-------- Forwarded Message -------- Subject: [AISWorld] CFP HICSS53 Minitrack: Collaboration for Data Science Date: Thu, 16 May 2019 20:35:15 -0400 From: Lina Zhou lzhou8@uncc.edu To: aisworld@lists.aisnet.org

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

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@uncc.edu

Souren Paul Nova Southeastern University Souren.paul@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 _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org