[AISWorld] Final CFP HICSS49 Mini-track on Data Science for Collaboration

Souren Paul souren.paul at gmail.com
Thu Jun 11 22:50:02 EDT 2015


Call for papers



January 5-8, 2016
Grand Hyatt, Kauai
http://www.hicss.hawaii.edu



HICSS2016 Minitrack: Data Science for Collaboration
(Collaboration Systems and Technologies Track)

Data science for collaboration is the study of the generalizable extraction
of knowledge from data to support human collaboration within and across
groups and organizations. The new knowledge gained is expected to be
actionable for achieving collaborative goals such as generating, choosing,
negotiating, and executing. Data science 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, engineer effective
solutions to the collaboration problems, and ways to effectively
communicate findings across roles ranging from business managers to data
analysts. There is exploding 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 investigating how data science can
facilitate collaboration both internally and externally. We welcome
technical, empirical, and behavioral research that cover various aspects of
data science for collaboration. Topics of interest include, but not limited
to:

·         Challenges and opportunities of data science for collaboration

·         Collection, aggregation, and organization collaborative Big Data

·         Managing heterogeneity of collaborative big data

·         Visualization and presentation of collaborative big data

·         Data science for collaborative work (decision making, problem
solving, negotiation, and creativity/innovation)

·         Data science for internal collaboration in groups and
organizations

·         Data science for inter-organizational collaboration

·         Crowdsourcing for collaborative tasks

·         Security and privacy in collaborative Data Science

·         Data science in collaborative creation

·         Case studies on Data science for Collaboration: Adaptive
collaboration systems that feature modeling, collaboration, and advanced
analytics to detect patterns, make sense, simulate, predict, learn, take
action, and improve performance with use and scale.

·         Application of control-theoretic models to interactions among
social entities

·         Application of survival models to predict hazard rate of computer
supported social processes

·         Application of N-person game theory in problems arising from
unregulated use of collaboration systems

·         Knowledge extraction from collaborative data in social media

·         Social network analysis (SNA) of collaboration big data

·         Data science applications in assessing risks in financial systems
and interdependent critical infrastructures

·         Analytics for data-driven operations management

·         Data science application for smart cities

·         Analytics for healthcare delivery



IMPORTANT DATES

June 15, 2015                                     Submission full
manuscripts
August 16, 2015                                Acceptance Notifications
September 15, 2015                        Submission camera-ready paper
October 1, 2015                                Early Registration fee
deadline

 For formatting and submission instructions, see HICSS website.
http://www.hicss.org/#!author-instructions/c1dsb



Minitrack Co-Chairs:

Souren Paul - Souren.paul at gmail.com, Nova Southeastern University
Lakshmi Iyer - Lsiyer at uncg.edu, The University of North Carolina at
Greensboro
Lina Zhou - zhoul at umbc.edu, University of Maryland Baltimore County



More information about the AISWorld mailing list