[AISWorld] CFP HICSS-53 Mini-track on Collaboration for Data Science

[Souren Paul]

Collaboration for Data Science (minitrack)
Collaboration Systems and Technologies (track)
HICSS-53 (January 7-10, 2020)

Collaboration is a critical success factor for data science. Collaboration
enables data scientists to be more productive and efficient in identifying
relevant questions or problems, collecting data from multitude of different
sources, organizing and making sense of the vast majority of data and
information, and communicating their findings in such a way that can be
easily used across different roles in support of business decision making.
The
new actionable knowledge and insights gained is in turn expected to support
achieving collaborative goals such as innovation, idea generation, decision
making, negotiation, and problem solving.

This minitrack invites submissions that address system, technical,
empirical, and theoretical issues  collaboration for data science.
Specifically, the topics of interest include, but are not limited to:


   - Challenges and opportunities of collaboration for data science
   - Collaborative data science
   - Trust in collaborative data science
   - Accountability in data science
   - Human-guided knowledge discovery
   - Humans in the loop data science
   - Collaborative analysis of big data
   - Collaboration for social impact of data science
   - Collaborative collection, aggregation, and organization big data
   - Collaborative management of heterogeneous data
   - Visualization of collaborative big data
   - Data science for collaborative work (decision making, problem solving,
   negotiation, and creativity/innovation)
   - Inter-organizational collaboration in data science
   - Collaborative crowdsourcing analytics
   - Security and privacy issues in collaborative data science
   - Social and psychological issues in collaborative data science
   - Ethical and legal issues in collaborative data science
   - Case studies on collaborative data science
   - Social media driven collaborative data science
   - Network analysis in collaborative data science
   - Best practice for collaboration in data science

Minitrack Co-Chairs:
Lina Zhou
University of North Carolina at Charlotte
lzhou8 at uncc.edu

Souren Paul
Nova Southeastern University
Souren.paul at gmail.com

IMPORTANT DATES FOR PAPER SUBMISSION
June 15, 2019:                        Paper Submission Deadline (11:59 pm
HST)
August 17, 2019:                     Notification of Acceptance/Rejection
September 22, 2019:              Deadline for Final Manuscript