Rough notes on BIG DATA and Tech Comm - Session A.3

13 March 2013

BIG DATA & Tech Comm

Brenton Faber Our field needs to learn database structures; lots of imperatives; quat literacies

BIG data: data too big to store for current tech Our tech ability to gather, but analytics are not up to speed; gives ex of bioinformatics and comp biology; seems to be creating a crisis, is this really happening?

data analysis
Visualization
Data integration – very little ability to network networked; why would they integrate? I don’t think its tech-related, but ideology-based issue.

He suggests to: - create relational meaning - his metric: connecting forms and events - How do we get meaning out of data? How to data mine?

Gives example of rural hospital in MYSQL, goes through basic identification of types of info in such databases

Helping to create metrics to help understand data and present information to other people. This seems a lot like Sociology. How is it different in scope? A focus on the rhetorical messages? Not getting a clear sense from the presentation though on how to work with that mediation process, nor the issues with the multiple perspectives, cultural diferences, etc.

Data selection, reduction, and methods.

Billions and Billions Served

Geofrey Sauers – Iowa State University

Pulling for a better balance between Quant/Qual methods

Interested in pubs and circulation of acad pubs

“Lots of data to be had” eserver.org 1.5 billion hits to analyze, but how? We need to find the problems and the questions to address.

Brings up Apple’s latest update that requires a type of png file needed for the retina display. Here I’m thinking about traking how often Apple requires updates and what kind of updates: a case of upgrade, consumer-driven culture
Reduce bounce rates
Brings up devices and tech that look at content; talking backwards compatibility

Very critical about Google Analytics and use of it in classes and university settings, but this feeds into their revenue model. Transitions into ethical questions about what kind of Big Data work is done and how. He suggests Apache Hadoop (sp?)

Sauer shows quant vizualization of hits that shows a very basic graph that indicates differences, and seems to suggest that this

Information Visualizations

Jennifer deWinter

Looking at complex data from gaming industry

Starts with Data Literacy – “an important way to construct representations of what it means to be human”

Lists basic snapshots, pie charts, etc. as usual thought

Stats are 1) discrete, 2) reports results that are easy to find and central to rhetorical purpose, and 3) diff representaitonal models require different levels of data literacy

However, some Critical Responses: cites Tufte Dragga & Voss’ “Cruel Pies”; data presented “mathmatically” dehumanizes and decontextualizes information from cultural context; Sara Soblin “Take a disciplined approach to visual stroytelling.. Graphics are meant to be fast…”

Shows some examples to indicate how this may intersect with Big Data: “History of Cars in the US” etc., but the infographics are more cartoonish and not seeing how they’re contextualizing info in the best way. Instead, I’m thinking about William Bunge’s Theoretical Geography (1962) as a much better example, where geagraphies and maps indicate location

Mentions cognitive studies that indicate people with incredible memories, place their thoughts into “spaces” (Ha!).

Speaks of “Semiotic Collapse” where many representations are created: spatial, process, visual, textual.

http://www.rhetoric-culture.com/visual for student examples.