Consider -- 2.5.1 High Concurrency Throughput -- 2.5.2 Interfaces for Interaction with Systems -- 2.5.3 High Availability and Disaster Recovery -- 2.5.4 Linear Scalability -- 2.5.5 Ability to Prioritize Workload -- 2.6 Cognitive System - Implementation Patterns -- 3 Cognitive System - Use Cases -- 3.1 Cognitive Systems in Health Care -- 3.2 Cognitive Systems in Internet of Things Domain -- 3.3 Cognitive Analytics to Become a Customer Centric Organization -- 3.3.1 Next Best Action -- 3.3.2 Changing Engagement Patterns -- 3.3.3 360 ° View of Customer -- 3.3.4 Understand Thy Customer -- 4 Conclusion -- References -- Distributed Computing in Social Media Analytics -- 1 Introduction -- 2 Open Source Tools for Social Media Analytics -- 3 Influencer Analytics -- 3.1 Understanding the Impact of Influencers -- 3.2 Wimbledon Influencer Case Study -- 4 Social Polling -- 4.1 Sentiment Analysis -- 4.2 Intent Detection -- 4.3 Topic Monitoring -- 4.4 User Segmentation -- 4.5 Some Social Polling Examples -- 4.6 Social Polling for Demand Planning -- 5 Conclusion -- References -- Utilizing Big Data Analytics for Automatic Building of Language-agnostic Semantic Knowledge Bases -- 1 Introduction -- 2 Search Engines -- 2.1 Key Technologies -- 2.2 Inverted Index -- 2.3 Sharding of Data -- 2.4 Replication of Data -- 2.5 Denormalized Data Model -- 2.6 Distributed Aggregation and Scoring -- 3 Recommendation Systems -- 4 Semantic Discovery -- 4.1 Problem Description -- 4.2 Semantic Similarity.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 4.3 Probabilistic Semantic Similarity Scoring Using PGMHD -- 4.4 Distributed PGMHD -- 5 Word Sense Ambiguity Detection -- 5.1 Ambiguity Score -- 5.2 Resolving Word Sense Ambiguity -- 6 Semantic Knowledge Graph -- 6.1 Model Structure -- 6.2 Materialization of Nodes and Edges -- 6.3 Discovering Semantic Relationships -- 6.4 Scoring Semantic Relationships -- 6.5 Scaling Characteristics -- 7 Real World Applications -- 8 Conclusion -- References.
588 ## -
-- Description based on publisher supplied metadata and other sources.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Singh Bhadoria, Robin.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Deka, Ganesh Chandra.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Mazumder, Sourav
Title Distributed Computing in Big Data Analytics : Concepts, Technologies and Applications
Place, publisher, and date of publication Cham : Springer,c2017
International Standard Book Number 9783319598338
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Scalable Computing and Communications Ser.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4996470">https://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4996470</a>
Link text Click here to view this ebook.
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBC
901 ## - LOCAL DATA ELEMENT A, LDA (RLIN)
Platform EBL
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic Book
Source of classification or shelving scheme
Holdings
Withdrawn status Lost item Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Full call number Barcode Date last seen Uniform Resource Identifier Price effective from Koha item type
          UT Tyler Online UT Tyler Online Online 2017-11-20 QA75.5-76.95 EBC4996470 2017-11-20 http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4996470 2017-11-20 Electronic Book