Data Science and Business Analytics

Track Chairs

Xiao Fang
University of Delaware
Wolfgang Ketter
Erasmus University, Netherlands
Olivia Sheng
University of Utah, USA
Chihping Wei
National University of Taiwan, Taiwan


Data science and business analytics is an interdisciplinary field of methods, processes and systems that extract knowledge from large volumes of data in a variety of forms. It creates value for businesses, governments and individuals via data-driven decision-making or pattern-based strategy, for example, in marketing, supply-chain, technology, service, healthcare, learning, transportation, energy, environment and engineering management. In addition to data volume and variety, the challenges to seize the value of data stem from the issues associated with the unprecedented speeds at which analytics need to be generated and delivered (velocity), incomplete and inconsistent data (variability), biased, uncertain or ill-intended data (veracity), and protection of private data. When addressing these challenges, value-oriented data science and business analytics research should also consider how to represent, model, analyze, evaluate, optimize and sustain value created by data and analytics.

For this track, we invite paper submissions that propose novel data science methods, techniques, or implementations based on principles in data mining, statistical and machine learning, econometrics, and other computational or quantitative fields. We welcome papers that address important business or decision problems with innovative, value-oriented business analytics in a wide range of contexts, including marketing, healthcare, social network and media, service and product recommendation, cyber and homeland security, transportation, digital learning, ambient computing, and energy, environment and engineering management. We are less interested in existing methods applied to existing problems but with large data sets, conceptual papers on the role of big data, or anecdotes of how big data alone provided managerial insights.

Topics of Interests

Topics of interest include, but are not limited to:

  • Data and text mining for business analytics
  • Web structure, content, and clickstream mining
  • Mining social network (media) data for social network analytics
  • Mining user generated content
  • Multimedia data mining
  • Mining healthcare data for healthcare analytics
  • Mining cloud and technology management data for cloud and technology analytics
  • Mining event and network data for transportation and security analytics
  • Digital learning analytics
  • Online deception detection
  • Financial fraud analytics
  • Relational and network data analytics
  • Energy, environmental and scientific analytics
  • Recommender systems
  • Statistical and econometric methods that fits business analytics
  • Real-time data analytics
  • Data quality improvement for business analytics
  • Visual analytics
  • Representation, analysis and sustainment of value created by data analytics
  • Value-oriented business analytics and utility-based data mining
  • Cost-sensitive data analytics
  • Privacy-preserving data mining and sharing
  • Data driven decision analysis and optimization

Associate Editors

  • Ahmed Abbasi, U. of Virginia, USA
  • Bart Baesens, Katholieke Universiteit, Leuven, Belgium
  • Sid Bhattacharyya, U. of Illinois at Chicago, USA
  • Martin Bichler, Technische Universität München, Germany
  • Jesse Bockstedt, U. of Arizona, USA
  • Michael Chau, The U. of Hong Kong, Hong Kong
  • Tomer Geva, Tel Aviv University, Israel
  • San-Yih Hwang, National Sun Yat-Sen U., Taiwan
  • Wolfgang Jank, U. of South Florida, USA
  • Thomas Lee, University of California, Berkeley
  • Xiaobai (Bob) Li, U. of Massachusetts at Lowell, USA
  • Yung-Ming Li, National Chiao Tung U., Taiwan
  • Zhepeng Li, York U., Canada
  • Hongyan Liu, Tsinghua U., China
  • Shawn Mankad, Cornell U., USA
  • James R. Marsden, U. of Connecticut, USA
  • David Martens, U. of Antwerp, Belgium
  • Balaji Padmanabhan, University of South Florida, USA
  • Gautam Pant, U. of Iowa, USA
  • Sarchar Reichmann, Massachusetts Institute of Technology, USA
  • Maytal Saar-Tsechansky, University of Texas, Austin, USA
  • Sumit Sarkar, U. of Texas at Dallas, USA
  • Soumya Sen, U. of Minnesota, USA
  • Galit Shmuéli, National Tsing Hua Univeristy, Taiwan
  • Nick Street, U. of Iowa, USA
  • Aixin Sun, Nanyang Technological U., Singapore
  • Hui Xiong, Rutgers U., USA
  • Junming Yin, U. of Arizona, USA
  • Daniel Zeng, U. of Arizona, USA
  • Dongsong Zhang, U. of Maryland, Baltimore County, USA
  • Kunpeng Zhang, U. of Maryland, USA
  • Zhu Zhang, Iowa State U., USA
  • Kang Zhao, U. of Iowa, USA
  • Wenjun Zhou, U. of Tennessee, USA