MIBDP 2018 - 4th International Workshop on Methodologies to Improve Big Data Projects

As data continues to be produced in massive amounts, with increasing volume, velocity and variety, Big Data projects are growing in frequency and importance. However, the growth in the use of Big Data has outstripped the knowledge of how to support teams that need to do big data projects.
Following our workshops at IEEE Big Data 2015, 2016 & 2017, this workshop will explore methodologies, tools and frameworks that have been or need to be developed to help support big data projects and help frame what a manager should think about when leading a big data science effort.
The workshop will provide a venue to explore new ideas in both possible methodologies and tools, as well case studies that describe examples of what has, or has not, worked within different Big Data teams. Significant work-in-progress papers are also encouraged.
To enable a cross pollination of ideas, the workshop welcomes both academic researchers and industry experts. We invite research results and position statements on topics including, but not limited to:
Team Process Methodologies (ex. Agile) – how should teams collaborate & communicate
Team Roles – what is needed within a big data team
Quality – how to try ensure the results are accurate
Ethics – what are possible model or data concerns (including Model Bias and Transparency)
Analytics Workflow Tools – how to help improve project modularity
CMM (Capability Maturity Model) – is it useful for evaluating Big Data efforts?
Software Engineering Project Management – challenges specific to Big Data projects
Analytics/Model Management – is an audit trail important
Production Robustness – ops migration & monitoring
Evaluation - how to know the effectiveness of a Big Data team
Frameworks - to describe Big Data projects and Big Data project process maturity
Case studies on related topics of interest to this workshop