Data Science and Analytics for Collaboration Minitrack

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 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
  • Analysis of collaborative social networks

Minitrack Co-Chairs:

Lakshmi Iyer (Primary Chair)
University of North Carolina Greensboro

Souren Paul
Nova Southeastern University

Lina Zhou
University of Maryland Baltimore County
Tel: (410) 455-8628

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