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Recent Developments in Data Science and Business Analytics

Proceedings of the International Conference on Data Science and Business Analytics (ICDSBA- 2017)

  • Madjid Tavana
  • Srikanta Patnaik

Part of the Springer Proceedings in Business and Economics book series (SPBE)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Marketing and Supply Chain Analytics

  3. Logistics and Operations Analytics

    1. Front Matter
      Pages 81-81
    2. Lizhong Tong, Taoyu Jia
      Pages 123-130
    3. Shican Liu, Yanli Zhou, Yonghong Wu, Xiangyu Ge
      Pages 139-147
    4. Yanming Yang, Xi Wang, Keliang Jia
      Pages 183-189
  4. Financial Analytics

  5. Predictive Modeling and Data Analytics

    1. Front Matter
      Pages 291-291
    2. Qingliang Li, Lili Xu, Pengliang Zheng, Fei He
      Pages 313-320
    3. Xiao Wang, Sijie Lu, Zhijian Zhou
      Pages 321-326
    4. X. L. Lu, H. X. Wang, Z. X. Zhao
      Pages 327-334
    5. Xiaoman Zhang, Fangqin Xu
      Pages 335-340
    6. Jianhua Liu, Wei Li
      Pages 373-379
    7. Tianbiao Liu, Philippe Fournier-Viger, Andreas Hohmann
      Pages 381-386
    8. Cunlin Li, Lin Zhang, Zhifu Jia
      Pages 387-394

About these proceedings

Introduction

This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the  theoretical knowledge about this upcoming area but also cutting edge applications of this domains.  

Keywords

Data Science Information Systems Business Analytics Descriptive Analytics Prescriptive Analytics Business Intelligence Probability Models Data Mining

Editors and affiliations

  • Madjid Tavana
    • 1
  • Srikanta Patnaik
    • 2
  1. 1.Department of Business Systems and AnalyticsLa Salle UniversityPhiladelphiaUSA
  2. 2.Department of Computer Science and EngineeringSOA UniversityBhubaneswarIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-72745-5
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Business and Management
  • Print ISBN 978-3-319-72744-8
  • Online ISBN 978-3-319-72745-5
  • Series Print ISSN 2198-7246
  • Series Online ISSN 2198-7254
  • Buy this book on publisher's site