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Big data analytics for sensor-network collected intelligence / (notice n° 58974)

000 -LEADER
fixed length control field 11452cam a22003977i 4500
001 - CONTROL NUMBER
control field 19336629
003 - CONTROL NUMBER IDENTIFIER
control field IMIST
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230106153159.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 161012t20172017enka b 001 0 eng d
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2016956355
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128093931
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 0128093935
040 ## - CATALOGING SOURCE
Original cataloging agency BTCTA
Language of cataloging eng
Transcribing agency BTCTA
Description conventions rda
Modifying agency YDX
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-- WSU
-- OCLCO
-- OCLCF
-- WAU
-- UPM
-- OCLCA
-- U3G
-- DLC
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number 22
Classification number 005.7
245 00 - TITLE STATEMENT
Title Big data analytics for sensor-network collected intelligence /
Statement of responsibility, etc edited by Hui-Huang Hsu, Chuan-Yu Chang, Ching-Hsien Hsu.
264 #1 - Production, Publication, Distribution, Manufacture, and Copyright Notice
Place of production, publication, distribution, manufacture London, United Kingdom :
Name of producer, publisher, distributor, manufacturer Academic Press, an imprint of Elsevier,
Date of production, publication, distribution, manufacture, or copyright notice [2017]
264 #4 - Production, Publication, Distribution, Manufacture, and Copyright Notice
Date of production, publication, distribution, manufacture, or copyright notice 2017
300 ## - PHYSICAL DESCRIPTION
Extent xx, 306 pages :
Other physical details illustrations ;
Dimensions 24 cm.
336 ## - CONTENT TYPE
Content Type Term text
Content Type Code txt
Source rdacontent
337 ## - MEDIA TYPE
Media Type Term unmediated
Media Type Code n
Source rdamedia
338 ## - CARRIER TYPE
Carrier Type Term volume
Carrier Type Code nc
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Intelligent data-centric systems
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Includes bibliographical references and index.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Machine generated contents note: pt. I BIG DATA ARCHITECTURE AND PLATFORMS -- ch. 1 Big Data: A Classification of Acquisition and Generation Methods -- 1.Big Data: A Classification -- 1.1.Characteristics of Big Data -- 2.Big Data Generation Methods -- 2.1.Data Sources -- 2.2.Data Types -- 3.Big Data: Data Acquisition Methods -- 3.1.Interface Methods -- 3.2.Interface Devices -- 4.Big Data: Data Management -- 4.1.Data Representation and Organization -- 4.2.Databases -- 4.3.Data Fusion and Data Integration -- 5.Summary -- References -- Glossary -- ch. 2 Cloud Computing Infrastructure for Data Intensive Applications -- 1.Introduction -- 2.Big Data Nature and Definition -- 2.1.Big Data in Science and Industry -- 2.2.Big Data and Social Network/Data -- 2.3.Big Data Technology Definition: From 6V to 5 Parts -- 3.Big Data and Paradigm Change -- 3.1.Big Data Ecosystem -- 3.2.New Features of the BDI -- 3.3.Moving to Data-Centric Models and Technologies -- 4.Big Data Architecture Framework and Components -- 4.1.Defining the Big Data Architecture Framework -- 4.2.Data Management and Big Data Lifecycle -- 4.3.Data Structures and Data Models for Big Data -- 4.4.NIST Big Data Reference Architecture -- 4.5.General Big Data System Requirements -- 5.Big Data Infrastructure -- 5.1.BDI Components -- 5.2.Big Data Stack Components and Technologies -- 5.3.Example of Cloud-Based Infrastructure for Distributed Data Processing -- 5.4.Benefits of Cloud Platforms for Big Data Applications -- 6.Case Study: Bioinformatics Applications Deployment on Cloud -- 6.1.Overall Description -- 6.2.UC1 -- Securing Human Biomedical Data -- 6.3.UC2 -- Cloud Virtual Pipeline for Microbial Genomes Analysis -- 6.4.Implementation of Use Cases and CYCLONE Infrastructure Components -- 7.CYCLONE Platform for Cloud Applications Deployment and Management -- 7.1.General Architecture for Intercloud and Multicloud Applications Deployment -- 7.2.Ensuring Consistent Security Services in Cloud-Based Applications -- 7.3.Dynamic Access Control Infrastructure -- 8.Cloud Powered Big Data Applications Development and Deployment Automation -- 8.1.Demand for Automated Big Data Applications Provisioning -- 8.2.Cloud Automation Tools for Intercloud Application and Network Infrastructure Provisioning -- 8.3.Slipstream: Cloud Application Management Platform -- 9.Big Data Service and Platform Providers -- 9.1.Amazon Web Services and Elastic MapReduce -- 9.2.Microsoft Azure Analytics Platform System and HDInsight -- 9.3.IBM Big Data Analytics and Information Management -- 9.4.Cloudera -- 9.5.Pentaho -- 9.6.LexisNexis HPCC Systems as an Integrated Open Source Platform for Big Data Analytics -- 10.Conclusion -- Acknowledgments -- References -- Glossary -- ch. 3 Open Source Private Cloud Platforms for Big Data -- 1.Cloud Computing and Big Data as a Service -- 1.1.Public Cloud Infrastructure -- 1.2.Advantages of the Cloud for Big Data -- 2.On-Premise Private Clouds for Big Data -- 2.1.Security of Cloud Computing Systems -- 2.2.Advantages of On-Premise Private Clouds -- 3.Introduction to Selected Open Source Cloud Environments -- 3.1.OpenNebula -- 3.2.Eucalyptus -- 3.3.Apache CloudStack -- 3.4.OpenStack -- 4.Heterogeneous Computing in the Cloud -- 4.1.Exclusive Mode -- 4.2.Sharing Mode -- 5.Case Study: The EMS, an On-Premise Private Cloud -- 6.Conclusion -- Disclaimer -- References -- pt. II BIG DATA PROCESSING AND MANAGEMENT -- ch. 4 Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud -- 1.Introduction -- 1.1.Motivation -- 1.2.Organization of the Chapter -- 2.Related Work and Problem Analysis -- 2.1.Related Work -- 2.2.Problem Analysis: Real-World Requirements for Nonlinear Regression -- 3.Temporal Compression Model Based on Nonlinear Regression -- 3.1.Nonlinear Regression Prediction Model -- 4.Algorithms -- 4.1.Algorithm for Nonlinear Regression -- 4.2.Nonlinear Regression Compression Algorithm Based on MapReduce -- 5.Experiments -- 5.1.Experiment Environment and Process -- 5.2.Experiment for the Compression With Nonlinear Regression -- 5.3.Experiment for Data Loss and Accuracy -- 6.Conclusions and Future Work -- References -- ch. 5 Big Data Management on Wireless Sensor Networks -- 1.Introduction -- 2.Data Management on WSNs -- 2.1.Storage -- 2.2.Query Processing -- 2.3.Data Collection -- 3.Big Data Tools -- 3.1.File System -- 3.2.Batch Processing -- 3.3.Streaming Data Processing -- 4.Put It Together: Big Data Management Architecture -- 4.1.Batch Layer -- 4.2.Serving Layer -- 4.3.Speed Layer -- 5.Big Data Management on WSNs -- 5.1.In-Network Aggregation Techniques and Data Integration Components -- 5.2.Exploiting Big Data Systems as Data Centers -- 6.Conclusion -- References -- Glossary -- ch. 6 Extreme Learning Machine and Its Applications in Big Data Processing -- 1.Introduction -- 1.1.Background -- 1.2.Artificial Neural Networks -- 1.3.Era of Big Data -- 1.4.Organization -- 2.Extreme Learning Machine -- 2.1.Traditional Approaches to Train ANNs -- 2.2.Theories of the Extreme Learning Machine -- 2.3.Classical ELM -- 2.4.ELM for Classification and Regression -- 2.5.ELM for Unsupervised Learning -- 3.Improved Extreme Learning Machine With Big Data -- 3.1.Shortcomings of the Extreme Learning Machine for Processing Big Data -- 3.2.Optimization Strategies for the Traditional Extreme Learning Machine -- 3.3.Efficiency Improvement for Big Data -- 3.4.Parallel Extreme Learning Machine Based on MapReduce -- 3.5.Parallel Extreme Learning Machine Based on Apache Spark -- 4.Applications -- 4.1.ELM in Predicting Protein Structure -- 4.2.ELM in Image Processing -- 4.3.ELM in Cancer Diagnosis -- 4.4.ELM in Big Data Security and Privacy -- 5.Conclusion -- References -- Glossary -- pt. III BIG DATA ANALYTICS AND SERVICES -- ch. 7 Spatial Big Data Analytics for Cellular Communication Systems -- 1.Introduction -- 2.Cellular Communications and Generated Data -- 3.Spatial Big Data Analytics -- 3.1.Statistical Foundation for Spatial Big Data Analytics -- 3.2.Spatial Pattern Mining From Spatial Big Data Analytics -- 4.Typical Applications -- 4.1.BS Behavior Understanding Through Spatial Big Data Analytics -- 4.2.User Behavior Understanding Through Spatial Big Data Analytics -- 5.Conclusion and Future Challenging Issues -- Acknowledgments -- References -- Glossary -- ch. 8 Cognitive Applications and Their Supporting Architecture for Smart Cities -- 1.Introduction -- 2.CSE for Smart City Applications -- 2.1.Architecture Specification -- 2.2.Big Data Analysis and Management -- 3.Anomaly Detection in Smart City Management -- 3.1.Related Work to Anomaly Detection -- 3.2.Challenges and Benefits of Anomaly Detection in Smart Cities -- 4.Functional Region and Socio-Demographic Regional Patterns Detection in Cities -- 4.1.Discovering Functional Regions -- 4.2.Deep Learning and Regional Pattern Detections -- 5.Summary -- References -- Glossary -- ch. 9 Deep Learning for Human Activity Recognition -- 1.Introduction -- 2.Motivations and Related Work -- 3.Convolutional Neural Networks in HAR -- 3.1.Temporal Convolution and Pooling -- 3.2.Architecture -- 3.3.Analysis -- 4.Experiments, Results, and Discussion -- 4.1.Experiment on OAR Dataset -- 4.2.Experiment on Hand Gesture Dataset -- 4.3.Experiment on REALDISP Dataset -- 4.4.Computational Requirements -- 4.5.Future Directions -- 5.Conclusion -- References -- Glossary -- ch. 10 Neonatal Cry Analysis and Categorization System Via Directed Acyclic Graph Support Vector Machine -- 1.Introduction -- 2.Neonatal Cry Analysis and Categorization System -- 2.1.Cry Signal Preprocessing -- 2.2.Feature Extraction -- Essential Features -- 2.3.Selection of Features -- 2.4.Categorization and Validation -- 3.Experimental Results and Discussion -- 3.1.Environment of the Experiments -- 3.2.Experiment 1: Neonatal Cry Analysis and Categorization -- Employing 15 Extracted Features -- 3.3.Experiment 2: Neonatal Cry Analysis and Categorization -- Deploying the Selected Four Features -- 3.4.Experiment 3: Comparison of Neonatal Cry Analysis and Categorization Between Male and Female Babies -- 3.5.Experiment 4: Comparison of Proposed System With Y. Abdulaziz's Approach -- 4.Conclusion -- Acknowledgment -- References -- pt. IV BIG DATA INTELLIGENCE AND IoT SYSTEMS -- ch. 11 Smart Building Applications and Information System Hardware Co-Design -- 1.Smart Building Applications -- 1.1.The Ever-Increasing Need for Smart Buildings -- 1.2.Smart Building Applications -- 2.Emerging Information System Hardware -- 2.1.Overview -- 2.2.Examples -- 3.Big Data Application and Information Hardware Co-Design -- 3.1.Motivation and Challenge -- 3.2.Case Study and Discussion -- 4.Conclusions -- References -- Glossary -- ch. 12 Smart Sensor Networks for Building Safety -- 1.Introduction -- 2.Related Works -- 3.Background: Modal Analysis -- 3.1.Modal Parameters -- 3.2.The ERA -- 4.Distributed Modal Analysis -- 4.1.Stage 1: Try to Distribute the Initial Stage of Modal Analysis Algorithms... -- 4.2.Stage 2: Divide and Conquer -- 5.A Multiscale SHM Using Cloud -- 6.Conclusion -- Acknowledgments -- References -- Glossary -- ch. 13 The Internet of Things and Its Applications -- 1.Introduction -- 2.Collection of Big Data From IoT -- 2.1.MQ Telemetry Transport -- 2.2.Constrained Application Protocol -- 2.3.MQTT vs. CoAP -- 3.IoT Analytics -- 3.1.Related Works -- 3.2.Outlier Detection for Big Data -- 3.3.Island-Based Cloud GA -- 4.Examples of IoT Applications -- 4.1.Applications on Intelligent Transportation Systems -- 4.2.Applications on Intelligent Manufacturing Systems -- 5.Conclusions -- References -- Glossary -- ch. 14 Smart Railway Based on the Internet of Things -- 1.Introduction -- 2.Architecture of the Smart Railway -- 2.1.Overview -- 2.2.Perception and Action Layer -- 2.3.Transfer Layer -- 2.4.Data Engine Layer -- 2.5.Application Layer -- 3.IRIS for Smart Railways -- 3.1.Rail Defects -- 3.2.The State-of-the-Art for Rail Inspection
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Note continued: 3.3.Rail Inspection Based on the IoT and Big Data -- 4.Conclusion -- Acknowledgment -- References -- Glossary.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Wireless sensor networks.
9 (RLIN) 5165
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Cloud computing.
9 (RLIN) 215222
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data.
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Cloud computing.
Source of heading or term fast
9 (RLIN) 215222
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Wireless sensor networks.
Source of heading or term fast
9 (RLIN) 5165
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Hsu, Hui-Huang,
Dates associated with a name 1965-
Relator term editor.
9 (RLIN) 215223
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Chang, Chuan-Yu,
Relator term editor.
9 (RLIN) 215224
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Hsu, Ching-Hsien,
Relator term editor.
9 (RLIN) 215225
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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