Design considerations for collaborative visual analytics.

Note

This paper discussed the factor to consist a collaborative visual analytics environment. Some of the theory is overlapping with the online community operation. A successful collaboration is an effective division of labor among participants, the author argue three factors here: modularity, granularity, and cost of integration. In other words, the tasks should split, conduct and integrate at a reasonable price. If each of the factors is too expensive, it may hard to be a success collaboration scenario. For modularity factor, the author provides an information visualization reference model; this model helps for decomposing the visualization process into data acquisition and representation visual encoding, display, and interaction. Each of the components can be a reasonable module to start the collaborative works. For granularity factor, the author discussed the sensemaking model, for instance, in cooperative scenarios, the collaborator can immediate benefit from the actions of others. It is hard to facilitate cooperation if a lack of the incentive.

The ground sense principle is listing below:

• discussion models, awareness 
• Reference & deixis, pointing
• Incentives & engagement, personal relevance, social-psychological incentives, gameplay, 
• Identity & trust & reputation, identity presentation 
• Group dynamics,  management, size, diversity 
• Consensus and decision making, information distribution & presentation

A good reference to consider the collaborative theory in different scenarios, e.g. business intelligence system. For social analysis, a extend reading at [2].

Reference

1. Heer, Jeffrey, and Maneesh Agrawala. "Design considerations for collaborative visual analytics." Information visualization 7.1 (2008): 49-62.
2. Wattenberg, Martin, and Jesse Kriss. "Designing for social data analysis." IEEE transactions on visualization and computer graphics 12.4 (2006): 549-557.

Collaborative visual analysis with RCloud

Note

This paper discussed a collaborative visual analysis environment for a team work. For a data science related project work, it is very common to design, analyze and deliver the result to target audience, could be a colleague, customer or your boss. This is a process of exploratory data analysis (EDA). This paper argues the works are usually done by different tools, i.e. coding in scripting language and design the interface with web techniques. This makes the collaborative work very difficult, due to lack of discoverability (code reuse), technology transfer (collaborate) and coexistence (plus interactive visualization tool). Hence, this paper proposed a framework - RCloud, which using R to integrate the back end analyze and front display in a restful API structure. The basic idea is every application natively demonstrates the result to users through web browsers. This framework is re-using and coupling the existing package in R.

Points: in a small scale teamwork size and low dynamic of project requirements, I think this framework would work well. However, if more and more projects (usually small and not mature result) go live, the search and re-use may create extra workload for the developer. In another hand, the R package may not be suitable to solve all the practical problems, e.g. a large scale data storage or distributed computing tasks. Besides, there are more framework options to better facilitate the collaborative between developer and designer, e.g. the MVC framework. I think a good framework should stand alone with the specific language and techniques, so it can generally support to dynamic real world requirement.

I actually like this idea, it shows the values to deliver the beta works to the users. It 'd be good if we can put the research finding or preliminary result on the web for a better potential collaborative, public exposure, and self-advertisement. The other trend is using Scala to bundle the analysis, implementation, and production.

Reference

1. North, Stephen, et al. "Collaborative visual analysis with RCloud." Visual Analytics Science and Technology (VAST), 2015 IEEE Conference on. IEEE, 2015.