Metadata and Quantum4D

You mention that in addition to metadata built into the system, new elements were ‘remembered’ based on the way the users searched.  You called this folksonomy. In my work, I’ve used a description of folksonomy as a  user-generated taxonomy but have viewed it as something the user added themselves, as with tagging of photos uploaded to Flickr or tags added to bookmarks added to Delicious.com. Is folksonomy the term also used for tags the system generates based on user activity?  I am not familiar with that use.

ANSWER

We associate data with the source on import.  After that data remembers where it is used through an entity reuse paradigm:  if you drag and drop and object into a new space to do analysis – it then lists that space (remembers) among those the object is used in.   In our sense the ‘tag’ is a pleasant unintentional by product of user activities.  Here is some more flavor on what we mean by tagging from some of our developing documentation:

Tagging is a broadly defined concept which includes several different use cases.  Quantum4D deals with tagging in a nuanced way according to the use case.   Automated or Source Specific tagging is handled via the maintenance of the connection to the external source.   User generated or on demand tagging as experienced now in standard page based work environments is refined in Quantum4D to support three distinct ways of associating information, association and identity with an entity.  

First, a user can add notes to an object, group or space.  This supports the use case where a user wishes to elucidate details or information associated with that object, group or space.   From a programmatic perspective this is essentially the same functionality described above as notes in the properties panel for entities in the system.  These currently appear on mouse over in the 3D canvas and tree.   From an new end user perspective, this specific interpretation matches what they now generally think of as tagging in page based work environment.

Secondly, the association of entities with each other can take the form of a strong association or a weak association.   Strong associations are handled with the merge function where a user is essentially saying the two entities derived perhaps from different sources are one and the same.   Weak associations are handled with the grouping function where a user may create a space where associated entities are clustered together in a group which defines the nature of their relatedness.   Strong associations are less frequent and nuanced than weak associations.   Our approach supports multiple weak associations since entities can appear in multiple groups.   Further detail on the nature of relations between entities can be further elucidated, mapped and discovered using relation entities to map and define analytic, identity or any other type of association between any two given entities.  

Finally, identity tagging is a ‘free’ byproduct of the use of the system – it should not be intrusive to the analytic work activity.   If, for example, a user has data on provinces in Mali, the associated data should be placed in sub-spaces of an entity corresponding to Mali.  This way a user will avoid a ‘traffic jam’ of information on the top level (all data on Mali on the Mali object) and the organization of data becomes a means of ‘meta’ tagging information via the simple and intuitive placement of information in the appropriate location in a broader information ecology.   This third transparent sense and use of tagging is quite powerful and unprecedented in page based interfaces.  It allows the user to articulate – as a free by product of the analytic process –tagging ecologies – where all data is positioned properly relative to other data in the analyst domain of concern.