Within the endeavor of research visual analytics, there has been much work to solve problems that require the close and interactive relationship between human and machines. With the standard practice for research at the initial basis, each paper usually creates a new standard to improve on the fundamental level of the area or improve upon a previously created standard. To this extent there have been many various projects that excel in their certain areas of expertise, however this paper is endeavors to create a framework to enable relativity (or comparability)between the various features of the projects to further the research. Within some of previous frameworks provided, they each created models based on the best of their abilities, including features such as maturity of the crowd sourcing platform, the model presentation, or the integration types. Whilst these are acknowledged as furthering the field, they are limited to their subsections and “cornering”their framework in relative to the framework presented in this paper. While the idea of the relationship between humans and computers were initially described and conversed from the early 1950’s, it was stabilized in the late 1970’s from J.J. Gibson in which “an organism and its environment complement each other”. These affordances are described are used as some of the core concepts between humans and machines,since through visual analytics the relationship (between human and machine) are at its core. Going through the multitude of papers underwent through this survey, include some of the following human affordances (human “features” required by machines); Visual perception, Visuo spatial thinking, creativity, and domain knowledge. Listed within the machine affordances (machine attributes used or further “exploited” for research purposes) includes some of the following; Large-Scale Data manipulation, efficient data movement, and bias-free analysis. Through these features, there can also be hybrid relationships where both the human and machine features.
In comparison to the other reading for the week, I do agree and like the framework created to relate crowd-sourcing tools and humans. Not only is it of more human aspects (suggesting a better future relationship), it also describes the co-dependency (currently) with a relatively bigger emphasis on human centric interactions.
I do also agree that this framework seems to be a good representation of the standard applications of visual analytics. While acknowledging the merging of both human and machine affordances, the human affordances seem to be enough for the framework. The machine affordances seem enough, though this may be due to the direction of the research in the area.
- Like the other reading of the week, I would like to see a user study (of researchers or people in the industry in the area)and see how this comparison lines up to practical usage.
- In the future, would there be a more granular weighting of which affordances are used by which platform? This is more practical of an application though it may help serve as a better direction in which companies (or researchers)could choose their platform to best fit their target audience.
- Comparing the affordances (or qualities) of a project may not be as fair to each respective (at a high level)to potential consumers. Though potentially being game-able (increasing the numbers through such malicious means) and exaggerated, impact score and depth could help compare each project.
