Elastic Tag Mapping and Data Ownership

Here is a screen shot (and accompanying hyperlink) to a demonstration overview of the visualization and interaction experiments produced by Moritz Stefaner for his 2007 master's thesis, Visual tools for the socio–semantic web.*

 

Elastic Tag Map Demo

When you run this demo, click on any one of the data sets in the drop down box in the upper right-hand corner. The screen shot above is from the 'avatar_1' data set but there are fourteen data sets in all.

Notice in the upper left-hand corner that you can choose to view the tag mapping in either 2-D or 1-D.

Run your pointer over any of the mapped words or terms. Chose one ... see what happens when you grab it and move it ... and see how other terms remain connected with the chosen word according to frequency. Really cool!

There are actually two representations of frequency. One representation is according to the size of each connected word or term. The other is in the bar graph in the lower left-hand corner. When you run your point over that bar graph, you will also see additional frequency information according to the chosen word or term. Notice how this bar graph changes with the selection of a chosen term.

What does this have to do with data ownership?

At page 5 of his paper Stefaner says this:

"The digital turn and the explosive growth of possibilities for information access and publishing fundamentally changes our way of interaction with data, information and knowledge. This process is neither finished nor understood, but currently, generally observed phenomena are:

• an acceleration of information diffusion
• an increasing process of chunking information into small, reusable bits (micro–content)
• a shift towards a larger population of people producing and sharing information
• along with an increasing specialization of topics, interests and the according social niches
• leading overall to a massive growth of space for action, expression and attention available to every single individual" [emphasis added]
  

Imagine using what Stefaner demonstrates in his demo to easily ...

• visualize, and therefore govern, your own personal health records in whole or in granular parts (like immunizations or special health conditions),
  
• conduct an audit as to whether those physicians, hospitals, insurance companies, and other healthcare providers whom you have permitted to view all or granular parts of your personal health records are the only ones who have accessed those records, and
• determine the frequencies, dates and times of their accesses.

It is critical that with technological data ownership we also be provided with data visualization tools that make it easy to directly determine to our satisfaction whether, when and how frequently our data is being used and accessed according to our permissions. See and compare Personal Health Records, Data Portability and the Continuing Privacy Paradigm.

 
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*If you think you might be interested in downloading Stefaner's master's thesis, please find it in the references to this blog entry.

Visual tools for the socio–semantic web
Author - Moritz Stefaner
Master’s Thesis
Published: June, 2007
Pages: 112 
PDF file size: 13.79 MB
Interface Design Programme
University of Applied Sciences, Potsdam
Supervisors: Prof. Boris Müller and Prof. Danijela Djokic.

Table of Contents:

1. OVERVIEW
2. ANALYSIS: THE EMERGING SOCIO–SEMANTIC WEB
2.1. THE RENAISSANCE OF THE SOCIAL WEB

• THE WEB AS A PLATFORM, SITES AS APPLICATIONS
• RICH INTERACTION, CASUALTY AND USER EXPERIENCE
• THE READ–WRITE WEB
• THE ANATOMY OF THE PARTICIPATORY WEB
• USER GENERATED CONTENT — OR METADATA?

2.2. THE LONG TAIL

• THE STATISTICAL DISTRIBUTION
• THE LONG TAIL OF WEB ECONOMICS

2.3. MICROCONTENT

• CHUNKS, SNIPPETS, MICROCONTENT
• PUBLISHING IMPLICIT INFORMATION

2.4. WEB FEEDS

• GO GET VS. COME TO ME
• WHAT ARE WEB FEEDS?
• USAGE PRACTICES
• PERSPECTIVES

2.5. TAGGING AND FOLKSONOMIES

• TAGGING SYSTEM DESIGN FEATURES
• A COGNITIVE PERSPECTIVE ON TAGGING
• WHY TAGGING WORKS
• HOW ARE TAGS USED?

 2.6. A NEW VIEW ON METADATA

• THE SEMANTIC WEB
• PACE LAYERING
• CONCLUSION

3. GUIDELINES AND MAXIMES
4. SYNTHESIS: EXPERIMENTS, VISUAL ANALYTICS AND APPLICATION DESIGN.
4.1. EXPERIMENTS AND VISUAL ANALYTICS

• UNDERSTANDING TAGGING STRUCTURES
• TEMPORAL DYNAMICS OF TAGGING AND CONTENTS
• LIFESTREAMS AND MASH–UPS
• INTERSUBJECTIVITY AND COMMUNITY AGREEMENT

4.2. MULTI–FACETED FOCUS & CONTEXT FOR LONG–TAIL NAVIGATION

• ELASTIC LISTS FOR FACET BROWSERS
• FACET BROWSING FOR TAGGING STRUCTURES

4.3. KONDUIT—A MODEL FOR A WEB FEED HUB APPLICATION

• BACKGROUND
• KONDUIT — A CONCEPTUAL MODEL FOR A WEB FEED HUB APPLICATION
• APPLICATION DESIGN

4.4. OUTLOOK
5. APPENDIX
5.1. ADDITIONAL MATERIAL
5.2. REFERENCES
5.3. EIDESSTATTLICHE ERKLÄRUNG