So, here they are. They all refer to situations where there is a set of resources which can be filtered according to different criteria.

Zoom–in

The most common pattern: Given a set of resources, select a subset by  adding an additional (AND-connected) filter criterion.

All photos -> zoom in (Europe) -> photos taken in Europe
Photos taken in Europe -> zoom-in (Cities) -> photos taken in Europe AND taken in cities

Zoom–out

The complementary pattern: Given a filtered view, select a superset by either

• Removing a (AND-connected) filter Photos taken in Europe AND taken in cities -> zoom out (cities) -> photos taken in Europe
• or: Adding a OR-connected filter Photos taken in Europe -> zoom–out-OR (Africa) -> photos taken in Europe OR Africa
• or: Replacing a filter with a more general version Photos taken in Germany -> zoom–out-UP (Germany) -> photos taken in Europe

In either way, you end up with a more general query, that yields at least all of the original results, and usually more.

Shift

Zoom-in and zoom-out can be combined into a shift navigation mode. Here, one part of the filters is replaced by a concept that is neither more general nor more specific than the original one.

Photos taken in Germany -> shift (France) -> photos taken in France

Pivot

Another combination of zoom-in and zoom-out is the pivot navigation. It is very common in web applications like e.g. delicious.com. Given a filter setting and its results, you can jump to a fresh query consisting only of one of the occurring metadata terms.

Photos taken in Germany -> pivot (Moritz) -> photos taken by Moritz

This is often accomplished by adding clickable links to the results’ metadata items.

Querying by examples

This corresponds to a pivot on a number of resources and metadata fields at the same time. The most specific concepts that apply to a whole item selection are collected and used in a new query.

Clinton, Bush, Obama -> query by examples -> American presidents

Obviously, the generalization capabilities here depend a lot on the metadata structure. In some database, the example above might generalize to “persons” or “males”, in others to “Male american presidents after 1980″.

Related set

Demonstrated in parallax and humboldt, this navigation mode follows the same metadata link on the whole result set to construct a new one.

Photos taken in Europe -> related set (photographer) -> Photographers of photos taken in Europe

I realize this is quite dry material and could use some illustrations, examples, references. Nevertheless, I hope it spawns some new thoughts in those of you thinking about search and browsing in web applications!

David Huynh has recently joined the freebase team, after having worked on Exhibit and other SIMILE tools at MIT. His new project Parallax is obviously based on Exhibit (which followed mostly a faceted filtering paradigm) but demonstrates a really interesting “sidewards browsing technique” for navigating related sets of different types of entities.

As an example, you could start with a set of architects, then filter down to all modern architects, plot them on a map, a timeline etc. – quite nice already, but traditional facet browsing in principle. The catch however, is that you can explore related collections, like the buildings they designed, their birth places etc. in the same manner. Very interesting principle and nicely executed, yet a bit hard to explain.

In this screencast, David explains it himself:
Freebase Parallax: A new way to browse and explore data from David Huynh on Vimeo.

As a side remark: academically, I think the Humboldt paper by Georgi Kobilarov first presented this principle (but they also refer to an earlier prototype of David’s work). Unfortunately it was introduced under the name of pivot browsing, which is sort of reserved already for the quite related, but not identical principle introduced in dogear.

Any ideas for a good name? Sidewards browsing? Entity shift? Or just stick with parallax?