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Big Data and Smart Service Systems.

By: Liu, Xiwei.
Contributor(s): Anand, Rangachari | Xiong, Gang | Shang, Xiuqin | Liu, Xiaoming.
Material type: TextTextSeries: eBooks on Demand.Publisher: San Diego : Elsevier Science, 2016Copyright date: ©2016Description: 1 online resource (233 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9780128120408.Subject(s): Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Big Data and Smart Service SystemsDDC classification: 005.7 LOC classification: QA76.9.B45.B54 2017Online resources: Click here to view this ebook.
Contents:
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.
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.
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.
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.
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.
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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA76.9.B45.B54 2017 (Browse shelf) http://ebookcentral.proquest.com/lib/uttyler/detail.action?docID=4747931 Available EBC4747931

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.

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.

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.

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.

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.

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