-------- Forwarded Message -------- Subject: [AISWorld] Call For Papers: HICSS 52 (2019)-Mini-track on Data Science and Digital Collaborations Date: Wed, 2 May 2018 16:22:31 +0000 (UTC) From: Souren Paul sousoup@yahoo.com To: aisworld@lists.aisnet.org aisworld@lists.aisnet.org
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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