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For Collaboration Systems and Technologies Track
Hawaii International Conference on System Sciences
HICSS-52: Jan 8th to 11th 2019 | Maui, Hawaii
Call for Papers
Data science and analytics for collaboration is the study of
generalizable extraction of knowledge from structured and/or
unstructured data to support human collaboration within and across
groups and organizations. The new actionable knowledge gained is
expected to support achieving collaborative goals such as
innovation, idea generation, decision making, negotiation, and
execution. Data science and analytics for collaboration couples a
systematic study of collection, aggregation, organization,
processing, and analysis of data. In addition, it requires deep
understanding of formulating problems valuable for collaboration,
engineering effective solutions to the collaboration problems, and
ways to effectively communicate findings across roles ranging from
business managers to data analysts. There is a continued interest
in organizations looking for ways to increase value from data
science and using it to address business challenges. One promising
way for businesses and organizations to enhance their performance
or competitiveness is by investigating how data science and
analytics can facilitate collaboration both internally and
externally. For example, businesses are trying to understand how
data science and analytics can help engage customers and improve
operation efficiency and how it can use social media to support
corporate knowledge management. Another example is collaborative
creation of ideas and solutions through crowdsourcing and online
communities (such asdominodatalab.com). Access to heterogeneous,
voluminous, and unverified data presents both new opportunities
and challenges for addressing collaboration problems. Yet another
example is the collection of data by the public around the world
which is then used by scientists working on Genographic data by
National Geographics.
Topics of interest include, but are not limited to:
· Challenges and opportunities of data science for
collaboration
· Analysis of big data for collaboration
· Collaboration across organizations for social impact
through analytics
· Collection, aggregation, and organization of collaborative
big data
· Managing heterogeneous big data from collaborative sources
· Visualization of collaborative big data
· Data science for collaborative work (decision making,
problem solving, negotiation, and creativity/innovation)
· Data science and analytics for inter-organizational
collaboration
· Crowdsourcing analytics for collaborative tasks
· Security and privacy issues in collaborative data science
· Data science in collaborative creation or innovation
· Human factors in applying data science for collaboration
· Team building in data science for collaboration
· Case studies on data science for collaboration: Adaptive
collaboration systems that feature modeling, collaboration, and
advanced analytics to detect patterns, make sense of, simulate,
predict, learn, take action, and improve performance with use and
scale.
· Knowledge discovery from collaborative data in social
media
· Analysis of collaborative social networks
IMPORTANT DATES
- April 15: Paper submission begins
- June 15: Paper submissions deadline
- August 17: Notification of Acceptance/Rejection
- September 22: Deadline for authors to submit final manuscript
for publication
- October 1: Deadline for at least one author to register
for HICSS-52
Conference Website:
http://hicss.hawaii.edu/
Author
Guidelines:
http://hicss.hawaii.edu/tracks-and-minitracks/authors/
Mini-track Co-Chairs
Lakshmi S. Iyer
Computer Information Systems and Supply Chain Management
Department
Appalachian State University
iyerLS@appstate.edu;
Souren Paul
College of Engineering and Computing
Nova Southeastern University
Souren.paul@gmail.com
Lina Zhou
Information Systems Department
University of Maryland Baltimore County
zhoul@umbc.edu
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