Big Data and Smart Service Systems

Big Data and Smart Service Systems

1st Edition - November 22, 2016

Write a review

  • Authors: Xiwei Liu, Rangachari Anand, Gang Xiong, Xiuqin Shang, Xiaoming Liu
  • Hardcover ISBN: 9780128120132
  • eBook ISBN: 9780128120408

Purchase options

Purchase options
DRM-free (EPub, PDF, Mobi)
Sales tax will be calculated at check-out

Institutional Subscription

Free Global Shipping
No minimum order


Big Data and Smart Service Systems presents the theories and applications regarding Big Data and smart service systems, data acquisition, smart cities, business decision-making support, and smart service design. The rapid development of computer and Internet technologies has led the world to the era of Big Data. Big Data technologies are widely used, which has brought unprecedented impacts on traditional industries and lifestyle. More and more governments, business sectors, and institutions begin to realize data is becoming the most valuable asset and its analysis is becoming the core competitiveness.

Key Features

  • Describes the frontier of service science and motivates a discussion among readers on a multidisciplinary subject areas that explores the design of smart service
  • Illustrates the concepts, framework, and application of big data and smart service systems
  • Demonstrates the crucial role of smart service to promote the transformation of the regional and global economy


Graduates and researchers in information science or service science and management. Government officials, enterprise managers, relevant professional personnel and other people who are interested in service science

Table of Contents

  • Chapter 1. Vision-based vehicle queue length detection method and embedded platform

    • Abstract
    • 1.1 Introduction
    • 1.2 Embedded Hardware
    • 1.3 Algorithms of Video-Based Vehicle Queue Length Detection
    • 1.4 Program Process of DM642
    • 1.5 Evaluation
    • 1.6 Conclusions
    • Acknowledgment
    • References

    Chapter 2. Improved information feedback in symmetric dual-channel traffic

    • Abstract
    • 2.1 Introduction
    • 2.2 CAM and Information Feedback Strategies
    • 2.3 Simulation Results
    • 2.4 Conclusions
    • Acknowledgments
    • References

    Chapter 3. Secure provable data possession for big data storage

    • Abstract
    • 3.1 Introduction
    • 3.2 Object Storage System Using SPDP
    • 3.3 Security Analysis and Implementation
    • 3.4 Robust Auditing with Authentication System
    • 3.5 Experimental Results
    • 3.6 Conclusions
    • References

    Chapter 4. The responsive tourism logistics from local public transport domain: the case of Pattaya city

    • Abstract
    • 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

    Chapter 5. Smart cities, urban sensing, and big data: mining geo-location in social networks

    • Abstract
    • 5.1 Introduction
    • 5.2 Systematic Literature Review
    • 5.3 Discussion
    • 5.4 Big Data Approach: A Case Study
    • 5.5 Conclusion
    • References

    Chapter 6. Parallel public transportation system and its application in evaluating evacuation plans for large-scale activities

    • Abstract
    • 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

    Chapter 7. Predicting financial risk from revenue reports

    • Abstract
    • 7.1 Introduction
    • 7.2 Related Studies
    • 7.3 The Framework of Risk Prediction
    • 7.4 Improving the Model with Humans-in-the-Loop
    • 7.5 Empirical Evaluation
    • 7.6 Conclusion
    • References

    Chapter 8. Novel ITS based on space-air-ground collected Big Data

    • Abstract
    • 8.1 Introduction
    • 8.2 Related R&D Areas: Their Current Situation and Future Trend
    • 8.3 Main Research Contents of Novel ITS
    • 8.4 Technical Solution of Novel ITS
    • 8.5 Conclusions
    • Acknowledgments
    • References

    Chapter 9. Behavior modeling and its application in an emergency management parallel system for chemical plants

    • Abstract
    • 9.1 Introduction
    • 9.2 Closed-Loop Management of ERP
    • 9.3 Refined Decomposition of an ERP
    • 9.4 Application on ERP Evaluation
    • 9.5 Applications in Emergency Response Training
    • 9.6 Applications in Emergency Response Support
    • 9.7 Conclusions
    • References

    Chapter 10. The next generation of enterprise knowledge management systems for the IT service industry

    • Abstract
    • 10.1 Introduction
    • 10.2 IT Service Providers as Knowledge-Based Organizations
    • 10.3 Requirements for Knowledge Management
    • 10.4 Current State of Knowledge Management
    • 10.5 Knowledge Management in the Era of Cognitive Computing
    • 10.6 Conclusions
    • References

    Chapter 11. Expertise recommendation and new skill assessment with multicue semantic information

    • Abstract
    • 11.1 Introduction
    • 11.2 Skill Assessment and Use Cases
    • 11.3 Methodology
    • 11.4 Empirical Study
    • 11.5 Conclusion
    • References

    Chapter 12. On the behavioral theory of the networked firm

    • Abstract
    • 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

Product details

  • No. of pages: 232
  • Language: English
  • Copyright: © Academic Press 2016
  • Published: November 22, 2016
  • Imprint: Academic Press
  • Hardcover ISBN: 9780128120132
  • eBook ISBN: 9780128120408

About the Authors

Xiwei Liu

Dr. Xiwei Liu is an associate professor in the State Key Laboratory of Management and Control for Complex Systems Automation Institute, Chinese Academy of Sciences. In 2006, he received Ph.D. degree in human factor engineering from the System Control and Management Laboratory, Nara Institute of Science and Technology, Japan. Then he worked there as a post doctor and assistant professor. From 2007 to 2009, he worked as a system engineer in Japan supplying research and development, consultant services of management information system for Toyota Motor Corporation, Japan Display Inc. (Hitachi Displays, Ltd.), Bank of Tokyo-Mitsubishi UFJ, etc. Since 2009, he has joined the State Key Laboratory of Management and Control for Complex Systems. His research interest covers sensor networks, human factor engineering, modeling and simulation of operator or organizational behavior, management information systems, cloud computing, etc. In recent years, he has published about 50 papers in academic journals and conferences.

Affiliations and Expertise

Associate professor, State Key Laboratory of Management and Control, Complex Systems Automation Institute, Chinese Academy of Sciences, China

Rangachari Anand

Dr. Rangachari Anand is a research staff member in the Business Service Solution department at the IBM T. J. Watson Research Center in New York. He completed his PhD in Computer Science at Syracuse University. His dissertation quantified the advantage of using modular neural networks for classification tasks. He has worked in a number of areas ranging from distributed systems to artificial intelligence and cognitive science. In the past few years, he has worked on applying advanced artificial technologies both internally to improve IBM’s service business and to improve the productivity, profitability and quality of IBM’s clients at a global level. He has received several technical achievement awards for his work. He has been prolific in publishing research work in books, papers in top academic journals, conferences and workshops. He has led the filing of more than 30 patent applications in the US and other countries

Affiliations and Expertise

Business Service Solution Department, IBM T. J. Watson Research Center, NY, USA

Gang Xiong

Dr. Gang Xiong received his Ph.D. degrees in Control Science and Engineering in 1996 from Shanghai Jiao Tong University, CHINA. From 1996 to 1998, he worked as Post doctor and Associate Professor with the Institute of Industrial Process Control, Zhejiang University, CHINA. From 1998 to 2007, he successively worked with Automation and Control Institute, Tampere University of Technology, FINLAND (1998-2001), Nokia Corporation, FINLAND (2001-2007), and Accenture and Chevron, USA (2007). In 2008, he worked as Deputy Director with Informatization Office, CAS, CHINA. In 2009, he started his current work as Professor of State Key Laboratory of Management and Control of Complex Systems, Institute of Automation, CAS, CHINA. In 2011, he became Deputy Director of Beijing Engineering Research Center for Intelligent Systems and Technology, Deputy Director of Cloud Computing Center of Chinese Academy of Sciences. He is an IEEE Senior member, INFORMS member, INCOSE member, Committee member of Chinese Association of Automation, MARQUIS WHO'S WHO in the world 2007, General Chair of IEEE SOLI 2011, SOLI 2012, and SOLI 2013 etc.

Affiliations and Expertise

Deputy Director, Beijing Engineering Research Center for Intelligent Systems and Technology, China

Xiuqin Shang

Dr. Xiuqin Shang is an assistant researcher in the State Key Laboratory of Management and Control for Complex Systems Automation Institute, Chinese Academy of Sciences. In 2005, she completed her bachelor’s degree from the Industrial Automation Branch of Information and Control Engineering Department in China University of Petroleum. In 2010, she earned her PhD degree of engineering from Zhejiang University, majoring in the Control Science and Control Engineering. Her main research area of interest is in the complex system modeling and optimization, social manufacturing, petrochemical industrial processing modeling and optimization, and so on. She was the chair of Award Committee in 2012 SOLI and 2013 SOLI. She has published about 20 research papers in key journals and conferences.

Affiliations and Expertise

Assistant researcher, State Key Laboratory of Management and Control for Complex Systems Automation Institute, Chinese Academy of Sciences, China

Xiaoming Liu

Dr. Xiaoming Liu works at Beijing Key Lab of Urban Intelligent Traffic Control Tech of North China University of Technology. In 2004, he completed his doctorate degree from Chinese Academy of Sciences, majoring in control theory and control engineering of institute of automation. In recent years, he has presided more than ten projects, such as the State Natural Science Foundation, the National High-tech Research and Development Program (863 Torch Program), National Science and Technology Support Plan Corpus, Science and Technology Plan Projects of Beijing Commission of Science and Technology, Beijing Outstanding Talent Training Projects, etc. He has published more than 40 papers in such publications as China Journal of Highway, Journal of Transportation Systems Engineering and Information Technology, Engineering Science of Journal of Jilin University.

Affiliations and Expertise

Researcher, Beijing Key Lab of Urban Intelligent Traffic Control Tech, North China University of Technology, China

Ratings and Reviews

Write a review

There are currently no reviews for "Big Data and Smart Service Systems"