[AISWorld] Final CFP: HICSS-56 Minitrack on Data Science for Digital Collaboration

[Souren Paul]

The 56th Hawaii International Conference on System Sciences Minitrack on
Data Science for Digital Collaboration

The evolution of digital collaboration technologies over the past decades
has given rise to a new application domain of data science. These emerging
technologies range from collaboration software for business such as
enterprise collaboration systems and social networking tools, distributed
ledger technology, and Internet of Things, to public collaboration
platforms such as social media crowdsourcing platforms, and open source
communities (e.g., GitHub). Businesses and organizations use these systems
for collaboration on projects, knowledge management, supply chain
management, and customer relationship management. The public can use them
for collaboration, information sharing, community support, and social
goods. Applying data science in these domains allows for leveraging the
newly available digital trace data for gaining actionable knowledge and
insights on how people collaborate and how these platforms facilitate
digital collaboration, which can lead to improvements in system design as
well as collaboration processes including problem solving and decision
making.

This minitrack seeks papers that address, but are not limited to, the
following topics:


   -    Social analytics in digital collaboration
   -    Process mining in digital collaboration
   -    Data science in blockchain and distributed ledger applications
   -    Data science in Internet of Things
   -    Data science in human-machine collaboration
   -    Data science in digital collaboration on social goods
   -    Collaborative analysis of big data
   -    Collaborative collection and management of big data
   -    Visualization of big data in digital collaboration
   -    Crowdsourcing analytics
   -    Security and privacy analytics in digital collaboration
   -    Human-guided knowledge discovery
   -    Humans in the loop data science
   -    Ethical and legal issues in collaborative data science
   -    Case studies on collaborative data science
   -    Best practice for collaboration in data science


Important Dates


   - June 15            Papers due
   - August 17         Notification of acceptance/rejection
   - September 4     Deadline for authors whose papers are conditionally
   accepted to submit a revised manuscript
   - September 22   Deadline for authors to submit final Manuscript for
   publication




Minitrack Co-Chairs:

Lina Zhou, University of North Carolina at Charlotte, USA

Souren Paul, Northern Kentucky University, USA

Florian Schwade, University of Koblenz-Landau, Germany