1. campaign viewer o psaap goals: o allow researchers to quickly explore campaign space o compliment and integrate into portal o visualization goals: o enable efficient visualization of multiple dimensions o offer analysis space as an interactive experience o allow to record and share discovery process o allow environment to be accessible via a browser

2. STRATEGIES FOR multivariate visualization LIMITATIONS o limited visual space o limited visual comprehension DESIGN CONSIDERATIONS o complex data better understood  layered representation o  reading order (levels of importance) o i.e. emphasizing/de-emphasizing different components of representation o  data discretized/binned o offer >6 visual dimensions o create interactive/intuitive space

3. interactivity considerations o allow direct interaction o make objects (variables/dimensions) draggable o make rendered datapoints interactive o allow efficient interaction o easy navigation of variable-dimension coupling o via keystrokes o via clicking o via dragging

4. browser considerations o use new standards/technologies o html5 o drag/drop o canvas (vs embedded svg) o use standard client/server communication api o XMLHttpRequest o xml formatted data collections o xml formatted data o xml formatted scripts

5. canvas vs svg o canvas o + html element with direct js access o + currently faster/more stable (under chosen browsers) o - generates pixel represention o - interativity needs to be added separately o svg o - requires connections to html document (and back) o + generates vector based representation o + interactivity can be encoded in visual elements o + xml format

6. scripting considerations o allow to record interactive session o allow edit scripts o allow to save/share/publish o scripts can be stepped through o with same or different data o keep data/environment interactive along replay o make scripting/replay seamless

7. portal integration o enable collaborative data analysis o plots+scripting as part of sharable information o leading into web based collaborative dashboard o dashboard= information summary  suggested actions o sample, see IBM’s Dashiki/ManyEyes wikified o data grouping o definition of batch-jobs/campaigns (custom subsets) o allows for targeted/better defined data analysis

8. application backend o goal : allow for easy access to data, optimized for grouping o object-oriented approach to group creation o strong representation of each simulation o Django implementation o Python based classes o  creation of classes to represent simulations, targets, projectiles,.. o + flexibility allows for modified/new model paradigms o enables manipulation from pre-existing python code and libraries o still in development stage

9. application backend: django o implementation using jinja2 template language o increased freedom in template design o + extends django templates with custom method calls o.i.e. passing of arbitrary arguments to functions o developed with the django-command-extensions o easy database-insensitive backup o  exports the database using django’s api o i.e. programmatically adds entries to the database using python o + other added features o e.g. automated way to graphically represent database backend

10. current development o developed using new html5 standards o developed in Safari o some testing on portable device (iPad) o some testing on Mozilla based browsers o implemented basic interactivity o drag/drop o keystroke navigation o script recording/playback o dynamic loading