The third annual workshop on methodologies and tools to improve big data projects was held in conjunction with IEEE Big Data 2017, in Boston, Mass, USA on Dec 11 – Dec 14, 2017.
In this workshop, we explored two literature reviews, several papers on methodologies and case studies as well as one general purpose tool 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.
Specifically, the following papers were presented:
Program Committee for the workshop
Contact / Questions
Please email any questions to jsaltz[at]syr.edu
Please join our group
In this workshop, we explored two literature reviews, several papers on methodologies and case studies as well as one general purpose tool 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.
Specifically, the following papers were presented:
- Saving Costs with a Big Data Strategy (paper)
- Towards a Requirements Engineering Artefact Model (paper)
- Does Pair Programming work in a Data Science Context (paper)
- Predicting Outcomes for Big Data Projects: Big Data Project Dynamics (paper)
- The Ambiguity of Data Science Team Roles and the Need for a Data Science Workforce Framework (paper)
- Make Accumulated Data in Companies Eloquent by SQL Statement Constructors (paper)
- Agile Big Data Analytics - AnalyticsOps for Data Science (paper)
Program Committee for the workshop
- Jeffrey Saltz, Syracuse University (Chair)
- Daniel Asamoah, Wright State University
- Kirk Kee, Chapman University
- Patrick Mikalef, Norwegian University of Science and Technology
- Andy Koronios, University of South Australia
- Ivan Shamshurin, Syracuse University
Contact / Questions
Please email any questions to jsaltz[at]syr.edu
Please join our group