Methodologies to Improve big Data Projects

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Papers from our 2015 Workshop  

The first annual workshop on methodologies and tools to improve big data projects was held in conjunction with IEEE Big Data 2015, in Santa Clara, CA, USA on Oct 29 – Nov 1, 2015. 

In this workshop, we explored methodologies, processes, frameworks and tools to support data teams - that have been or need to be developed to help support Big Data projects. 

The workshop provided a venue to explore new ideas in possible methodologies as well case studies that describe examples of what has, or has not, worked within different Big Data teams. To enable a cross pollination of ideas, the workshop welcomed both academic researchers and industry experts.

Specifically, the following papers were presented:
  • Business Information Modeling: A Methodology for Data-Intensive Projects, Data Science and Big Data Governance (paper, slides)
  • Exploring the Process of Doing Data Science Via an Ethnographic Study of a Media Advertising Company (paper, slides)
  • Towards Methods for Systematic Research On Big Data  (paper, slides)
  • Towards A Big Data Theory Model  (paper, slides)
  • Three Critical Matters in Big Data Projects for e-Science  (paper, slides)
  • The Need for New Processes, Methodologies and Tools to Support Big Data Teams and Improve Big Data Project Effectiveness  (paper, slides)
 Note that papers will link to IEEE Explore database (once the papers are loaded into the system)

Program Committee for the workshop
  • Jeffrey Saltz, Syracuse University (Chair)
  • Bintong Chen, University of Delaware 
  • Kevin Crowston, Syracuse University
  • Alexandros Labrinidis, University of Pittsburgh 
  • Jason Dedrick, Syracuse University
  • Rakshit Kapoor, Hartford Insurance
  • Tayo Ibikunle, JP Morgan Chase

Contact / Questions
Please email any questions to jsaltz[at]syr.edu

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