Data Science and Analytics for Collaboration Minitrack
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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
Lakshmi Iyer (Primary Chair)
University of North Carolina Greensboro
Nova Southeastern University
University of Maryland Baltimore County
Tel: (410) 455-8628
ItemSocial Collaboration Analytics for Enterprise Collaboration Systems: Providing Business Intelligence on Collaboration Activities( 2017-01-04)The success of public Social Media has led to the emergence of Enterprise Social Software (ESS), a new type of collaboration software for organizations that incorporates “social features”. Surveys show that many companies are trying to implement ESS but that adoption is slower than expected. We believe that in order to understand the issues with its implementation we need to first examine and understand the “social” interactions that are taking place in this new kind of collaboration software. We propose Social Collaboration Analytics (SCA), a specialized form of examination of log files and content data, to gain a better understanding of the actual usage of ESS. Our research was guided by the CRISP-DM approach. We first analyzed the data available in a leading ESS. Together with leading user companies of this ESS, we then developed a framework for Social Collaboration Analysis, which we present in this paper.
ItemCollaborative Software Performance Engineering for Enterprise Applications( 2017-01-04)In the domain of enterprise applications, organizations usually implement third-party standard software components in order to save costs. Hence, application performance monitoring activities constantly produce log entries that are comparable to a certain extent, holding the potential for valuable collaboration across organizational borders. Taking advantage of this fact, we propose a collaborative knowledge base, aimed to support decisions of performance engineering activities, carried out during early design phases of planned enterprise applications. To verify our assumption of cross-organizational comparability, machine learning algorithms were trained on monitoring logs of 18,927 standard application instances productively running at different organizations around the globe. Using random forests, we were able to predict the mean response time for selected standard business transactions with a mean relative error of 23.19 percent. Hence, the approach combines benefits of existing measurement-based and model-based performance prediction techniques, leading to competitive advantages, enabled by inter-organizational collaboration.
ItemCollaboration for Success in Crowdsourced Innovation Projects: Knowledge Creation, Team Diversity, and Tacit Coordination( 2017-01-04)When innovation projects are crowdsourced, individuals are allowed to form teams and collaborate to develop a successful solution. In this environment, teams will be competing with each other, as only the winning ones take the award home. Should a worker work alone, so that she or he does not need to share the award when she or he wins, or should she or he form a team for a better chance to win? In this paper, we studied the behaviors of workers in the context of crowdsourced innovation projects (CIPs). Building upon the theoretical framework of the organizational knowledge creation theory (OKCT), we linked team performance to team formation factors, including team diversity, team coordination, and task complexity. Our preliminary analysis showed that team coordination was an important factor for success. Team diversity in terms of connectivity was a positive factor towards better performance, whereas other factors were not significant. Our study indicates that workers in CIPs are likely to benefit from collaborations, connectivity diversity, and role diversity.