Big Data and Smart Service Systems. (Record no. 1014509)

001 - CONTROL NUMBER
control field EBC4747931
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
additional material characteristics m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 171219s2016 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128120408
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Cancelled/invalid ISBN 9780128120132
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC4747931
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL4747931
035 ## - SYSTEM CONTROL NUMBER
System control number (CaPaEBR)ebr11302702
035 ## - SYSTEM CONTROL NUMBER
System control number (CaONFJC)MIL972207
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)964596033
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 QA76.9.B45.B54 2017
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.7
090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC)
Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) QA76.9.B45.B54 2017
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Liu, Xiwei.
245 10 - TITLE STATEMENT
Title Big Data and Smart Service Systems.
264 #1 -
-- San Diego :
-- Elsevier Science,
-- 2016.
264 #4 -
-- ©2016.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (233 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
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Front Cover -- Big Data and Smart Service Systems -- Copyright Page -- Contents -- List of Contributors -- Introduction -- Concepts -- Age of Big Data -- Service Science and System -- Smart Service System -- Techniques and Applications of Big Data -- Characteristics of Big Data -- Techniques of Big Data -- Application of Big Data -- The Framework of the Smart Service System -- Example Analysis -- Government Department -- Public Health -- Business -- Social Management -- Public Safety -- Intelligent Transportation -- Education Industry -- Conclusions -- References -- 1 Vision-based vehicle queue length detection method and embedded platform -- 1.1 Introduction -- 1.2 Embedded Hardware -- 1.3 Algorithms of Video-Based Vehicle Queue Length Detection -- 1.3.1 Vehicle Motion Detection -- 1.3.2 Vehicle Presence Detection -- 1.3.3 Threshold Selection -- 1.3.4 Algorithm Summarization -- 1.4 Program Process of DM642 -- 1.5 Evaluation -- 1.6 Conclusions -- Acknowledgment -- References -- 2 Improved information feedback in symmetric dual-channel traffic -- 2.1 Introduction -- 2.2 CAM and Information Feedback Strategies -- 2.3 Simulation Results -- 2.4 Conclusions -- Acknowledgments -- References -- 3 Secure provable data possession for big data storage -- 3.1 Introduction -- 3.2 Object Storage System Using SPDP -- 3.2.1 Object Storage System -- 3.2.2 Definition of SPDP -- 3.2.3 SPDP Verification Algorithm -- 3.2.4 Hierarchical Structure -- 3.2.5 The Architecture for SPDP Model -- 3.3 Security Analysis and Implementation -- 3.3.1 SPDP Performance Analysis -- 3.3.2 Approved Directories Optimization -- 3.3.3 Secure Protection Strategy -- 3.4 Robust Auditing With Authentication System -- 3.4.1 Robust Auditing -- 3.4.2 Authorized Server -- 3.4.3 Large Object Processing -- 3.5 Experimental Results -- 3.6 Conclusions -- References.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 4 The responsive tourism logistics from local public transport domain: the case of Pattaya city -- 4.1 Introduction -- 4.2 Previous Research -- 4.3 Problems and Challenges -- 4.4 Tourism Demand and Supply Characteristics -- 4.5 Public Transportations -- 4.6 Proximity of Tourist Attractions -- 4.7 Capacity Flexibility Model for Responsive Transportations -- 4.8 Capacity Considerations of Baht Bus Route -- 4.9 Routing for DRT -- References -- 5 Smart cities, urban sensing, and big data: mining geo-location in social networks -- 5.1 Introduction -- 5.2 Systematic Literature Review -- 5.2.1 Question Formulation -- 5.2.2 Source Selection -- 5.2.3 Information Extraction and Result Summarization -- 5.3 Discussion -- 5.3.1 Data Sources -- 5.3.1.1 Twitter -- 5.3.1.2 Foursquare -- 5.3.1.3 Others -- 5.3.2 Mining Techniques -- 5.3.2.1 k-Means -- 5.3.2.2 Self-organizing map -- 5.3.2.3 Density-based clustering -- 5.3.2.4 Spectral clustering -- 5.3.2.5 Mean-shift -- 5.3.2.6 Others -- 5.3.3 Use Scenarios -- 5.3.3.1 Urban characterization -- 5.3.3.2 Spatial discovery -- 5.3.3.3 Exception alerting -- 5.4 Big Data Approach: A Case Study -- 5.5 Conclusion -- References -- 6 Parallel public transportation system and its application in evaluating evacuation plans for large-scale activities -- 6.1 Introduction -- 6.2 Framework of the PPTS -- 6.3 Modeling Participants Using Agent Model -- 6.4 Implementation on Intelligent Traffic Clouds -- 6.5 Case Study -- 6.6 Conclusions -- References -- 7 Predicting financial risk from revenue reports -- 7.1 Introduction -- 7.1.1 Background and Motivation -- 7.1.2 Problems and Challenges -- 7.1.3 Risk Prediction via Active Pairwise Learning -- 7.1.4 Chapter Organization -- 7.2 Related Studies -- 7.3 The Framework of Risk Prediction -- 7.3.1 Overview of the Prediction System -- 7.3.2 Preliminaries and Notations.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 7.3.3 The Prediction Model -- 7.3.4 Nonlinear Dual Optimization -- 7.4 Improving the Model With Humans-in-the-Loop -- 7.4.1 Overview of the Active Prediction System -- 7.4.2 Query Selection Strategy -- 7.4.3 Definition of the LU -- 7.4.4 Definition of the GU -- 7.5 Empirical Evaluation -- 7.5.1 Baselines and Accuracy Measure -- 7.5.2 Data Set and Experimental Settings -- 7.5.3 Result and Discussion -- 7.6 Conclusion -- References -- 8 Novel ITS based on space-air-ground collected Big Data -- 8.1 Introduction -- 8.2 Related R&D Areas: Their Current Situation and Future Trend -- 8.2.1 Cloud Computing and Big Data -- 8.2.2 Remote Sensing Spatial Information -- 8.3 Main Research Contents of Novel ITS -- 8.3.1 ITS Big Data Center -- 8.3.1.1 Public Transit Operation Data -- 8.3.1.2 On-Vehicle Terminal Data -- 8.3.1.3 Crowd Sourcing Road Condition Data -- 8.3.1.4 Intelligent Parking Data -- 8.3.1.5 Spatial Data Collection -- 8.3.2 ITS Cloud Computing Supporting Platform -- 8.3.3 ITS Big Data Application and Service Platform -- 8.4 Technical Solution of Novel ITS -- 8.4.1 The Space-Air-Ground Big Data Collection and Transmission Technology and On-Vehicle Terminals -- 8.4.1.1 The Beidou/GPS Dual-Mode Positioning and Navigation Technology -- 8.4.1.2 Integrated Intelligent Vehicle Terminal -- 8.4.2 The Space-Air-Ground Big Data Fusion and Mining -- 8.4.2.1 Data Fusion -- 8.4.2.2 Data Mining -- 8.4.3 The Space-Air-Ground Big Data Processing -- 8.4.3.1 Parallel Reception of Massive Data -- 8.4.3.2 Segmental Storage of Massive Small Files -- 8.4.3.3 Duplication Storage of Massive Data -- 8.4.3.4 High-Performance Reading of Massive Data -- 8.4.3.5 High-Performance Writing of Massive Data -- 8.4.4 ITS Application of the Space-Air-Ground Big Data -- 8.4.4.1 Traffic Infrastructure Data Extraction and Real-Time Updating.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 8.4.4.2 Live-Action Three-Dimensional Navigation and Intelligent Prewarning -- 8.4.4.3 Driver Behavior Analysis and Prewarning Based on the Big Data of Driving -- 8.5 Conclusions -- Acknowledgments -- References -- 9 Behavior modeling and its application in an emergency management parallel system for chemical plants -- 9.1 Introduction -- 9.2 Closed-Loop Management of ERP -- 9.3 Refined Decomposition of an ERP -- 9.3.1 The Base of the Refined Decomposition -- 9.3.2 The Refined Decomposition Approach -- 9.3.2.1 Cell Activities -- 9.4 Application on ERP Evaluation -- 9.4.1 Evaluation of the ERP Execution Time -- 9.4.2 Usability Evaluation of the ERP -- 9.4.3 Complexity Evaluation of ERP -- 9.4.3.1 Network Analysis Method -- 9.4.3.2 Graph Entropy-Based Evaluation Method -- 9.5 Applications in Emergency Response Training -- 9.6 Applications in Emergency Response Support -- 9.7 Conclusions -- References -- 10 The next generation of enterprise knowledge management systems for the IT service industry -- 10.1 Introduction -- 10.2 IT Service Providers as Knowledge-Based Organizations -- 10.2.1 Task Complexity -- 10.2.2 Cross-Disciplinary Collaboration -- 10.2.3 Complex Interactions -- 10.2.4 Fluid Organizational Structure -- 10.2.5 Information Transfer Restrictions -- 10.2.6 Dual Status of Employees -- 10.2.7 Time Constraints -- 10.2.8 Continuous Education -- 10.3 Requirements for Knowledge Management -- 10.3.1 Knowledge Acquisition -- 10.3.2 Knowledge Maintenance and Curation -- 10.3.3 Knowledge Delivery -- 10.4 Current State of Knowledge Management -- 10.5 Knowledge Management in the Era of Cognitive Computing -- 10.5.1 Unstructured Data -- 10.5.2 Learning by Observation -- 10.5.3 Virtual Agents -- 10.6 Conclusions -- References -- 11 Expertise recommendation and new skill assessment with multicue semantic information -- 11.1 Introduction.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 11.2 Skill Assessment and Use Cases -- 11.3 Methodology -- 11.4 Empirical Study -- 11.5 Conclusion -- References -- 12 On the behavioral theory of the networked firm -- 12.1 Background -- 12.2 Introduction -- 12.3 Network Behaviors in Firms -- 12.4 Functional Network Characteristics -- 12.5 Theoretical Challenges -- 12.6 Network Architecture as a Lens to Firm Behavior -- 12.7 Towards a Behavioral Theory of the Networked Firm -- 12.8 On the Emergence of Multiple Networks -- 12.9 Conclusions -- Acknowledgments -- References -- Index -- Back Cover.
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 Anand, Rangachari.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Xiong, Gang.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Shang, Xiuqin.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Liu, Xiaoming.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Print version:
Main entry heading Liu, Xiwei
Title Big Data and Smart Service Systems
Place, publisher, and date of publication San Diego : Elsevier Science,c2016
International Standard Book Number 9780128120132
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4747931">http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4747931</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 2017-12-20 QA76.9.B45.B54 2017 EBC4747931 2017-12-20 http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4747931 2017-12-20 Electronic Book