Data Science, Decision analytics and visualization

Track Chairs

Jeffrey Hu

Georgia Institute of Technology’s Scheller College

Ting Li

Erasmus University

Jiayin Qi

Beijing University of Posts and Telecommunications


Data science and analytics is an interdisciplinary field of methods, processes and systems that extract business insights from large volumes of data in a variety of forms. It creates value for businesses, governments and individuals by helping them make better and smarter decisions. In addition to data volume and variety, the challenges of generating value from 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, financial services, cyber and homeland security, transportation, 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 Interest

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
  • Energy, environmental and scientific analytics
  • Statistical and econometric methods that fit business analytics
  • Real-time data analytics
  • Visual analytics

Associate Editors

  • Dongsong Zhang, University of Maryland, Baltimore County, USA
  • Beibei Li, Carnegie Mellon University, USA
  • Dimitrios Tsekouras, Erasmus University, The Netherlands
  • Rodrigo Belo, Erasmus University, The Netherlands
  • Yixin Lu, George Washington University, USA
  • Jing Gong, Temple University, USA
  • Jingjing Zhang, Indiana University, USA
  • Miguel Godinho de Matos, Católica Lisbon School of Business & Economics, Portugal
  • Wenjing Duan, George Washington U., USA
  • Juhee Kwon, City University of HongKong, Hong Kong
  • Qian Tang, Singapore Management University
  • Huaxia Rui, University of Rochester
  • Bin Zhang, University of Arizona, USA
  • Dokyun Lee, Carnegie Mellon University, USA
  • Tuan Phan, National University of Singapore
  • Rajiv Garg, UT Austin, USA
  • Hongyan Liu, Tsinghua U., China
  • Xitong Li, HEC Paris, France
  • Inbal Yahav, Bar Ilan University, Israel
  • Tomer Geva, Tel Aviv University
  • Pedro Ferreira, Carnegie Mellon University, USA
  • Frank Nagle, University of Southern California, USA
  • Animesh Animesh, McGill University, Canada
  • Ohad Barzilay, Tel Aviv U., Israel
  • Soumya Sen, University of Minnesota, USA
  • Xiaobai Li, University of Massachusetts Lowell
  • Onn Shehory, Bar Ilan University, Israel
  • Dan Zhu, Iowa State University, USA
  • Yi-Jen Ian Ho, Penn State University, USA
  • Jorge Mejia, Indianna, USA
  • Panos Adamopolous, University of Minnesota, USA
  • Vilma Todri, Emory, USA
  • Christos Nicolaides, MIT, USA
  • Edward McFowland, University of Minnesota, USA