[AISWorld] Call For Papers: HICSS-Mini-track on Data Science and Analytics for Collaboration

[Souren Paul]

*Mini-track on Data Science and Analytics for Collaboration*

*Collaboration Systems and Technologies Track *


*Hawaii International Conference on System Sciences HICSS-50: January 4-7,
2017 | Hilton Waikoloa Village*

Deadline for submissions: June 15, 2016
Website: www.hicss.hawaii.edu

With explosive growth in unstructured and structured data, organizations
are looking for ways to innovate through the use of data science and
analytics. Data science and analytics for collaboration is the study of
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 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, 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 and analytics can facilitate collaboration
both internally and externally.  For example, businesses are not just
looking at how data science and analytics can help acquire, grow and retain
customers but also at how it can use social media to support business
operations and how it can leverage the power of online word-of-mouth to
promote customers’ engagement with brands.  One example is collaborative
generation and creation of ideas and solutions through crowdsourcing and
online communities. Emerging heterogeneous, voluminous, and unverified data
present both opportunities and new challenges for addressing collaboration
problems. Another example is the collection of data by public around the
world which is then used by scientists working on Genographic data by
National Geographic.

Topics of interest include, but not limited to:

·         Challenges and opportunities of data science for collaboration

·         Analysis of big data for collaboration

·         Collection, aggregation, and organization of collaborative big
data

·         Managing heterogeneous big data from collaborative sources

·         Visualization and presentation of collaborative big data

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

·         Data science and analytics for internal collaboration in groups
and organizations

·         Data science and analytics for inter-organizational collaboration

·         Crowdsourcing for collaborative tasks

·         Security and privacy issues in collaborative data science

·         Data science in collaborative creation or innovation

·         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 discovery from collaborative data in social media

·         Analysis of collaborative social networks



*Mini-track Co-Chairs*

Lakshmi S. Iyer
Information Systems and Supply Chain Management (ISSCM) Department
The University of North Carolina Greensboro
Lsiyer at uncg.edu



Souren Paul
College of Engineering and Computing
Nova Southeastern University
Fort Lauderdale, Florida
Souren.paul at gmail.com



Lina Zhou
Information Systems Department
University of Maryland Baltimore County
zhoul at umbc.edu



*Submission Process:*

Full paper submissions must be made electronically through the HICSS
on-line submission system at https://precisionconference.com/~hicss by June
15, 2016.  Papers should not exceed ten pages and the initial submission
will not have author names. Please check the above web site or contact the
mini-track co-chairs for more information.



*Key Dates:*

June 15, 2016 (11:59pm Hawaii Time): Full papers uploaded to the
appropriate minitrack.

August 16, 2016: Notification of accepted papers mailed to authors.

September 15, 2016: Final Paper Due*.  *At least one author of each paper
should register by this date.  This is the Early Registration fee deadline.